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Mancino, Francesco. "An embedded model predictive controller for optimal truck driving." Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205649.
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An embedded model predictive controller for velocity control of trucks is developed and tested. By using a simple model of a heavy duty vehicle and knowledge about the slope of the road ahead, the fuel consumption while traveling near a set speed is diminished by almost 1% on an example road compared to a rule based speed control system. The problem is formulated as a look-ahead optimization problem were fuel consumption and total trip time have to be minimized. To find the optimal solution dynamic programming is used, and the whole code is designed to run on a Scania gearbox ECU in parallel with all the current software. Simulations were executed in a Simulink environment, and two test rides were performed on the E4 motorway.<br>En algoritm för hastighetsstyrning baserad på modell-prediktiv reglering har utvecklats och testats på befintlig styrsystem i ett Scania lastbil. Genom att använda en enkel modell av fordonet och kunskap om lutningen på vägen framför den kunde man sänka bränsleförbrukningen med nästan 1% i vissa sträckor, jämfört med en regelbaserad farthållare. Problemet är formulerat som en optimerings-problem där bränsleförbrukning och total restid måste minimeras. För att hitta den optimala lösningen användes dynamisk programmering och hela koden är skriven så att den kan exekveras på en Scania styrenehet. Koden är kan köras parallellt med den mjukvara som är installerad på styrenheten. Simuleringar utfördes i en miljö utvecklad i Simulink. Två test-körningar på E4 motorvägen utfördes.
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Juhlin-Henricson, Teddy. "Implementation and Analysis of a Clothoid-based Model Predictive Controller." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187688.
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For the last couple of years autonomous driving has increased in popularity as a research area, and it continues to grow. A topic within autonomous driving is path following, which is the subject studied in this project. One of the popular controllers to use for controlling a vehicle is the model predictive controller, because it finds an optimal control input for the vehicle based on the model of the vehicle, and its estimated future behaviour within the prediction horizon - which covers a distance ahead of the vehicle. To increase the length of this distance, one can use a new controller - the clothoid-based model predictive controller. The clothoid-based model predictive controller is a linear time-varying model predictive controller that uses a clothoid-based vehicle model to find an optimal input based on the vehicle’s behaviour at the kink-points. The kink-points are way-points that are used to create the clothoids, and the distance between them can be very far. Therefore, it is possible to cover a large distance ahead of the vehicle with a small prediction horizon. In this thesis, the controller is implemented at the Smart Mobility Laboratory at KTH Royal Institute of Technology so that it can be tested and evaluated for future use. The controller is implemented on a 1 : 32 scaled radio truck that is monitored by a motion capture system, and remotely controlled by a desktop computer. The outcome of the implementation is a new controller for the remote controlled radio trucks with a fast control algorithm, where the greatest mean deviation from the path was 0.117m.<br>Under de senaste åren har självkörande fordon blivit populärare som forskningsområde, och det blir allt mer populärt. Ett område inom självkörande fordon är att den ska följa efter en bana, även kallat path following, vilket är området som projektet fokuserat på. En av de populära kontrollerna för att styra fordonet är predikterande modell-kontroller (model predictive control), för den hittar en den optimala kontrol signalen baserat på modellen av fordonet och dess framtida bettende inom prediktions horisonten - som täcker ett område framför bilen. För att öka täckningsgraden av det här området kan en använda en ny kontroller - den klotoidbaserade predikterande modell-kontroller (clothoid-based model predictive controller). Den klotoidbaserade predikterande modell-kontroller är en linjärt tidsvarierande predikterande modell-kontroller (linear time-varying model predictive controller) som använder sig av en klotoidbaserad fordonsmodel för att hitta den optimala inputsignalen baserat på fordonets beteende vid knut-punkterna (kink-points). Knut-punkterna är punkter som används för att skapa klotoiderna, och avståndet mellan punkterna kan vara långt. Därför är det möjligt att täcka ett större område framför fordonet med en mindre prediktions horisont. I den här uppsatsen är kontroller implementerad i Smart Mobility Laboratory på Kungliga Tekniska Högskolan, så att den kan bli evaluerad och testad för användning i framtiden. Kontrollern används på˚ en 1 : 32 skalenlig radiostyd lastbild som är övervakad av ett rörelse detektionssystem, och lastbilen är radio fjärrstyrd via en dator. Resultatet av implementeringen är en ny kontroller för radiostyrd lastbil med en snabb kontroller algorithm med en maximal medelavvikelse från banan på 0.117m.
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Xu, Shuqi. "Learning Model Predictive Control for Autonomous Racing : Improvements and Model Variation in Model Based Controller." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-247881.
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In this work, an improved Learning Model Predictive Control (LMPC)architecture for autonomous racing is presented. The controller is referencefree and is able to improve lap time by learning from history data of previouslaps. A terminal cost and a sampled safe set are learned from history data toguarantee recursive feasibility and non-decreasing performance at each lap.Improvements have been proposed to implement LMPC on autonomousracing in a more efficient and reliable way. Improvements have been doneon three aspects. Firstly, system identification has been improved to be runin a more efficient way by collecting feature data in subspace, so that thesize of feature data set is reduced and time needed to run sorting algorithmcan be reduced. Secondly, different strategies have been proposed toimprove model accuracy, such as least mean square with/without lifting andGaussian process regression. Thirdly, for reducing algorithm complexity,methods combining different model construction strategies were proposed.Also, running controller in a multi-rate way has also been proposed toreduced algorithm complexity when increment of controller frequency isnecessary. Besides, the performance of different system identificationstrategies have been compared, which include strategy from newton’s law,strategy from classical system identification and strategy from machinelearning. Factors that can possibly influence converged result of LMPCwere also investigated, such as prediction horizon, controller frequency.Experiment results on a 1:10 scaled RC car illustrates the effectiveness ofproposed improvements and the difference of different system identificationstrategies.<br>I detta arbete, presenteras en förbättrad inlärning baserad modell prediktivkontroll (LMPC) för autonom racing, styralgoritm är referens fritt och har visatsig att kunna förbättra varvtid genom att lära sig ifrån historiska data från tidigarevarv. En terminal kostnad och en samplad säker mängd är lärde ifrån historiskdata för att garantera rekursiv genomförbarhet och icke-avtagande prestanda vidvarje varv.förbättringar har presenterats för implementering av LMPC på autonom racingpå ett mer effektivt och pålitligt sätt. Förbättringar har gjorts på tre aspekter.Först, för system identifiering, föreslår vi att samlar feature data i delrummet,så att storlek på samlade datamängd reduceras och tiden som krävs för attköra sorteringsalgoritm minskas. För det andra, föreslår vi olika strategierför förbättrade modellnoggrannheten, såsom LMS med/utan lyft och Gaussianprocess regression. För det tredje, För att reducerar komplexitet för algoritm,metoder som kombinerar olika modellbygg strategier föreslogs. Att körastyrenhet på ett multi-rate sätt har också föreslagits till för att reduceraalgoritmkomplexitet då inkrementet av styrfrekvensen är nödvändigt.Prestanda av olika systemidentifiering har jämförts, bland annat, Newtonslag, klassisk systemidentifierings metoder och strategier från maskininlärning.Faktorer som eventuellt kan påverka konvergens av LMPC resultat har ocksåundersökts. Såsom, prediktions horisont, styrfrekvensen.Experimentresultat på en 1:10 skalad RC-bilen visar effektiviteten hos föreslagnaförbättringarna och skillnaderna i olika systemidentifierings strategier.
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Mann, Gustav, and Jakob Luedtke. "Implementation of a Model Predictive Controller in a Spark-Ignition Engine." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176534.
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The propulsion of the spark-ignition engine has been investigated and developed during the past century to improve driveability, minimize fuel consumption and emissions, resulting in highly engineered and computerized powertrains. Well balanced engine maps containing coordinated set-points and model-based information sharing have solved the cross-coupling between different control loops. During transitions between the operating conditions a disadvantageous transient behavior that affects the engine performance may occur. By implementing an MPC as a superior controller a nearly optimal control solution was accomplished. A digital twin of the SI engine was designed through collected measurements and system modeling. The twin made it possible to investigate and elaborate different cost functions in a simulation environment before applying the controller in real-time. By utilizing MPC together with the engine maps a strong relationship between the throttle and iVVT actuator was achieved, which removed the cross-coupling between the actuator control loops and reduced the unfavorable transient behavior.
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Ranjbar, Gigasari Roza. "Model Predictive Controller for large-scale systems - Application to water networks." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Lille Douai, 2024. http://www.theses.fr/2024MTLD0002.
