Relevant bibliographies by topics / Nonlinear Model Predictive Control - NMPC

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  2. Dissertations / Theses
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Academic literature on the topic 'Nonlinear Model Predictive Control - NMPC'

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Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Nonlinear Model Predictive Control - NMPC"

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Al Younes, Younes, and Martin Barczyk. "Nonlinear Model Predictive Horizon for Optimal Trajectory Generation." Robotics 10, no. 3 (July 14, 2021): 90. http://dx.doi.org/10.3390/robotics10030090.

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This paper presents a trajectory generation method for a nonlinear system under closed-loop control (here a quadrotor drone) motivated by the Nonlinear Model Predictive Control (NMPC) method. Unlike NMPC, the proposed method employs a closed-loop system dynamics model within the optimization problem to efficiently generate reference trajectories in real time. We call this approach the Nonlinear Model Predictive Horizon (NMPH). The closed-loop model used within NMPH employs a feedback linearization control law design to decrease the nonconvexity of the optimization problem and thus achieve faster convergence. For robust trajectory planning in a dynamically changing environment, static and dynamic obstacle constraints are supported within the NMPH algorithm. Our algorithm is applied to a quadrotor system to generate optimal reference trajectories in 3D, and several simulation scenarios are provided to validate the features and evaluate the performance of the proposed methodology.
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Fnadi, Mohamed, and Julien Alexandre dit Sandretto. "Experimental Validation of a Guaranteed Nonlinear Model Predictive Control." Algorithms 14, no. 8 (August 20, 2021): 248. http://dx.doi.org/10.3390/a14080248.

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This paper combines the interval analysis tools with the nonlinear model predictive control (NMPC). The NMPC strategy is formulated based on an uncertain dynamic model expressed as nonlinear ordinary differential equations (ODEs). All the dynamic parameters are identified in a guaranteed way considering the various uncertainties on the embedded sensors and the system’s design. The NMPC problem is solved at each time step using validated simulation and interval analysis methods to compute the optimal and safe control inputs over a finite prediction horizon. This approach considers several constraints which are crucial for the system’s safety and stability, namely the state and the control limits. The proposed controller consists of two steps: filtering and branching procedures enabling to find the input intervals that fulfill the state constraints and ensure the convergence to the reference set. Then, the optimization procedure allows for computing the optimal and punctual control input that must be sent to the system’s actuators for the pendulum stabilization. The validated NMPC capabilities are illustrated through several simulations under the DynIbex library and experiments using an inverted pendulum.
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Jung, Sooyong, and John T. Wen. "Nonlinear Model Predictive Control for the Swing-Up of a Rotary Inverted Pendulum." Journal of Dynamic Systems, Measurement, and Control 126, no. 3 (September 1, 2004): 666–73. http://dx.doi.org/10.1115/1.1789541.

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This paper presents the experimental implementation of a gradient-based nonlinear model predictive control (NMPC) algorithm to the swing-up control of a rotary inverted pendulum. The key attribute of the NMPC algorithm used here is that it only seeks to reduce the error at the end of the prediction horizon rather than finding the optimal solution. This reduces the computation load and allows real-time implementation. We discuss the implementation strategy and experimental results. In addition to NMPC based swing-up control, we also present results from a gradient based iterative learning control, which is the basis our NMPC algorithm.
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Minh, Vu Trieu. "Nonlinear Model Predictive Controller and Feasible Path Planning for Autonomous Robots." Open Computer Science 6, no. 1 (November 15, 2016): 178–86. http://dx.doi.org/10.1515/comp-2016-0015.

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AbstractThis paper develops the nonlinear model predictive control (NMPC) algorithm to control autonomous robots tracking feasible paths generated directly from the nonlinear dynamic equations.NMPC algorithm can secure the stability of this dynamic system by imposing additional conditions on the open loop NMPC regulator. The NMPC algorithm maintains a terminal constrained region to the origin and thus, guarantees the stability of the nonlinear system. Simulations show that the NMPC algorithm can minimize the path tracking errors and control the autonomous robots tracking exactly on the feasible paths subject to the system’s physical constraints.
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Wei, Yao, Yanjun Wei, Yening Sun, Hanhong Qi, and Mengyuan Li. "An Advanced Angular Velocity Error Prediction Horizon Self-Tuning Nonlinear Model Predictive Speed Control Strategy for PMSM System." Electronics 10, no. 9 (May 10, 2021): 1123. http://dx.doi.org/10.3390/electronics10091123.