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Cette thèse aborde le défi de l’optimisation de la gestion des ressources en eau au sein des canaux. Il s’agit d’une tâche particulièrement complexe en raison de leur échelle étendue et de la nature diverse de leurs composants, mais également de leurs dynamiques complexes caractérisées par des retards importants et parfois des pentes nulles. En ce qui concerne les réseaux de voies navigables, l’objectif principal est de mettre en œuvre des techniques issues de la théorie du Contrôle afin d’assurer la navigabilité du réseau, garantissant le respect des niveaux d’eau pour la navigation. Plus précisément, les niveaux d’eau doivent demeurer dans une plage prédéfinie autour d’un point de consigne. D’autres objectifs visent en la réduction des coûts opérationnels et l’amélioration de la durabilité des équipements. A cet égard, une alternative dans la gestion de tels réseaux est de remplacer les capteurs le long des canaux par un robot mobile qui effectue les mesures requises en se déplaçant, limitant l’ensemble des tâches de déploiement et de maintenance des capteurs et des systèmes de transmission de l’information. Pour parvenir à une gestion efficiente, il est impératif de garantir un contrôle efficace des structures hydrauliques telles que les vannes, les pompes et les écluses tout en limitant leur utilisation. A cette fin, un algorithme de contrôle est introduit, basé sur un modèle existant dérivé des équations de Saint-Venant. Le modèle simplifié offre une facilité d’intégration des informations actuelles et retardées du système dynamique en simplifiant sa complexité originale. L’utilisation de ce modèle nécessite cependant certaines extensions aux outils standards de contrôle et d’estimation d’état standard. Des méthodes de contrôle prédictif, de type MPC, et des méthodes d’estimation de l’état du système, de type MHE, sont adaptées à ce modèle. Elles permettent de considérer les contraintes physiques et opérationnelles des canaux contrôlés. Le MPC centralisé offre une résilience grâce à son couplage avec la technique MHE. Sa nature déterministe limite cependant sa capacité à aborder systématiquement les incertitudes. Pour relever efficacement ces incertitudes, la mise en œuvre du MPC stochastique (SMPC) a ensuite été proposée. Le SMPC intègre des descriptions probabilistes dans la conception du contrôle, offrant une approche tenant compte des incertitudes. Dans ce contexte d’études, le SMPC est interconnecté avec un robot mobile dont l’usage vise à limiter le nombre de capteurs répartis le long du canal. Par conséquent, une partie de cette thèse se concentre sur la conception du SMPC en intégrant un robot mobile. Cette approche a été appliquée à un canal de test ASCE pour en évaluer l’efficacité. Compte tenu de la nature étendu et de la complexité des interactions des canaux avec leur environnement, une conception d’un jumeau numérique a été entreprise avec pour objectifs de répondre aux besoins d’outils d’analyse avancée de leur gestion. En exploitant les capacités des jumeaux numériques, nous avons cherché à améliorer notre compréhension des dynamiques des canaux, en considérant des scénarios passés, mais également à projeter les avantages de nouvelles stratégies de gestion et de contrôle. Cette évaluation vise à combler le fossé entre la théorie et la mise en œuvre pratique, offrant un moyen tangible de rejouer les événements passés, de tester diverses approches de gestion et, finalement, de proposer aux gestionnaires des outils et des critères conduisant à une gestion efficace des réseaux hydrographiques. Les méthodologies présentées dans cette thèse sont appliquées à un cas réel, un canal situé dans la région des Hauts de France, avec l’objectif de tester et de valider leur efficacité dans un contexte réel<br>This thesis addresses the challenge of optimizing the management of canals, a complex task due to their extensive scale and distinctive attributes, including intricate dynamics, considerable time delays, and minimal bottom slopes. Specifically, the central goal is to ensure the navigability of the network, which involves maintaining safe water levels for vessel travel, through control theory. More precisely, the water levels must remain within a predefined range around a setpoint. Additionally, typical aims encompass reducing operational costs and enhancing the equipment’s life expectancy. In this regard, another objective in the management of such networks is replacing the possible sensors across canals by applying a moving robot to take the required measurements. To accomplish effective management, it becomes imperative to ensure efficient control over hydraulic structures such as gates, pumps, and locks. To this end, a control algorithm is introduced based on an existing model derived from the Saint-Venant equations. The modeling approach simplified the original complex description providing adaptability and facilitating the systematic integration of both current and delayed information. However, the resulting model formulation falls within the category of delayed descriptor systems, necessitating extensions to standard control and state estimation tools. Model predictive control and moving horizon estimation methods can be readily tailored for this formulation, while also adapting physical and operational constraints seamlessly. Given the extensive nature of canals, an evaluation of the digital twin was untaken to address the critical need for advanced tools in the management of such networks. By harnessing the capabilities of digital twins, we aimed to enhance our understanding of canal dynamics, past scenarios, and management strategies. This evaluation sought to bridge the gap between theory and practical implementation, offering a tangible means to playback past events, test diverse management approaches, and ultimately equip decision-makers with robust criteria for informed and effective network management.The methodologies presented above are applied to a practical case study, a canal in the northern region of France. The objective is to validate the efficacy of these approaches in a real-world context.While centralized MPC provides resilience through its receding-horizon approach, its deterministic nature limits its ability to systematically address uncertainties. To effectively tackle these system uncertainties, the implementation of Stochastic MPC (SMPC) has been adopted. SMPC integrates probabilistic descriptions into control design, offering a methodical approach to accommodating uncertainties. In this context, the application of SMPC is interconnected with a mobile robot aimed at replacing existing sensors along the canal to capture measurements. Consequently, a part of this thesis focuses on the design of SMPC in conjunction with a mobile robot. This approach has been applied to an ASCE Test canal to evaluate its effectiveness
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Rogalsky, Dennis Wayne. "Quantifying plant model parameter effects on controller performance /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/9843.
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Campher, Andre Herman. "A systematic approach to model predictive controller constraint handling : rigorous geometric methods." Diss., University of Pretoria, 2011. http://hdl.handle.net/2263/28378.
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The models used by model predictive controllers (MPCs) to predict future outcomes are usually unconstrained forms like impulse or step responses and discrete state space models. Certain MPC algorithms allow constraints to be imposed on the inputs or outputs of a system; but they may be infeasible as they are not checked for consistency via the process model. Consistent constraint handling methods - which account for their interdependence and disambiguate the language used to specify constraints – would therefore be an attractive aid when using any MPC package. A rigorous and systematic approach to constraint management has been developed, building on the work of Vinson (2000), Lima (2007) and Georgakis et al. (2003) in interpreting constraint interactions. The method supports linear steady-state system models, and provides routines to obtain the following information: <ul> <li> effects of constraint changes on the corresponding input and output constraints, </li><li> feasibility checks for constraints, </li><li> specification of constraint-set size and</li><li> optimal fitting of constraints within the desirable input and output space.</li></ul> Mathematical rigour and unambiguous language for identifying constraint types were key design criteria. The outputs of the program provide guidance when handling constraints, as opposed to rules of thumb and experience, and promote understanding of the system and its constraints. The metrics presented are not specific to any commercial MPC and can be implemented in the user interfaces of such MPCs. The method was applied to laboratory-scale test rigs to illustrate the information obtained.<br>Dissertation (MEng)--University of Pretoria, 2011.<br>Chemical Engineering<br>unrestricted
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Bangalore, Narendranath Rao Amith Kaushal. "Online Message Delay Prediction for Model Predictive Control over Controller Area Network." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78626.
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Today's Cyber-Physical Systems (CPS) are typically distributed over several computing nodes communicating by way of shared buses such as Controller Area Network (CAN). Their control performance gets degraded due to variable delays (jitters) incurred by messages on the shared CAN bus due to contention and network overhead. This work presents a novel online delay prediction approach that predicts the message delay at runtime based on real-time traffic information on CAN. It leverages the proposed method to improve control quality, by compensating for the message delay using the Model Predictive Control (MPC) algorithm in designing the controller. By simulating an automotive Cruise Control system and a DC Motor plant in a CAN environment, it goes on to demonstrate that the delay prediction is accurate, and that the MPC design which takes the message delay into consideration, performs considerably better. It also implements the proposed method on an 8-bit 16MHz ATmega328P microcontroller and measures the execution time overhead. The results clearly indicate that the method is computationally feasible for online usage.<br>Master of Science
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Mattsson, Mathias, and Rasmus Mehler. "Optimal Vehicle Speed Control Using a Model Predictive Controller for an Overactuated Vehicle." Thesis, Linköpings universitet, Fordonssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119480.
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To control the speed of an overactuated vehicle there may be many possible ways to use the actuators of the car achieving the same outcome. The actuators in an ordinary car is a combustion engine and a friction brake. In some cases it is trivial how to coordinate actuators for the optimal result, but in many cases it is not. The goal with the thesis is to investigate if it is possible to achieve the same or improved performance with a more sophisticated control structure than today's, using a model predictive controller. A model predictive controller combines the possibility to predict the outcome through an open-loop controller with the stability of a closed loop controller and gives the optimal solution for a finite horizon optimization problem. A simple model of the longitudinal dynamics of a car is developed and used in the model predictive controller framework. This is then used in simulations and in a real car. It is shown that it is possible to replace the current controller structure with a model predictive controller, but there are advantages and disadvantages with the new control structure.
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Thorin, Kristoffer. "Optimal Speed Controller in the Presence of Traffic Lights." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-325352.
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This report presents an approach on how to utilize information on future states of traffic lights to reduce the energy consumption and trip time for a Heavy Duty Vehicle. Model Predictive Control is proposed as a solution to handle the optimisation on-line and the concept is tested for various prediction horizons in which information can be received. Further on, it is investigated if the implemented controller is robust enough to execute the same task in a scenario where only the current state is known and future states are predicted. Comparison with a reference vehicle demonstrates improved fuel economy as well as reduced trip time when the information is given. It is shown that the results are improved as the prediction horizon is extended, but converges after 400-500 meters. As the phases of the traffic lights are predicted, fuel economy can be improved, but it comes at a price from being non-robust with drastic braking and increased trip time as predictions might be inaccurate.
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Leising, Sophie. "Nonlinear controller synthesis for complex chemical and biochemical reaction systems." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-050205-152657/.
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Thesis (M.S.) -- Worcester Polytechnic Institute.<br>Keywords: model predictive control; discrete-time model; continuous-time model; nonlinear systems; Lyapunov design. Includes bibliographical references (p. 99-102).
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Sandrock, Carl. "Implementation and performance analysis of a model-based controller on a batch pulp digester." Diss., Pretoria : [s.n.], 2003. http://upetd.up.ac.za/thesis/available/etd-10152004-113239/.
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Thesis (M. Eng.)(Chemical)--University of Pretoria, 2003.<br>Summaries in Afrikaans and English. Includes bibliographical references (leaves 83-86) and index. Available on the Internet via the World Wide Web.
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Shamsudin, Syariful Syafiq. "The Development of Neural Network Based System Identification and Adaptive Flight Control for an AutonomousHelicopter System." Thesis, University of Canterbury. Mechanical Engineering Department, 2013. http://hdl.handle.net/10092/8803.
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This thesis presents the development of self adaptive flight controller for an unmanned helicopter system under hovering manoeuvre. The neural network (NN) based model predictive control (MPC) approach is utilised in this work. We use this controller due to its ability to handle system constraints and the time varying nature of the helicopter dynamics. The non-linear NN based MPC controller is known to produce slow solution convergence due to high computation demand in the optimisation process. To solve this problem, the automatic flight controller system is designed using the NN based approximate predictive control (NNAPC) approach that relies on extraction of linear models from the non-linear NN model at each time step. The sequence of control input is generated using the prediction from the linearised model and the optimisation routine of MPC subject to the imposed hard constraints. In this project, the optimisation of the MPC objective criterion is implemented using simple and fast computation of the Hildreth's Quadratic Programming (QP) procedure.
The system identification of the helicopter dynamics is typically performed using the time regression network (NNARX) with the input variables. Their time lags are fed into a static feed-forward network such as the multi-layered perceptron (MLP) network. NN based modelling that uses the NNARX structure to represent a dynamical system usually requires a priori knowledge about the model order of the system. Low model order assumption generally leads to deterioration of model prediction accuracy. Furthermore, massive amount of weights in the standard NNARX model can result in an increased NN training time and limit the application of the NNARX model in a real-time application. In this thesis, three types of NN architectures are considered to represent the time regression network: the multi-layered perceptron (MLP), the hybrid multi-layered perceptron (HMLP) and the modified Elman network. The latter two architectures are introduced to improve the training time and the convergence rate of the NN model. The model structures for the proposed architecture are selected using the proposed Lipschitz coefficient and k-cross validation methods to determine the best network configuration that guarantees good generalisation performance for model prediction.
Most NN based modelling techniques attempt to model the time varying dynamics of a helicopter system using the off-line modelling approach which are incapable of representing the entire operating points of the flight envelope very well. Past research works attempt to update the NN model during flight using the mini-batch Levenberg-Marquardt (LM) training. However, due to the limited processing power available in the real-time processor, such approaches can only be employed to relatively small networks and they are limited to model uncoupled helicopter dynamics. In order to accommodate the time-varying properties of helicopter dynamics which change frequently during flight, a recursive Gauss-Newton (rGN) algorithm is developed to properly track the dynamics of the system under consideration.
It is found that the predicted response from the off-line trained neural network model is suitable for modelling the UAS helicopter dynamics correctly. The model structure of the MLP network can be identified correctly using the proposed validation methods. Further comparison with model structure selection from previous studies shows that the identified model structure using the proposed validation methods offers improvements in terms of generalisation error. Moreover, the minimum number of neurons to be included in the model can be easily determined using the proposed cross validation method. The HMLP and modified Elman networks are proposed in this work to reduce the total number of weights used in the standard MLP network. Reduction in the total number of weights in the network structure contributes significantly to the reduction in the computation time needed to train the NN model. Based on the validation test results, the model structure of the HMLP and modified Elman networks are found to be much smaller than the standard MLP network. Although the total number of weights for both of the HMLP and modified Elman networks are lower than the MLP network, the prediction performance of both of the NN models are on par with the prediction quality of the MLP network.
The identification results further indicate that the rGN algorithm is more adaptive to the changes in dynamic properties, although the generalisation error of repeated rGN is slightly higher than the off-line LM method. The rGN method is found capable of producing satisfactory prediction accuracy even though the model structure is not accurately defined. The recursive method presented here in this work is suitable to model the UAS helicopter in real time within the control sampling time and computational resource constraints. Moreover, the implementation of proposed network architectures such as the HMLP and modified Elman networks is found to improve the learning rate of NN prediction. These positive findings inspire the implementation of the real time recursive learning of NN models for the proposed MPC controller. The proposed system identification and hovering control of the unmanned helicopter system are validated in a 6 degree of freedom (DOF) safety test rig. The experimental results confirm the effectiveness and the robustness of the proposed controller under disturbances and parameter changes of the dynamic system.
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Mojica, Velazquez Jose Luis. "A Dynamic Optimization Framework with Model Predictive Control Elements for Long Term Planning of Capacity Investments in a District Energy System." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3886.
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The capacity expansion of a district heating system is studied with the objective of evaluating the investment decision timing and type of capacity expansion. District energy is an energy generation system that provides energy, such as heat and electricity, generated at central locations and distributed to the surrounding area. The study develops an optimization framework to find the optimal investment schedule over a 30 year horizon with the options of investing in traditional heating sources (boilers) or a next-generation combined heat and power (CHP) plant that can provide heat and electricity. In district energy systems, the investment decision on the capacity and type of system is dependent on demand-side requirements, energy prices, and environmental costs. The main contribution of this work is to formulate the capacity planning over a time horizon asa dynamic optimal control problem. In this way, an initial system configuration can be modified by a 'controller' that optimally applies control actions that drive the system from an initial state to an optimal state. The optimal control is a model predictive control (MPC) formulation that not only provides the timing and size of the capacity investment, but also guidance on the mode of operation that meets optimal economic objectives with the given capacity.
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Claro, Érica Rejane Pereira. "Localização de canais afetando o desempenho de controladores preditivos baseados em modelos." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/149927.
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O escopo desta dissertação é o desenvolvimento de um método para detectar os modelos da matriz dinâmica que estejam degradando o desempenho de controladores preditivos baseados em modelos. O método proposto se baseia na análise de correlação cruzada entre o erro nominal do controlador em malha fechada e a uma estimativa da contribuição de cada canal para o cálculo da saída, filtrada pela função de sensibilidade do controlador. Esse método pode ser empregado na auditoria de controladores com variáveis controladas em setpoints e/ou com variáveis que operem entre faixas, como é usual de se encontrar na indústria. Esta dissertação apresenta os resultados da aplicação bem sucedida do método no sistema de quatro tanques (JOHANSSON, 2000), para o qual três cenários foram avaliados. No primeiro cenário, o método localizou corretamente discrepâncias de ganho e de dinâmica de modelos de um controlador preditivo baseado em modelos (Model-based Predictive Controller, ou controlador MPC). No segundo, o método foi utilizado para avaliar a influência de uma variável externa para melhorar o desempenho de um controlador afetado por distúrbios não medidos. No terceiro cenário, o método localizou canais com modelos nulos que deveriam ser incluídos na matriz de controle de um controlador MPC de estrutura descentralizada. Os resultados deste estudo de caso foram comparados com aqueles obtidos pelo método proposto por BADWE, GUDI e PATWARDHAN (2009), constatando-se que o método proposto é mais robusto que o método usado na comparação, não demandando ajustes de parâmetros por parte do usuário para fornecer bons resultados. A dissertação inclui também um estudo de caso da aplicação industrial do método na auditoria de desempenho de um controlador preditivo linear de estrutura descentralizada, com doze variáveis controladas, oito manipuladas e quatro distúrbios não medidos, aplicado a um sistema de fracionamento de propeno e propano em uma indústria petroquímica. A auditoria permitiu reduzir o escopo de revisão do controlador a dezenove canais da matriz, sendo que quatorze destes correspondiam a canais com modelos nulos que deveriam ser incluídos na matriz. A eficácia do método foi comprovada repetindo-se a avaliação da qualidade de modelo para todas as variáveis controladas.<br>The scope of this dissertation is the development of a method to detect the models of the dynamic matrix that are affecting the performance of model-based predictive controllers. The proposed method is based on the cross correlation analysis between the nominal controller error and an estimate of the contribution of each channel to the controller output, filtered by the controller nominal sensitivity function. The method can be used in the performance assessment of controllers employing variables controlled at the setpoint and/or those controlled within ranges. This dissertation presents the results of the successful application of the method to the quadruple-tank process (JOHANSSON, 2000), for which three scenarios were evaluated. In the first scenario, the method correctly located gain and dynamic mismatches on a model-based predictive controller (MPC controller). In the second one, the method was used to evaluate the influence of an external variable to improve the performance of a controller affected by unmeasured disturbances. In the third scenario, the method located null models that should be included in the dynamic matrix of a decentralized MPC controller. The results of the three scenarios were compared with the ones obtained through the method proposed by BADWE, GUDI e PATWARDHAN (2009). The proposed method was considered more robust than the reference one for not requiring parameters estimation performed by the user to provide good results. This dissertation also includes a case study about the application of the method on the performance assessment of an industrial linear predictive controller of decentralized structure. The controller has twelve controlled variables, eight manipulated variables, and four unmeasured disturbances and is applied to a propylene-propane fractionation system of a petrochemical industry. The performance assessment allowed reducing the scope of the controller revision to nineteen channels of the models matrix, fourteen of which were null models that should be included in the controller. The efficacy of the proposed method was confirmed by repeating the model quality evaluation for all the controlled variables.