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In nonlinear model predictive control (NMPC), higher accuracy can be obtained with a shorter prediction horizon in steady-state, better dynamics can be obtained with a longer prediction horizon in a transient state, and calculation burden is proportional to the prediction horizon which is usually pre-selected as a constant according to dynamics of the system with NMPC. The minimum calculation and prediction accuracy are hard to ensure for all operating states. This can be improved by an online changing prediction horizon. A nonlinear model predictive speed control (NMPSC) with advanced angular velocity error (AAVE) prediction horizon self-tuning method has been proposed in which the prediction horizon is improved as a discrete-time integer variable and can be adjusted during each sampling period. A permanent magnet synchronous motor (PMSM) rotor position control system with the proposed strategy is accomplished. Tracking performances including rotor position Integral of Time-weighted Absolute value of the Error (ITAE), the maximal delay time, and static error are improved about 15.033%, 23.077%, and 10.294% respectively comparing with the conventional NMPSC strategy with a certain prediction horizon. Better disturbance resisting performance, lower weighting factor sensitivities, and higher servo stiffness are achieved. Simulation and experimental results are given to demonstrate the effectiveness and correctness.
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Bai, Guoxing, Yu Meng, Li Liu, Weidong Luo, Qing Gu, and Li Liu. "Review and Comparison of Path Tracking Based on Model Predictive Control." Electronics 8, no. 10 (September 23, 2019): 1077. http://dx.doi.org/10.3390/electronics8101077.

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Recently, model predictive control (MPC) is increasingly applied to path tracking of mobile devices, such as mobile robots. The characteristics of these MPC-based controllers are not identical due to the different approaches taken during design. According to the differences in the prediction models, we believe that the existing MPC-based path tracking controllers can be divided into four categories. We named them linear model predictive control (LMPC), linear error model predictive control (LEMPC), nonlinear model predictive control (NMPC), and nonlinear error model predictive control (NEMPC). Subsequently, we built these four controllers for the same mobile robot and compared them. By comparison, we got some conclusions. The real-time performance of LMPC and LEMPC is good, but they are less robust to reference paths and positioning errors. NMPC performs well when the reference velocity is high and the radius of the reference path is small. It is also robust to positioning errors. However, the real-time performance of NMPC is slightly worse. NEMPC has many disadvantages. Like LMPC and LEMPC, it performs poorly when the reference velocity is high and the radius of the reference path is small. Its real-time performance is also not good enough.
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Thangavel, Sakthi, Radoslav Paulen, and Sebastian Engell. "Robust Multi-Stage Nonlinear Model Predictive Control Using Sigma Points." Processes 8, no. 7 (July 16, 2020): 851. http://dx.doi.org/10.3390/pr8070851.

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We address the question of how to reduce the inevitable loss of performance that is incurred by robust multi-stage NMPC due to the lack of knowledge compared to the case where the exact plant model (no uncertainty) is available. Multi-stage NMPC in the usual setting over-approximates a continuous parametric uncertainty set by a box and includes the corners of the box and the center point into the scenario tree. If the uncertainty set is not a box, this augments the uncertainty set and results in a performance loss. In this paper, we propose to mitigate this problem by two different approaches where the scenario tree of the multi-stage NMPC is built using sigma points. The chosen sigma points help to capture the true mean and covariance of the uncertainty set more precisely. The first method computes a box over-approximation of the reachable set of the system states whereas the second method computes a box over-approximation of the reachable set of the constraint function using the unscented transformation. The advantages of the proposed schemes over the traditional multi-stage NMPC are demonstrated using simulation studies of a simple semi-batch reactor and a more complex industrial semi-batch polymerization reactor benchmark example.
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Keighobadi, J., J. Faraji, and S. Rafatnia. "Chaos Control of Atomic Force Microscope System Using Nonlinear Model Predictive Control." Journal of Mechanics 33, no. 3 (September 13, 2016): 405–15. http://dx.doi.org/10.1017/jmech.2016.89.