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Jansson, Lovisa, and Amanda Nilsson. "Evaluation of Model-Based Design Using Rapid Control Prototyping on Forklifts." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158715.
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The purpose of this thesis is to evaluate Rapid Control Prototyping which is apart of the Model-Based Design concept that makes it possible to convenientlytest prototype control algorithms directly on the real system. The evaluation ishere done by designing two different controllers, a gain-scheduled P controllerand a linear Model Predictive Controller (mpc), for the lowering of the forks of aforklift.The two controllers are first tested in a simulation environment. The thesis con-tains two different simulation models: one physical where only minor parameteradjustments are done and one estimated black-box model. After evaluating thecontrollers in a simulation environment they are tested on a real forklift with areal-time target machine.The designed controllers have different strengths and weaknesses as one is non-linear and single variable, the P controller, and the other linear and multivariable,thempc. The P controller has a smooth movement in all situations without be-ing slow, unlike thempc. The disadvantage of the P controller compared to thempcis that there is no guarantee that the P controller will keep the speed limit,whereas thempcapproach gives such a guarantee.The better performance of the P controller outweighs the speed limit guaranteeand thus a conclusion is drawn that the nonlinearities of the system has a largereffect than the multivariable aspect. Also, another conclusion drawn is that work-ing with Model-Based Design and Rapid Control Prototyping makes it possibleto test many different ideas on a real forklift without spending a lot of time onimplementation.<br>Syftet med detta examensarbete är att utvärdera Rapid Control Prototyping vil-ket är en del av modellbaserad utveckling som gör det möjligt att enkelt testamodeller av styralgoritmer direkt på det riktiga systemet. Utvärderingen är gjordgenom att testa två olika regulatorer, en P-regulator med parameterstyrning ochen linjär modelbaserad prediktionsregulator (mpc), för sänkningen av gafflarnapå en truck.De två regulatorerna testas först i en simuleringsmiljö. I arbetet används två olikasimuleringsmodeller: en fysikalisk där endast mindre parameterjusteringar görsoch en estimerad black-box modell. Efter att regulatorerna utvärderas i simule-ringsmiljön testas de även på en riktig truck med hjälp av automatisk kodgenere-ring och exekvering på en dedikerad hårdvaruplattform.De konstruerade regulatorerna har olika för- och nackdelar eftersom en är olinjäroch envariabel, P-regulatorn, och en är linjär men flervariabel,mpc:n. P-regulatornhar en mjuk rörelse i alla lägen utan att bli för långsam, till skillnad frånmpc:n.Nackdelen med P-regulatorn, jämfört medmpc:n är att det inte finns någon ga-ranti för att P-regulatorn håller hastighetsbegränsningen sommpc:n gör.P-regulatorns bättre prestanda överväger garantin om att hålla hastighetsbegräns-ningen och därför dras slutsatsen att olinjäriteterna i systemet överväger effekter-na av det faktum att det också är flervariabelt. En annan slutsats är att modell-baserad utveckling och Rapid Control Prototyping gör det möjligt att testa fleraolika idéer på en riktig gaffeltruck utan att spendera för mycket tid på implemen-tationen.
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Francisco, Denilson de Oliveira. "Manutenção de modelos para controladores preditivos industriais." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/171396.
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O escopo desta dissertação é o desenvolvimento de uma metodologia para identificar os modelos de canais da matriz dinâmica que estejam degradando o desempenho de controladores preditivos, ou MPC (Model Predictive Control), baseado nas técnicas de auditoria e diagnóstico deste tipo de controlador propostas por BOTELHO et al. (2015) e BOTELHO; TRIERWEILER; FARENZENA (2016) e CLARO (2016). A metodologia desenvolvida contempla dois métodos distintos. O primeiro, chamado método direto compensado, tem como base o método direto de identificação em malha fechada (LJUNG, 1987)e compensa cada saída medida do processo de modo a se reter apenas a contribuição do canal que se deseja identificar. O segundo, chamado método do erro nominal, utiliza a definição de saída nominal do processo, proposta por BOTELHO et al. (2015), como métrica para se quantificar o quão próximo o modelo está do comportamento da planta através da minimização do erro nominal. Os métodos foram aplicados ao sistema de quatro tanques cilíndricos (JOHANSSON, 2000) para dois cenários distintos, sendo o primeiro um sistema 2x2 em fase não mínima contendo um MPC trabalhando com setpoint e o segundo um sistema 4x4 em fase mínima com o MPC atuando por faixas. Para o sistema 2x2, se avaliou a influência da localização do canal discrepante (dentro ou fora da diagonal principal da matriz dinâmica de transferência) na eficácia dos métodos. Para o sistema 4x4, o estudo foi voltado para a eficácia dos métodos frentes a controladores que atuam dentro de limites para as variáveis. Os modelos identificados foram comparados pela capacidade de identificar um modelo que capturasse o zero de transmissão da planta e o RGA dinâmico, par ao sistema 2x2, e pelas respostas degrau e diagrama de Bode para o sistema 4x4. O método direto compensado resultou em baixo erro relativo no valor do zero para a discrepância na diagonal principal da matriz dinâmica e alto valor quando a discrepância se encontrava fora da diagonal principal. O método do erro nominal, por sua vez, foi capaz de identificar um modelo cujo zero de transmissão possuía baixo erro relativo frente ao zero da planta em ambos os cenários. No cenário do controlador atuando por faixas, os métodos propostos obtiveram melhores estimativas dos modelos quando comparados com o método concorrente, uma vez que apresentou alto percentual de aderência das saídas simuladas com as saídas medidas. Em todos os cenários estudados, o método do erro nominal se mostrou capaz de identificar um modelo mais robusto, pois este apresentou RGA dinâmico compatível com a planta em todo o range de frequências analisado.<br>The objective of this dissertation is to develop a method to identify the model for the channel of the dynamic matrix that are affecting the performance of model predictive controllers (MPC), based on the assessment and diagnosis techniques for this type of controller proposed by BOTELHO et al. (2015) e BOTELHO; TRIERWEILER; FARENZENA (2016) and CLARO (2016). The proposed methodology includes two different methods. The first, called the compensated direct method, is based on the closed-loop direct identification method (LJUNG, 1987) and compensates each process measured output in order to retain only the contribution of the channel being identified. The second, called nominal error method, uses the definition of the process nominal output, proposed by BOTELHO et al. (2015), as a metric to quantify how close the model is to the actual plant behavior by minimizing the nominal error. The proposed methods were applied to the quadruple-tank system (JOHANSSON, 2000) for two distinct scenarios, the first being a nonminimum-phase 2x2 system containing a MPC working with setpoint and the second a minimum-phase 4x4 system with the MPC working by ranges. For the 2x2 system, the influence of the model mismatch location (inside or outside the main diagonal of the dynamic transfer matrix) on the effectiveness of the methods was evaluated. For the 4x4 system, the study was focused on the effectiveness of the methods with controllers that operate within limits for the variables. The identified models were compared by the capability of identifying a model with accurate plant transmission zero and dynamic RGA, for the 2x2 system, and by the step responses and Bode diagram for the 4x4 system. The compensated direct method resulted in low relative error in the value of the transmission zero for the model mismatch located in the main diagonal of the dynamic matrix and high relative error when the mismatch was outside the main diagonal. On the other hand, the nominal error method was able to identify a model whose transmission zero had low relative error against the plant zero in both scenarios. In the scenario of a controller working by range, the proposed methods obtained better estimates of the models when compared to the concurrent method, since it presented a high percentage of adherence of the simulated outputs with the measured outputs. In all the studied scenarios, the nominal error method was able to identify a more robust model, since it presented dynamic RGA compatible with the plant in the entire range of analyzed frequencies.
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Mörhed, Joakim, and Filip Östman. "Automatic Parking and Path Following Control for a Heavy-Duty Vehicle." Thesis, Linköpings universitet, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-144496.