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AbstractOwing to robust and optimal specification, model predictive control method has received wide attentions over recent years. Since in certain operational conditions, an Atomic/scanning Force Microscope (AFM) shows chaos behavior, the chaos feedback control of the AFM system is considered. According to the nonlinear model of forces interacting between the tip of micro cantilever and the substrate of AFM; the nonlinear control methods are proposed. In the paper, the chaos control of a micro cantilever AFM based on the nonlinear model predictive control (NMPC) technique is presented. Through software simulation results, the effectiveness of the designed NMPC of the AFM is assessed. The simulation results together with analytical stability proofs indicate that the proposed method is effective in keeping the system in a stable range.
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Isaza Hurtado, Jhon Alexander, Diego A. Muñoz, and Hernán Álvarez. "Efficient solution of nonlinear model predictive control by a restricted enumeration method." Enfoque UTE 9, no. 4 (December 21, 2018): 13–23. http://dx.doi.org/10.29019/enfoqueute.v9n4.393.

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This work presents an alternative method to solve the nonlinear program (NLP) for nonlinear model predictive control (NMPC) problems. The NLP is the most computational demanding task in NMPC, which limits the industrial implementation of this control strategy. Therefore, it is important to consider algorithms that can solve the nonlinear program, not only in real time but also guaranteeing feasibility. In this work, the restricted enumeration method is proposed as alternative to solve the NLP for NMPC problems, showing successful results for pH control in a sugar cane process plant. This method enumerates in restricted way a set of final control element possible positions around the current one. Next, it tests all positions in that set to find the best one, taken as the optimization solution.
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Dettori, Stefano, Alessandro Maddaloni, Filippo Galli, Valentina Colla, Federico Bucciarelli, Damaso Checcacci, and Annamaria Signorini. "Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control." Energies 14, no. 13 (July 2, 2021): 3998. http://dx.doi.org/10.3390/en14133998.

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The current flexibility of the energy market requires operating steam turbines that have challenging operation requirements such as variable steam conditions and higher number of startups. This article proposes an advanced control system based on the Nonlinear Model Predictive Control (NMPC) technique, which allows to speed up the start-up of steam turbines and increase the energy produced while maintaining rotor stress as a constraint variable. A soft sensor for the online calculation of rotor stress is presented together with the steam turbine control logic. Then, we present how the computational cost of the controller was contained by reducing the order of the formulation of the optimization problem, adjusting the scheduling of the optimizer routine, and tuning the parameters of the controller itself. The performance of the control system has been compared with respect to the PI Controller architecture fed by the soft sensor results and with standard pre-calculated curves. The control architecture was evaluated in a simulation exploiting actual data from a Concentrated Solar Power Plant. The NMPC technique shows an increase in performance, with respect to the custom PI control application, and encouraging results.
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Dissertations / Theses on the topic "Nonlinear Model Predictive Control - NMPC"

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Norén, Christoffer. "Path Planning for Autonomous Heavy Duty Vehicles using Nonlinear Model Predictive Control." Thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-95547.

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In the future autonomous vehicles are expected to navigate independently and manage complex traffic situations. This thesis is one of two theses initiated with the aim of researching which methods could be used within the field of autonomous vehicles. The purpose of this thesis was to investigate how Model Predictive Control could be used in the field of autonomous vehicles. The tasks to generate a safe and economic path, to re-plan to avoid collisions with moving obstacles and to operate the vehicle have been studied. The algorithm created is set up as a hierarchical framework defined by a high and a low level planner. The objective of the high level planner is to generate the global route while the objectives of the low level planner are to operate the vehicle and to re-plan to avoid collisions. Optimal Control problems have been formulated in the high level planner for the use of path planning. Different objectives of the planning have been investigated e.g. the minimization of the traveled length between the start and the end point. Approximations of the static obstacles' forbidden areas have been made with circles. A Quadratic Programming framework has been set up in the low level planner to operate the vehicle to follow the high level pre-computed path and to locally re-plan the route to avoid collisions with moving obstacles. Four different strategies of collision avoidance have been implemented and investigated in a simulation environment.
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Penet, Maxime. "Robust Nonlinear Model Predictive Control based on Constrained Saddle Point Optimization : Stability Analysis and Application to Type 1 Diabetes." Phd thesis, Supélec, 2013. http://tel.archives-ouvertes.fr/tel-00968899.

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This thesis deals with the design of a robust and safe control algorithm to aim at an artificial pancreas. More precisely we will be interested in controlling the stabilizing part of a classical cure. To meet this objective, the design of a robust nonlinear model predictive controller based on the solution of a saddle point optimization problem is considered. Also, to test the controller performances in a realistic case, numerical simulations on a FDA validated testing platform are envisaged.In a first part, we present an extension of the usual nonlinear model predictive controller designed to robustly control, in a sampled-data framework, systems described by nonlinear ordinary differential equations. This controller, which computes the best control input by considering the solution of a constrained saddle point optimization problem, is called saddle point model predictive controller (SPMPC). Using this controller, it is proved that the closed-loop is Ultimately Bounded and, with some assumptions on the problem structure, Input-to State practically Stable. Then, we are interested in numerically solving the corresponding control problem. To do so, we propose an algorithm inspired from the augmented Lagrangian technique and which makes use of adjoint model.In a second part, we consider the application of this controller to the problem of artificial blood glucose control. After a modeling phase, two models are retained. A simple one will be used to design the controller and a complex one will be used to simulate realistic virtual patients. This latter is needed to validate our control approach. In order to compute a good control input, the SPMPC controller needs the full state value. However, the sensors can only provide the value of blood glucose. That is why the design of an adequate observer is envisaged. Then, numerical simulations are performed. The results show the interest of the approach. For all virtual patients, no hypoglycemia event occurs and the time spent in hyperglycemia is too short to induce damageable consequences. Finally, the interest of extending the SPMPC approach to consider the control of time delay systems in a sampled-data framework is numerically explored.
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Kozubík, Michal. "Aplikace nelineárního prediktivního řízení pro pohon se synchronním motorem." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400605.

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This thesis focuses on the possibilities of application of nonlinear model predictive control for electric drives. Specifically, for drives with a permanent magnet synchronous motor. The thesis briefly describes the properties of this type of drive and presents its mathematical model. After that, a nonlinear model of predictive control and methods of nonlinear optimization, which form the basis for the controller output calculation, are described. As it is used in the proposed algorithm, the Active set method is described in more detail. The thesis also includes simulation experiments focusing on the choice of the objective function on the ability to control the drive. The same effect is examined for the different choices of the length of the prediction horizon. The end of the thesis is dedicated to the comparison between the proposed algorithm and commonly used field oriented control. The computational demands of the proposed algorithm are also measured and compared to the used sampling time.
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Murali, madhavan rathai Karthik. "Synthesis and real-time implementation of parameterized NMPC schemes for automotive semi-active suspension systems." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALT052.