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The interest in autonomous vehicles has never been higher and there are several components that need to function for a vehicle to be fully autonomous; one of which is the ability to perform a parking at the end of a mission. The objective of this thesis work is to develop and implement an automatic parking system (APS) for a heavy-duty vehicle (HDV). A delimitation in this thesis work is that the parking lot has a known structure and the HDV is a truck without any trailer and access to more computational power and sensors than today's commercial trucks. An automatic system for searching the parking lot has been developed which updates an occupancy grid map (OGM) based on measurements from GPS and LIDAR sensors mounted on the truck. Based on the OGM and the known structure of the parking lot, the state of the parking spots is determined and a path can be computed between the current and desired position. Based on a kinematic model of the HDV, a gain-scheduled linear quadratic (LQ) controller with feedforward action is developed. The controller's objective is to stabilize the lateral error dynamics of the system around a precomputed path. The LQ controller explicitly takes into account that there exist an input delay in the system. Due to minor complications with the precomputed path the LQ controller causes the steering wheel turn too rapidly which makes the backup driver nervous. To limit these rapid changes of the steering wheel a controller based on model predictive control (MPC) is developed with the goal of making the steering wheel behave more human-like. A constraint for maximum allowed changes of the controller output is added to the MPC formulation as well as physical restrictions and the resulting MPC controller is smoother and more human-like, but due to computational limitations the controller turns out less effective than desired. Development and testing of the two controllers are evaluated in three different environments of varying complexity; the simplest simulation environment contains a basic vehicle model and serves as a proof of concept environment, the second simulation environment uses a more realistic vehicle model and finally the controllers are evaluated on a full-scale HDV. Finally, system tests of the APS are performed and the HDV successfully parks with the LQ controller as well as the MPC controller. The concept of a self-parking HDV has been demonstrated even though more tuning and development needs to be done before the proposed APS can be used in a commercial HDV.
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Perez, José Manuel Gonzalez Tubio. "Controle preditivo robusto com realimentação de saída." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/3/3137/tde-08052006-091853/.
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Esse trabalho apresenta uma contribuição para o projeto de um controlador MPC robusto quanto à estabilidade baseado na realimentação da saída e admitindo restrições nas entradas e incertezas no modelo da planta. Ele estende a abordagem existente para o projeto de um MPC considerando o caso particular de um modelo em espaço de estados, onde o estado é lido diretamente da planta, sendo aplicado para a situação em que o sistema escolhido de entradas possa ficar saturado ou que o processo seja representado por um modelo diferente do modelo considerado na função objetivo do controlador. Para isso, o MPC se propõe a resolver o problema de otimização em dois estágios: No estágio off-line, vários controladores sem restrição são obtidos a partir de um problema de otimização onde inequações de Lyapunov são acrescentadas ao problema como restrições de forma a garantir a contração do estado (estabilidade). Esses controladores, representados por uma matriz de ganhos, correspondem a todas configurações possíveis de saturação das variáveis manipuladas para um dado conjunto possível de variáveis controladas. Nessas combinações, incluídas como restrições no controlador, todos os modelos previstos para o processo são considerados. Dessa forma, perdendo-se uma entrada, o subconjunto de saídas controladas pode ser alterado.Na versão anterior do método proposto por Rodrigues & Odloak (2005), esse estágio off-line envolve um observador de estados o que dificulta a solução do problema de otimização do MPC robusto, consumindo grande tempo computacional. Além disso, requer uma solução inicial viável que nem sempre é trivial. Com a versão proposta do sistema de modelo espaço estado, o estimador de estado torna-se desnecessário pois o estado passa a ser medido. Na etapa on-line do projeto do controlador, uma lei ótima de controle é obtida a partir da combinação convexa das configurações de controle que correspondem ao conjunto de variáveis manipuladas disponíveis (não saturadas). Também nessa etapa é considerada a incerteza do modelo utilizado pelo controlador. O controlador proposto é testado com alguns exemplos simulados a partir de modelos obtidos na indústria de processo.<br>In this work, it is presented a contribution to the design of a robust MPC with output feedback, input constraints and uncertain model. This work extends existing approaches by considering a particular non-minimal state space model, which transforms the output feedback strategy into a state feedback strategy. The controller is developed to the case in which the system inputs may become saturated and the model is uncertain. We follow a two stages approach: In the off-line stage, a series of unconstrained robust MPCs is obtained by including in the control optimization problem, inequality constraints that force the state of the closed-loop system to contract along the time. Each of these controllers, represented by a gain matrix, is associated to particular sets of manipulated inputs and controlled outputs. When one manipulated input becomes saturated, we may need to reduce the set of controlled variables. In the existing version of the method, the closed loop system involves a state observer that makes the solution to the robust MPC optimization problem a time consuming step. The problem also requires an initial solution that may not be trivial to find. With the adopted version of the system state space model, the state filter becomes trivial and the state can be considered measured. In the on-line step of the proposed controller design, a sub optimal control law is obtained by combining control configurations that correspond to particular subsets of available manipulated inputs. The method is illustrated with simulation examples of the process industry.
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Paim, Anderson de Campos. "Controle preditivo retroalimentado por estados estimados, aplicado a uma planta laboratorial." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/21258.
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A retroalimentação de controladores preditivos que utilizam modelos em espaço de estado pode ser realizada de duas formas: (a) correção por bias, em que as saídas preditas são corrigidas adicionando-se um valor proporcional a discrepância encontrada entre o valor medido atual e sua respectiva predição e por (b) retroalimentação dos estados, onde se determinam as condições iniciais através da estimação dos estados, e a partir de uma melhor condição inicial se realizam as predições futuras usadas no cálculo das ações de controle. Nesta dissertação estas duas abordagens são comparadas utilizando a Planta Laboratorial de Seis Tanques Esféricos. As técnicas de Filtro de Kalman Estendido (EKF) e Filtro de Kalman Estendido com Restrições (CEKF) foram empregadas para estimar os estados não medidos. Inicialmente foram feitos testes off-line destes algoritmos de estimação. Para estes testes são utilizados uma série de dados da planta laboratorial do estudo de caso, na qual são estudadas as influências de diversos fatores de ajuste que determinam a qualidade final de estimação. Estes ajustes serviram de base para a aplicação destes algoritmos em tempo real, quando então, estimadores de estados estão associados ao sistema de controle do processo baseado em um algoritmo de controle preditivo. Após se ter certificado a qualidade das estimações de estado, partiu-se para sua utilização como uma alternativa de retroalimentação de controladores preditivos. Estes resultados foram comparados com os obtidos através da correção simples por bias. Os resultados experimentais apontam para uma marginal piora devido à retroalimentação por estimadores de estados frente à correção por bias, pelo menos para o caso do controlador preditivo linear utilizado na comparação. Entretanto, espera-se que resultados melhores sejam obtidos no caso de modelos preditivos não-lineares, uma vez que nestes casos o modelo é bem mais sensível à qualidade da condição inicial.<br>The feedback of controllers that use predictive models in state space can be accomplished in two ways: (a) bias correction, where the predicted outputs are corrected by adding a value proportional to the discrepancy found between the current measurement and its respective prediction; and by (b) state feedback, which establishes the initial conditions through the states estimation, and from a better initial condition are carried out the future predictions used in the calculation of control. In this thesis these two approaches are compared using a Laboratorial Plant of Six Spherical Tanks. The techniques of Extended Kalman Filter (EKF) and Constraint Extended Kalman Filter (CEKF) were used to estimate the unmeasured states. Initially, tests were carried out off-line for theses estimation algorithms. For such testing are used a dataset of the plant in case study, in which are studied the influences of several adjustment factors that they determine the final quality of estimation. These adjustments were used of base for the application of these algorithms in real time, when then state estimators are associated with the system of process control based on a predictive control algorithm. After having ascertained the quality of the state estimates, begins its use as an alternative for feedback of predictive controllers. These results were compared with those obtained by the simple correction of bias. The experimental results show a marginal worsening due to feedback from state estimated compared with bias correction, at least for the case of linear predictive controller used in the comparison. However, one expects that better results will be obtained in the case of non-linear predictive models, since in these cases the model is much more sensitive to the quality of the initial condition.
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Fakir, Felipe [UNESP]. "Controle preditivo multi-rate para eficiência energética em sistema de controle via rede sem fio." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/150992.