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Cette thèse traite de la synthèse et de la mise en œuvre en temps réel (RT) de schémas de contrôle prédictif non linéaire paramétré (pNMPC) pour les systèmes de suspension semi-active des automobiles. Le schéma pNMPC est basé sur une technique d'optimisation par simulation en boîte noire. Le point essentiel de la méthode est de paramétrer finement le profil d'entrée et de simuler le système pour chaque entrée paramétrée et d'obtenir la valeur approximative de l'objectif et de la violation des contraintes pour le problème pNMPC. Avec les résultats obtenus de la simulation, l'entrée admissible (si elle existe) ayant la valeur objective minimale ou, à défaut, la valeur de violation de contrainte la plus faible est sélectionnée et injectée dans le système et ceci est répété indéfiniment à chaque période de décision. La méthode a été validée expérimentalement sur dSPACE MicroAutoBoX II (MABXII) et les résultats montrent de bonnes performances de l'approche proposée. La méthode pNMPC a également été étendue à une méthode pNMPC parallélisée et la méthode proposée a été mise en œuvre pour le contrôle du système de suspension semi-active d'un demi-véhicule. Cette méthode a été mise en œuvre grâce à des unités de traitement graphique (GPU) qui servent de plate-forme modèle pour la mise en œuvre d'algorithmes parallèles par le biais de ses processeurs multi-cœurs. De plus, une version stochastique de la méthode pNMPC parallélisée est proposée sous le nom de schéma pNMPC à Scénario-Stochastique (SS-pNMPC). Cette méthode a été mise en œuvre et testée sur plusieurs cartes NVIDIA embarquées pour valider la faisabilité de la méthode proposée pour le contrôle du système de suspension semi-active d'un demi-véhicule. En général, les schémas pNMPC parallélisés offrent de bonnes performances et se prêtent bien à un large espace de paramétrage en entrée. Enfin, la thèse propose un outil logiciel appelé "pNMPC - A code generation software tool for implementation of derivative free pNMPC scheme for embedded control systems". L'outil logiciel de génération de code (S/W) a été programmé en C/C++ et propose également une interface avec MATLAB/Simulink. Le logiciel de génération de code a été testé pour divers exemples, tant en simulation que sur du matériel embarqué en temps réel (MABXII), et les résultats semblent prometteurs et viables pour la mise en œuvre de la RT pour des applications réelles. L'outil de génération de code S/W comprend également une fonction de génération de code GPU pour une mise en œuvre parallèle. Pour conclure, la thèse a été menée dans le cadre du projet EMPHYSIS et les objectifs du projet s'alignent sur cette thèse et les méthodes pNMPC proposées sont compatibles avec la norme eFMI
This thesis discusses the synthesis and real-time (RT) implementation of parameterized Nonlinear Model Predictive Control (pNMPC) schemes for automotive semi-active suspension systems. The pNMPC scheme uses a black-box simulation-based optimization method. The crux of the method is to finitely parameterize the input profile and simulate the system for each parameterized input and obtain the approximate objective and constraint violation value for the pNMPC problem. With the obtained results from the simulation, the input with minimum objective value or the least constraint violation value is selected and injected into the system and this is repeated in a receding horizon fashion. The method was experimentally validated on dSPACE MicroAutoBoX II (MABXII) and the results display good performance of the proposed approach. The pNMPC method was also augmented to parallelized pNMPC and the proposed method was implemented for control of semi-active suspension system for a half car vehicle. This method was implemented by virtue of Graphic Processing Units (GPUs) which serves as a paragon platform for implementation of parallel algorithms through its multi-core processors. Also, a stochastic version of the parallelized pNMPC method is proposed which is termed as Scenario-Stochastic pNMPC (SS-pNMPC) scheme and the method was implemented and tested on several NVIDIA embedded boards to verify and validate the RT feasibility of the proposed method for control of semi-active suspension system for a half car vehicle. In general, the parallelized pNMPC schemes provide good performance and also, fares well for large input parameterization space. Finally, the thesis proposes a software tool termed “pNMPC – A code generation software tool for implementation of derivative free pNMPC scheme for embedded control systems”. The code generation software (S/W) tool was programmed in C/C++ and also, provides interface to MATLAB/Simulink. The S/W tested for variety of examples both in simulation as well as on RT embedded hardware (MABXII) and the results looks promising and viable for RT implementation for real world applications. The code generation S/W tool also includes GPU code generation feature for parallel implementation. To conclude, the thesis was conducted under the purview of the EMPHYSIS project and the goals of the project align with this thesis and the proposed pNMPC methods are amenable with eFMI standard
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Azevedo, Diego Sousa de. "Otimização do código do sistema de navegação e controle de robôs móveis baseado em NMPC para embarcar em arquiteturas de baixo custo." Universidade Federal da Paraíba, 2015. http://tede.biblioteca.ufpb.br:8080/handle/tede/7853.

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The purpose of this study is to adapt and embed a navigation system and control of mobile robots, based on NMPC, in a low-cost board existent on the market, to provide sufficient com-putational resources so that the robot is able to converge, without losing performance, using the same horizons applied in a Laptop. The obtained results demonstrate the proposed scenario according with the experiments, proving that it is possible to use low cost boards, to a navigation system and control of mobile robots, based on NMPC, using the same predictive and control horizons applied in a Laptop.
A proposta desse trabalho é adaptar e embarcar um sistema de navegação e controle de robôs móveis, baseado em NMPC, em uma placa de baixo custo já existente no mercado, que dispo-nibilize recursos computacionais suficientes para que o Robô seja capaz de convergir, sem perda de desempenho e utilizando os mesmos horizontes aplicados em um Laptop. Os Resulta-dos obtidos demonstram todo o cenário proposto e de acordo com os experimentos realizados, comprovou-se que é possível o uso de placas de baixo custo, para controle de robôs móveis, baseado em NMPC, utilizando os mesmos horizontes de predição e controle aplicados em uma Laptop.
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Furieri, Luca. "Geometric versus Model Predictive Control based guidance algorithms for fixed-wing UAVs in the presence of very strong wind fields." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11872/.

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The recent years have witnessed increased development of small, autonomous fixed-wing Unmanned Aerial Vehicles (UAVs). In order to unlock widespread applicability of these platforms, they need to be capable of operating under a variety of environmental conditions. Due to their small size, low weight, and low speeds, they require the capability of coping with wind speeds that are approaching or even faster than the nominal airspeed. In this thesis, a nonlinear-geometric guidance strategy is presented, addressing this problem. More broadly, a methodology is proposed for the high-level control of non-holonomic unicycle-like vehicles in the presence of strong flowfields (e.g. winds, underwater currents) which may outreach the maximum vehicle speed. The proposed strategy guarantees convergence to a safe and stable vehicle configuration with respect to the flowfield, while preserving some tracking performance with respect to the target path. As an alternative approach, an algorithm based on Model Predictive Control (MPC) is developed, and a comparison between advantages and disadvantages of both approaches is drawn. Evaluations in simulations and a challenging real-world flight experiment in very windy conditions confirm the feasibility of the proposed guidance approach.
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Sriniwas, Ganti Ravi. "Nonlinear model predictive control." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/10267.

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Al, Seyab Rihab Khalid Shakir. "Nonlinear model predictive control using automatic differentiation." Thesis, Cranfield University, 2006. http://hdl.handle.net/1826/1491.

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Although nonlinear model predictive control (NMPC) might be the best choice for a nonlinear plant, it is still not widely used. This is mainly due to the computational burden associated with solving online a set of nonlinear differential equations and a nonlinear dynamic optimization problem in real time. This thesis is concerned with strategies aimed at reducing the computational burden involved in different stages of the NMPC such as optimization problem, state estimation, and nonlinear model identification. A major part of the computational burden comes from function and derivative evaluations required in different parts of the NMPC algorithm. In this work, the problem is tackled using a recently introduced efficient tool, the automatic differentiation (AD). Using the AD tool, a function is evaluated together with all its partial derivative from the code defining the function with machine accuracy. A new NMPC algorithm based on nonlinear least square optimization is proposed. In a first–order method, the sensitivity equations are integrated using a linear formula while the AD tool is applied to get their values accurately. For higher order approximations, more terms of the Taylor expansion are used in the integration for which the AD is effectively used. As a result, the gradient of the cost function against control moves is accurately obtained so that the online nonlinear optimization can be efficiently solved. In many real control cases, the states are not measured and have to be estimated for each instance when a solution of the model equations is needed. A nonlinear extended version of the Kalman filter (EKF) is added to the NMPC algorithm for this purpose. The AD tool is used to calculate the required derivatives in the local linearization step of the filter automatically and accurately. Offset is another problem faced in NMPC. A new nonlinear integration is devised for this case to eliminate the offset from the output response. In this method, an integrated disturbance model is added to the process model input or output to correct the plant/model mismatch. The time response of the controller is also improved as a by–product. The proposed NMPC algorithm has been applied to an evaporation process and a two continuous stirred tank reactor (two–CSTR) process with satisfactory results to cope with large setpoint changes, unmeasured severe disturbances, and process/model mismatches. When the process equations are not known (black–box) or when these are too complicated to be used in the controller, modelling is needed to create an internal model for the controller. In this thesis, a continuous time recurrent neural network (CTRNN) in a state–space form is developed to be used in NMPC context. An efficient training algorithm for the proposed network is developed using AD tool. By automatically generating Taylor coefficients, the algorithm not only solves the differentiation equations of the network but also produces the sensitivity for the training problem. The same approach is also used to solve online the optimization problem of the NMPC. The proposed CTRNN and the predictive controller were tested on an evaporator and two–CSTR case studies. A comparison with other approaches shows that the new algorithm can considerably reduce network training time and improve solution accuracy. For a third case study, the ALSTOM gasifier, a NMPC via linearization algorithm is implemented to control the system. In this work a nonlinear state–space class Wiener model is used to identify the black–box model of the gasifier. A linear model of the plant at zero–load is adopted as a base model for prediction. Then, a feedforward neural network is created as the static gain for a particular output channel, fuel gas pressure, to compensate its strong nonlinear behavior observed in open–loop simulations. By linearizing the neural network at each sampling time, the static nonlinear gain provides certain adaptation to the linear base model. The AD tool is used here to linearize the neural network efficiently. Noticeable performance improvement is observed when compared with pure linear MPC. The controller was able to pass all tests specified in the benchmark problem at all load conditions.
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Fannemel, Åsmund Våge. "Dynamic Positioning by Nonlinear Model Predictive Control." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8921.