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Previous issue date: 2017-06-01<br>A tecnologia de comunicação wireless vem se tornando parte fundamental do cotidiano das indústrias de processos, onde o uso de transmissores wireless aplicados à monitoração e controle já é uma realidade. A arquitetura de Sistema de Controle via Rede Sem Fio (WNCS) possui vantagens em relação às arquiteturas tradicionais ponto-a-ponto e às arquiteturas de redes cabeadas devido à facilidade de instalação, configuração e manutenção. No entanto, a evolução desta tecnologia introduziu novos desafios para a implementação da malha de controle fechada por um instrumento wireless como as não linearidades, perda de pacote de dados e restrições da comunicação de dados nas redes sem fio. Outro fator crítico relacionado à implementação de WNCSs é a fonte de energia limitada destes transmissores, que possuem vida útil dependente da quantidade de acessos e dados transmitidos. Este trabalho apresenta o estudo e o desenvolvimento de um controlador preditivo multi-rate como alternativa para melhorar a eficiência energética em aplicações industriais de WNCSs. A estratégia proposta não necessita receber constantemente os valores reais das variáveis do processo transmitidos pelos transmissores wireless, pois o controlador preditivo baseado em modelo (MPC) se utiliza do submodelo interno das variáveis de processo para estimar os valores das variáveis quando estas não são transmitidas. Dessa forma, uma diminuição da frequência de transmissão de dados na rede sem fio pode ser obtida e, consequentemente uma redução do consumo energético dos dispositivos sem fio. Resultados de simulações em diferentes condições de operação de um WNCS multivariável de controle de tanques acoplados demonstram que o MPC multi-rate possui características de robustez e é efetivo para aplicações de WNCS, garantindo requisitos de controle e estabilidade mesmo com a diminuição da frequência de transmissão de dados de realimentação na rede sem fio. Adicionalmente, resultados do consumo energético dos dispositivos do WNCS mostraram que o MPC multi-rate proporciona uma economia de energia de até 20% das baterias dos transmissores wireless. Uma análise da eficiência energética do WNCS é apresentada através do estudo dos limites operacionais do controlador MPC multi-rate considerando a relação de compromisso entre o período de amostragem dos dispositivos sem fio e o desempenho de controle do WNCS.<br>Wireless communication technology has become a fundamental part of the everyday life of process industries, where the use of wireless transmitters for monitoring and control is already a reality. The architecture of Wireless Networked Control Systems (WNCSs) has advantages over point-to-point and wired networks architectures due to the ease of installation, configuration and maintenance. However, the evolution of this technology has introduced new challenges to the implementation of the closed loop control with a wireless instrument as nonlinearities, packet losses and data communication constraints in the wireless networks. Another critical factor related to implementation of WNCSs is the energy source of these transmitters, which have limited lifetime dependent on the amount of access and data transmitted. This work presents the study and the development of a multi-rate predictive controller as an alternative to improve energy efficiency in industrial applications of WNCSs. The proposed strategy does not need to frequently receive updated process variables transmitted by wireless transmitters, because the model predictive controller (MPC) uses the internal submodel of the process variables to estimate the variables values when they are not transmitted. Thus, a decrease in the frequency of data transmission on the wireless network can be obtained and consequently a reduction of energy consumption of wireless devices. Simulation results for different operating conditions of a multivariable WNCS of coupled tanks shows that the multi-rate MPC provides robustness and it is effective for WNCS applications, ensuring control and stability requirements even with the reduction of the transmission frequency of the feedback data in the wireless network. In addition, energy consumption results from the WNCS devices showed that MPC multi-rate provides 20% of energy economy as it is effective in saving the energy expenditure of the wireless transmitter’s battery. An energy efficiency analysis of the WNCS is presented by studying the operating limits of the multi-rate MPC controller considering the compromise relationship between the sampling period of the wireless devices and the control performance of the WNCS.
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ROSSINI, LUCA. "Offline and Online Planning and Control Strategies for the Multi-Contact and Biped Locomotion of Humanoid Robots." Doctoral thesis, Università degli studi di Genova, 2023. https://hdl.handle.net/11567/1107993.
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In the past decades, the Research on humanoid robots made progress forward accomplishing exceptionally dynamic and agile motions. Starting from the DARPA Robotic Challenge in 2015, humanoid platforms have been successfully employed to perform more and more challenging tasks with the eventual aim of assisting or replacing humans in hazardous and stressful working situations. However, the deployment of these complex machines in realistic domestic and working environments still represents a high-level challenge for robotics. Such environments are characterized by unstructured and cluttered settings with continuously varying conditions due to the dynamic presence of humans and other mobile entities, which cannot only compromise the operation of the robotic system but can also pose severe risks both to the people and the robot itself due to unexpected interactions and impacts. The ability to react to these unexpected interactions is therefore a paramount requirement for enabling the robot to adapt its behavior to the task needs and the characteristics of the environment. Further, the capability to move in a complex and varying environment is an essential skill for a humanoid robot for the execution of any task. Indeed, human instructions may often require the robot to move and reach a desired location, e.g., for bringing an object or for inspecting a specific place of an infrastructure. In this context, a flexible and autonomous walking behavior is an essential skill, study of which represents one of the main topics of this Thesis, considering disturbances and unfeasibilities coming both from the environment and dynamic obstacles that populate realistic scenarios.
Locomotion planning strategies are still an open theme in the humanoids and legged robots research and can be classified in sample-based and optimization-based planning algorithms. The first, explore the configuration space, finding a feasible path between the start and goal robot’s configuration with different logic depending on the algorithm. They suffer of a high computational cost that often makes difficult, if not impossible, their online implementations but, compared to their counterparts, they do not need any environment or robot simplification to find a solution and they are probabilistic complete, meaning that a feasible solution can be certainly found if at least one exists. The goal of this thesis is to merge the two algorithms in a coupled offline-online planning framework to generate an offline global trajectory with a sample-based approach to cope with any kind of cluttered and complex environment, and online locally refine it during the execution, using a faster optimization-based algorithm that more suits an online implementation. The offline planner performances are improved by planning in the robot contact space instead of the whole-body robot configuration space, requiring an algorithm that maps the two state spaces.
The framework proposes a methodology to generate whole-body trajectories for the motion of humanoid and legged robots in realistic and dynamically changing environments.
This thesis focuses on the design and test of each component of this planning framework, whose validation is carried out on the real robotic platforms CENTAURO and COMAN+ in various loco-manipulation tasks scenarios.
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Casillo, Danielle Simone da Silva. "Controle preditivo n?o linear baseado no modelo de Hammerstein com prova de estabilidade." Universidade Federal do Rio Grande do Norte, 2009. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15130.
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Previous issue date: 2009-03-27<br>Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior<br>The Predictive Controller has been receiving plenty attention in the last decades, because the need to understand, to analyze, to predict and to control real systems has been quickly growing with the technological and industrial progress. The objective of this thesis is to present a contribution for the development and implementation of Nonlinear Predictive Controllers based on Hammerstein model, as well as to its make properties evaluation. In this case, in the Nonlinear Predictive Controller development the time-step linearization method is used and a compensation term is introduced in order to improve the controller performance. The main motivation of this thesis is the study and stability guarantee for the Nonlinear Predictive Controller based on Hammerstein model. In this case, was used the concepts of sections and Popov Theorem. Simulation results with literature models shows that the proposed approaches are able to control with good performance and to guarantee the systems stability<br>O Controle Preditivo tem recebido muita aten??o nas ?ltimas d?cadas, visto que a necessidade de compreender, analisar, predizer e controlar sistemas reais tem crescido rapidamente com o avan?o tecnol?gico e industrial. O objetivo desta tese ? apresentar uma contribui??o para o desenvolvimento e implementa??o de Controladores Preditivos N?o lineares baseado no modelo de Hammerstein, bem como fazer uma avalia??o de suas propriedades. Neste caso, no desenvolvimento do Controlador Preditivo N?o Linear utiliza-se o m?todo de lineariza??o por degrau de tempo e ? introduzido um termo de compensa??o a fim de melhorar o desempenho do mesmo. A principal motiva??o desta tese ? o estudo e a prova da estabilidade para o Controlador Preditivo N?o Linear baseado no modelo de Hammerstein. Para isso utilizou-se os conceitos de setores e Crit?rio de Popov. Testes de simula??o com modelos da literatura mostram que as abordagens propostas s?o capazes de controlar com um bom desempenho e garantir a estabilidade dos sistemas
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Elgharib, Ahmed Omar Ahmed. "Différentes stratégies de contrôle pour le système d'éolienne connecté PMSG." Electronic Thesis or Diss., Aix-Marseille, 2022. http://www.theses.fr/2022AIXM0647.
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L'énergie éolienne est l'une des sources d'énergie renouvelables les plus attrayantes et prometteuses. Celle-ci offre un excellent substitut à la production d'énergie électrique traditionnelle. Les éoliennes basées sur un PMSG sont les mieux adaptées aux applications autonomes en raison de leur fiabilité et de leur haute efficacité. L'énergie éolienne a continué à jouer un rôle important et peut être considérée comme la source d'énergie renouvelable la plus déployée. Ce travail de recherche propose quelques méthodes de contrôle efficaces associées au contrôle de l'énergie éolienne. Il porte principalement sur le réajustement de certaines approches de contrôle disponibles, comme l'amélioration du NSSFC et du NDSFC, afin d'augmenter les performances du contrôleur pour un tel système. En parallèle, ce travail traite le contrôleur NPIC qui a été ajouté au système en présentant une technique de contrôle sans capteur d'une éolienne PMSG à entraînement direct. Ensuite, le contrôleur PI est étudié dans ce travail en intégrant un algorithme génétique qui a un impact significatif sur l'efficacité et l'exécution des applications éoliennes et de leur système entier. Le MPC est le dernier contrôleur qui a été exploré avec ses résultats de simulation pour le système. Tous ces contrôleurs utilisent PMSG. Plusieurs tests expérimentaux ont été appliqués à une grande variété de configurations, afin de valider les résultats de simulation obtenus. Cette thèse de recherche servira comme référence pour les études futures sur le contrôle des systèmes d'éoliennes<br>Renewable energy is considered as a viable alternative to conventional fossil fuel generators globally. One of the appealing and promising renewable energy sources is wind energy. This renewable energy source offers an excellent substitute for the generation of traditional electricity. Wind turbines based on PMSG are best suited for stand-alone applications due to their reliability. This research work proposes some efficient control methods associated with wind energy control. It is focused more on the readjustment of some available control approaches as the improvement of NSSFC (nonlinear static state feedback controller) and NDSFC (nonlinear dynamic state feedback controller) to increase the controller performance for such a system. In sequence with that, this work moves forward to another controller(NPIC) which has been added to this system by presenting a sensor-less control technique of direct driven PMSG wind turbine. Afterwards, PI Controller is studied in this work by integrating genetic algorithm that has significant impact on the efficiency and execution of wind turbine applications and their whole system. Model predictive control (MPC) is thelast controller that has been explored. All of these controllers are using PMSG, discussed under different operating ranges of wind speed. Several experimental tests were applied to wide variety of configurations in order to validate the simulation results produced. This research aims to serve as a detailed reference for future studies on the control of wind turbine systems
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Lopes, Jos? Soares Batista. "Controle preditivo robusto baseado em desigualdades matriciais lineares aplicado a um sistema de tanques acoplados." Universidade Federal do Rio Grande do Norte, 2011. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15339.