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This thesis discusses the theoretical aspects of the unscented Kalman filter (UKF) and nonlinear model predictive control (NMPC) and try to evaluate their practical value in a dynamic positioning (DP) system. A nonlinear horizontal vessel model is used as the basis for performing state, disturbance, and parameter estimation, and attempts at controling the vessel using NMPC are made. It is shown that the extended Kalman filter (EKF), which is much used in various navigation applications including DP, is outperformed both theoretically and practically in simulations by the UKF. Much of which is due to the UKF's improved approximation of the estimated system's true stochastic properties. An attempt to estimate the current from the hydrodynamical damping forces have been applied and shown to be working when the vessel is not subjected to other slowly-varying drift forces. It is implemented a dual estimation approach to try to estimate hydrodynamic damping, which is a very real problem for actual vessels and DP systems. A theoretical evaluation of NMPC is performed and it is concluded that NMPC schemes could fulfill a need in vessel control and DP. Its combination of model based control, optimization approach to achieving performance and predictive properties are indeed useful also for DP. It is found that NMPC could be a step towards a unified control approach combining low and high speed reference tracking, station-keeping and several other control operations which today are handled by separate control approaches. NMPC provides the control designer with an exceptional amount of freedom when quantifying the performance, that it is impossible not to find some use for NMPC.

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Balbis, Luisella. "Nonlinear model predictive control for industrial applications." Thesis, University of Strathclyde, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501892.

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Books on the topic "Nonlinear Model Predictive Control - NMPC"

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Allgöwer, Frank. Nonlinear Model Predictive Control. Basel: Birkhäuser Basel, 2000.

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Magni, Lalo, Davide Martino Raimondo, and Frank Allgöwer, eds. Nonlinear Model Predictive Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1.

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Grüne, Lars, and Jürgen Pannek. Nonlinear Model Predictive Control. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46024-6.

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Grüne, Lars, and Jürgen Pannek. Nonlinear Model Predictive Control. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-501-9.

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Allgöwer, Frank, and Alex Zheng, eds. Nonlinear Model Predictive Control. Basel: Birkhäuser Basel, 2000. http://dx.doi.org/10.1007/978-3-0348-8407-5.

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Grancharova, Alexandra, and Tor Arne Johansen. Explicit Nonlinear Model Predictive Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0.

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Jürgen, Pannek, ed. Nonlinear model predictive control: Theory and algorithms. London: Springer, 2011.

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Albin Rajasingham, Thivaharan. Nonlinear Model Predictive Control of Combustion Engines. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68010-7.

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service), SpringerLink (Online, ed. Nonlinear model predictive control: Towards new challenging applications. Berlin: Springer, 2009.

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Grancharova, Alexandra. Explicit Nonlinear Model Predictive Control: Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Book chapters on the topic "Nonlinear Model Predictive Control - NMPC"

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Grüne, Lars, and Jürgen Pannek. "Economic NMPC." In Nonlinear Model Predictive Control, 221–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46024-6_8.

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Grüne, Lars, and Jürgen Pannek. "Distributed NMPC." In Nonlinear Model Predictive Control, 259–95. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46024-6_9.