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Previous issue date: 2011-02-14<br>This work deals with an on-line control strategy based on Robust Model
Predictive Control (RMPC) technique applied in a real coupled tanks system.
This process consists of two coupled tanks and a pump to feed the liquid to the
system. The control objective (regulator problem) is to keep the tanks levels in
the considered operation point even in the presence of disturbance. The RMPC
is a technique that allows explicit incorporation of the plant uncertainty in the
problem formulation. The goal is to design, at each time step, a state-feedback
control law that minimizes a 'worst-case' infinite horizon objective function,
subject to constraint in the control. The existence of a feedback control law
satisfying the input constraints is reduced to a convex optimization over linear
matrix inequalities (LMIs) problem. It is shown in this work that for the plant
uncertainty described by the polytope, the feasible receding horizon state
feedback control design is robustly stabilizing. The software implementation of
the RMPC is made using Scilab, and its communication with Coupled Tanks
Systems is done through the OLE for Process Control (OPC) industrial protocol<br>Este trabalho tem como objetivo desenvolver uma estrat?gia de controle on-line
baseado no Controlador Preditivo Robusto (RMPC, acr?nimo do ingl?s Robust
Model Predictive Control) aplicado a um sistema real de tanques acoplados.
Este processo consiste em sistema de dois tanques conectados, cujo liquido ?
enviado aos mesmos por uma bomba. O objetivo do controle (problema
regulat?rio) ? deixar os n?veis dos tanques no ponto de opera??o considerado,
mesmo na presen?a de perturba??es. A s?ntese da t?cnica RMPC consiste em
incorporar de forma explicita as incertezas da planta na formula??o do
problema. O objetivo do projeto, a cada per?odo de amostragem, ? encontrar
uma realimenta??o de estados que minimiza o pior caso de uma fun??o
objetivo com horizonte infinito, sujeita a restri??es no sinal de controle. O
problema original, do tipo Min-max, ? reduzido em a problema de otimiza??o
convexa expresso em desigualdades matriciais lineares (LMI, Linear Matriz
Inequalities). Mostram-se, neste trabalho, a descri??o da incerteza da planta na
forma polit?pica e as condi??es de factibilidade do problema de otimiza??o. A
implementa??o do algoritmo RMPC foi feita utilizando o software Scilab e a sua
comunica??o com o sistema de tanques acoplados foi feita atrav?s do
protocolo OPC (do ingl?s OLE for Process Control)
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LU, ZONG-XUN, and 呂宗訓. "Predictive and expert-type fuzzy controller desigen." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/74646644256975279136.
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Sutton, Gordon J. "Nonlinear model-predictive controller design." Phd thesis, 1999. http://hdl.handle.net/1885/147965.
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Simma, Govindarajulu. "Performance Analysis of Process Using Model Predictive Controller Strategy." Thesis, 2013. http://ethesis.nitrkl.ac.in/5419/1/211EC3319.pdf.
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Distillation is a method of separating mixtures based on different in their boiling points. It is a
physical separation and not a chemical operation. McCabe-Thiele method used to find the no of
trays in a distillation column of tray type distillation column. In this thesis we will see how and
why certain variables may be manipulated to control product composition in distillation column,
to control distillation column we used model predictive control.
Model predictive control (MPC) has become the leading form of advanced multi variable control
technique in process industry. With the help of this thesis we want to presents reliable tuning
strategy for unconstrained single input single out put (SISO) dynamic matrix control (DMC).
The tuning strategy achieves set point with minimal over shoot and modest manipulate input
move sizes and it applicable to a broad class of open loop stable process.
We used DMC algorithms for model control algorithm; in this thesis explain Single input Single
output and Multi input and Multi output DMC algorithms.
This thesis presents a model predictive control strategy for multivariable nonlinear control
problems2 2,3 3 and 4 4process in distillation column,also explain how the tuning
parameters affect the step response model of water heater.
The aim is to provide a solution to nonlinear control problem that is favorable in terms of
industrial implementation. MPC TOOLS of MATLAB® has used to simulate the all process.
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Muller, Bernard. "Development of a model predictive controller for a milling circuit." Diss., 2000. http://hdl.handle.net/2263/24424.
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Please read the abstract (Synopsis) in the section, 00front, of this document<br>Dissertation (M Eng (Control Engineering))--University of Pretoria, 2008.<br>Chemical Engineering<br>MEng<br>unrestricted
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Petryna, Stephen. "Model predictive control of a thermoelectric-based heat pump." Thesis, 2013. http://hdl.handle.net/10155/397.
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Government regulations and growing concerns regarding global warming has
lead to an increasing number of passenger vehicles on the roads today that are not
powered by the conventional internal combustion (IC) engine. Automotive manufacturers
have introduced electric powertrains over the last 10 years which have
introduced new challenges regarding powering accessory loads historically reliant on
the mechanical energy of the IC engine. High density batteries are used to store
the electrical energy required by an electric powertrain and due to their relatively
narrow acceptable temperature range, require liquid cooling. The cooling system in
place currently utilizes the A/C compressor for cooling and a separate electric element
for heating which is energy expensive when the source of energy is electricity.
The proposed solution is a thermoelectric heat pump for both heating and cooling.
A model predictive controller (MPC) is designed, implemented and tested to
optimize the operation of the thermoelectric heat pump. The model predictive
controller is chosen due to its ability to accept multiple constrained inputs and
outputs as well as optimize the system according to a cost function which may
consist of any parameters the designer chooses. The system is highly non-linear and
complex therefore both physical modelling and system identi cation were used to
derive an accurate model of the system. A steepest descent algorithm was used for
optimization of the cost function.
The controller was tested in a test bench environment. The results show the
thermoelectric heat pump does hold the battery at the speci ed set point however
more optimization was expected from the controller. The controller fell short of
expectation due to operational restriction enforced during design meant to simplify
the problem. The MPC controller is capable of much better performance through
adding more detail to the model, an improved optimization algorithm and allowing
more flexibility in set point selection.
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Lin, Bo-Nian, and 林柏年. "Microgrid Frequency Improvement Using a Model Predictive Controller for Doubly Fed Induction Generator." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/9j7z9z.
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碩士<br>國立臺灣大學<br>電機工程學研究所<br>106<br>Design of a model predictive auxiliary frequency controller and maximum power tracking compensator for a doubly-fed induction generator (DFIG) in a microgrid is investigated in this thesis. When there’s frequency change in a microgrid, the conventional approach is to rely on the inertia control, primary control, and secondary control of synchronous generators to stabilize the system frequency. With the increasing need of green energy, some of the traditional synchronous generators are replaced by wind turbine generators. If the wind turbine generators are not provided with auxiliary frequency controller mechanism, satisfactory frequency response can not be achieved. Therefore, the wind turbines must be designed with the auxiliary frequency controller in order to improve system frequency response.
In a doubly-fed induction generator (DFIG), the auxiliary frequency controller is usually installed on the rotor side converter (RSC) and frequency regulation is achieved through a droop control signal which is proportional to frequency deviation and the torque reference command of the RSC is modulated through this droop control input. In previous works, the gain of the auxiliary frequency controller was fixed. However, a fixed-gain auxiliary frequency controller is not able to provide satisfactory frequency response when there is a change in generator parameter or wind speed. Moreover, underfrequency load shedding must be enforced when the frequency deviation exceeds the preset value.
In the present work, a model predictive auxiliary frequency controller is designed for the DFIG in order to improve frequency response in a microgrid. The plant predictive model will change when there is a change in generator parameter or wind speed. As a result, better frequency response can be achieved with the adaptive control provided by the proposed model predictive auxiliary frequency controller. Furthermore, underfrequency load shedding can be avoided with the implementation of state variable (frequency) constraint in the model predictive auxiliary frequency controller.
When there is a change in system frequency, the auxiliary frequency controller of the wind turbines would provide the needed active power to the system timely. In the dynamic process, the rotating speed of the wind turbine would decrease. As a result, the maximum power tracking control and frequency compensation are affected. Therefore, the investigation of maximum power tracking compensator is required. The research results show that, with maximum power tracking compensator, the frequency response in the dynamic process can be improved.
In order to demonstrate the effectiveness of the proposed model predictive auxiliary frequency controller and maximum power tracking compensator, MATLAB/Simulink dynamic simulation are performed on a microgrid in central Taiwan which comprises conventional synchronous generators and off-shore wind farms.
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Jhang, Ji-Wei, and 張繼偉. "Design and Implementation of Model-Free Predictive Current Controller for Brushless DC Motor Drive Systems." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/05741755094557018564.
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(8967548), Hikmet Duygu Ozdemir. "EVALUATION OF MODEL PREDICTIVE CONTROL METHOD FOR COLLISION AVOIDANCE OF AUTOMATED VEHICLES." Thesis, 2020.
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<div>Collision avoidance design plays an essential role in autonomous vehicle technology. It's an attractive research area that will need much experimentation in the future. This research area is very important for providing the maximum safety to automated vehicles, which have to be tested several times under different circumstances for safety before use in real life. This thesis proposes a method for designing and presenting a collision avoidance maneuver by using a model predictive controller with a moving obstacle for automated vehicles. It consists of a plant model, an adaptive MPC controller, and a reference trajectory. The proposed strategy applies a dynamic bicycle model as the plant model, adaptive model predictive controller for the lateral control, and a custom reference trajectory for the scenario design. The model was developed using the Model Predictive Control Toolbox and Automated Driving Toolbox in Matlab. Builtin tools available in Matlab/Simulink were used to verify the modeling approach and analyze the performance of the system. The major contribution of this thesis work was implementing a novel dynamic obstacle avoidance control method for automated vehicles. The study used validated parameters obtained from previous research. The novelty of this research was performing the studies using a MPC based controller instead of a sliding mode controller, that was primarily used in other studies. The results obtained from the study are compared with the validated models. The comparisons consisted of the lateral overlap,lateral error, and steering angle simulation results between the models. Additionally,this study also included outcomes for the yaw angle. The comparisons and other outcomes obtained in this study indicated that the developed control model produced reasonably acceptable results and recommendations for future studies.</div>
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Oosthuizen, Daniël Jacobus. "Economic evaluation and design of an electric arc furnace controller based on economic objectives." Diss., 2001. http://hdl.handle.net/2263/30199.
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Please read the abstract in the section, 00front of this document<br>Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2007.<br>Electrical, Electronic and Computer Engineering<br>MEng<br>unrestricted
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Iqbal, Mohammad Hasan. "Advanced control of the twin screw extruder." Phd thesis, 2010. http://hdl.handle.net/10048/1438.
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This research deals with the modeling and control of a plasticating twin screw extruder (TSE) that will be used to obtain consistent product quality. The TSE is a widely used process technology for compounding raw polymers. Compounding creates a polymer with improved properties that satisfy the demand of modern plastic applications. Modeling and control of a TSE is challenging because of its high nonlinearity, inherent time delay, and multiple interactive dynamic behavior. A complete methodology is proposed in this thesis to design an advanced control scheme for a TSE. This methodology was used to develop a model predictive control scheme for a laboratory scale plasticating TSE and to implement the control scheme in real-time. The TSE has a processing length of 925 mm and a length to screw diameter ratio (L/D) of 37. High density polyethylenes with different melt indices were used as processing materials.
Manipulated variables and disturbance variables were selected based on knowledge of the process. Controlled variables were selected using a selection method that includes a steady state correlation between process output variables and product quality variables, and dynamic considerations. Two process output variables, melt temperature (Tm) at the die and melt pressure (Pm) at the die, were selected as controlled variables.
A new modeling approach was proposed to develop grey box models based on excitation in the extruder screw speed (N), one of the manipulated variables. The extruder was excited using a predesigned random binary sequence (RBS) type excitation in N and nonlinear models relating Tm and Pm to N were developed using this approach. System identification techniques were used to obtain model parameters. The obtained models have an autoregressive moving average with exogenous (ARMAX) input structure and the models explain the physics of the extrusion process successfully.
The TSE was also excited using a predesigned RBS in the feed rate (F) as a manipulated variable. Models relating Tm and Pm to F were developed using a classical system identification technique; both models have ARMAX structures. The model between Pm and F was found to give excellent prediction for data obtained from a stair type excitation, indicating that the obtained models provide a good representation of the dynamics of the twin screw extruder.
Analysis of the TSE open loop process indicated two manipulated variables, N and F, and two controlled variables, Tm and Pm. Thus, a model predictive controller (MPC) was designed using the developed models for this 2X2 system and implemented in real-time. The performance of the MPC was studied by checking its set-point tracking ability. The robustness of the MPC was also examined by imposing external disturbances.
Finally, a multimodel operating regime was used to model Tm and N. The operating regime was divided based on the screw speed, N. Local models were developed using system identification techniques. The global model was developed by combining local models using fuzzy logic methodology. Simulated results showed excellent response of Tm for a wide operating range. A similar approach was used to design a global nonlinear proportional-integral controller (n-PI) and a nonlinear MPC (n-MPC). Both the controllers showed good set-points tracking ability over the operating range. The multiple model-based MPC showed smooth transitions from one operating regime to another operating regime.<br>Process Control
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Chiu, Chi-Lun, and 邱啟倫. "Design and Implementation of Model-Free Predictive Current Controller for Four-Switch Three-Phase Inverter-Fed Synchronous Reluctance Motors." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/45034255736362108121.
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碩士<br>國立臺灣海洋大學<br>電機工程學系<br>103<br>This thesis presents a novel predictive current control for four-switch three-phase inverter-fed synchronous reluctance motor drive systems. We not only develop a new strategy for four-switch three-phase inverter-fed synchronous reluctance motor drive systems, but also discuss the feasibility and correctness of this method so as to improve the ability of the current control of the motor drive system. Furthermore, we expect to meet the industrial requirements of low-cost drive system equipped with high-performance current control. Compared to the six-switch drive system, the four-switch three-phase drive system can effectively reduce the development costs in both hardware and software. For a four-switch three-phase synchronous reluctance motor drive system, its switch strategies that we can choose are few. Generally speaking, the drawback of hysteresis current control is that it has large ripples in the output currents of the inverter. In order to improve the current-tracking performance, the development of the new switching strategy for low-cost drive system is urgently needed. As a result, the proposed method is based on the stator current measurement and the current variation so as to predict the future stator current. A digital signal processor, TMS320F2809, made by Texas Instruments Company, is used to execute the algorithms of the proposed predictive current control and hysteresis current control. Experimental results can be used as a reference for developing a new switching strategy of a four-switch three-phase drive system in the future.
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QUANG, LE TRI, and 黎志光. "System Identification and Autonomous Flight Controller Design Based on Adaptive Learning-Based and Model Predictive Control for a Small-Scaled Unmanned Helicopter." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/u45465.
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博士<br>逢甲大學<br>機械與航空工程博士學位學程<br>107<br>The dynamic models of a helicopter are highly nonlinear and change dramatically among different flight conditions, such as hovering, cruising, taking off, and landing. Since the longitudinal and lateral motions are highly coupling, the system identification and control system design of a helicopter still remain challenging because of its inherent complex dynamic movements. These nonlinear dynamics can be linearized by the concept of stability derivatives according to a specific trim condition. The full state-space model has more than 30 unknown parameters in the system matrix, A, and control matrix, B. Determining all of these parameters is impractical. Therefore, the full model is suggested broken into sub-systems such as pitch, roll, yaw, and heave models; then the subspace method is applied to reconstruct and determine initial values. Finally, the prediction error estimate (PEM) is utilized to improve the accuracy of the estimated models. On the other hand, the yaw response oscillates more than other attitude motions due to inherent nonlinearity. In this study, the yaw dynamic is considered as a second-order system for the yaw rate state variable. Then unknown parameters are estimated by Levenberg-Marquardt (LM) method.
Based on the derived models, the integration of model predictive control (MPC), Laguerre function, and exponential data weighting is proposed to control pitch, roll, and heave motions. The Laguerre function is utilized to reduce the matrix size in the optimization procedure of MPC, which can reduce the computing load of the onboard computer. Moreover, the exponential data weighting is adopted to modify the original cost function for solving the numerical conditioning problem, and it is able to increase the stability of the flight control system. Due to a limitation of the sub-systems, which cannot solve the coupled dynamic, the online dynamic inversion is proposed based on the recursive learning algorithm, which is regarded as an online adaptation and its output signal is added to the control system. This approach is able to solve the limitation caused by using subsystems as mentioned above.
On the other hand, in this article, the adaptive control based on neural approximation is proposed to control the yaw motion. By using the Lyapunov stability theory, the off-line training process is not a requirement. The weight matrix is updated online. By using the proposed method, the measured error and effect of the environmental disturbance are eliminated. The designed control system is verified and compared with the fuzzy controller under ideal and turbulent environments. The simulation results show that the proposed method performs much better than that of fuzzy.
Finally, the proposed solutions are implemented by C language in a HIL system. Then feasibility verification is performed in the developed simulation environment. Simulation Results have shown that the performance of the proposed method is significantly better than that of H∞, and it has the potential to apply in practice.