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Grancharova, Alexandra, and Tor Arne Johansen. "Explicit Stochastic NMPC." In Explicit Nonlinear Model Predictive Control, 157–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0_7.

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Grancharova, Alexandra, and Tor Arne Johansen. "Semi-explicit Distributed NMPC." In Explicit Nonlinear Model Predictive Control, 209–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0_9.

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Böhm, Christoph, Felix Heß, Rolf Findeisen, and Frank Allgöwer. "An NMPC Approach to Avoid Weakly Observable Trajectories." In Nonlinear Model Predictive Control, 275–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_22.

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Syafiie, S., J. Niño, C. Ionescu, and R. De Keyser. "NMPC for Propofol Drug Dosing during Anesthesia Induction." In Nonlinear Model Predictive Control, 501–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_40.

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Biegler, Lorenz T. "Efficient Solution of Dynamic Optimization and NMPC Problems." In Nonlinear Model Predictive Control, 219–43. Basel: Birkhäuser Basel, 2000. http://dx.doi.org/10.1007/978-3-0348-8407-5_13.

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Grancharova, Alexandra, and Tor Arne Johansen. "Explicit NMPC via Approximate mp-NLP." In Explicit Nonlinear Model Predictive Control, 87–110. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0_4.

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Goodwin, Graham C., Jan Østergaard, Daniel E. Quevedo, and Arie Feuer. "A Vector Quantization Approach to Scenario Generation for Stochastic NMPC." In Nonlinear Model Predictive Control, 235–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_19.

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Cannon, Mark, Desmond Ng, and Basil Kouvaritakis. "Successive Linearization NMPC for a Class of Stochastic Nonlinear Systems." In Nonlinear Model Predictive Control, 249–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_20.

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Conference papers on the topic "Nonlinear Model Predictive Control - NMPC"

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Lee, Tae-Kyung, and Zoran S. Filipi. "Control Oriented Modeling and Nonlinear Model Predictive Control of Advanced SI Engine System." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4024.

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Control oriented model (COM) using crank-angle resolved flame propagation simulation and nonlinear model predictive control (NMPC) methodology for the purpose of transient control of HDOF engines are proposed in this paper. The nonlinear nature of the combustion process has been a challenge in building a reliable COM and engine simulation. Artificial neural networks (ANNs) are subsequently trained on the data generated with a quasi-D combustion model to create fast surrogate combustion models. System dynamics are augmented by manifold and actuator dynamics models. Then, NMPC for an internal combustion (IC) engine with a dual-independent variable valve timing (VVT) system is designed to achieve fast torque responses, to eliminate exhaust emissions penalty, and to track the optimal actuator response closely. The NMPC significantly improves engine dynamics and minimizes excursions of in-cylinder variables under highly transient operation. Dead-beat like control is achieved with selected prediction horizon and control horizon in the NMPC.
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Mu, Junxia, and David Rees. "Nonlinear Model Predictive Control for Gas Turbine Engines." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53146.

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In this paper Nonlinear Model Predictive Control (NMPC) is applied to a gas turbine engine. Since the performance of model based control schemes is highly dependent on the accuracy of the process model, the estimation of global nonlinear gas turbine models using NARMAX and neural network is first examined. To solve the NMPC problem, the Newton-based Levenberg-Marquardt Approach (NLMA) with hard constraints and Sequential Quadratic Programming (SQP) with soft constraints are validated using a wide range of large random, small and ramp signal tests. It is shown that the control performance using SQP is slightly better than that of NLMA, and proposed methods are robust in the face of large disturbances and model uncertainties. The results presented illustrate the improvement in the control performance using both methods over against gain-scheduling PID controllers.
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Brunell, Brent J., Daniel E. Viassolo, and Ravi Prasanth. "Model Adaptation and Nonlinear Model Predictive Control of an Aircraft Engine." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53780.

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The performance improvement of constrained nonlinear model predictive control (NMPC) with state and parameter estimation over traditional control architectures is investigated and applied to a model turbofan aircraft engine. Strong nonlinearities are present in turbofan aircraft engines due to the large range of operating conditions and power levels experienced during a typical mission. Also, turbine operation is restricted due to mechanical, aerodynamic, thermal, and flow limitations. Current control methodologies rely strictly on a priori information; therefore they fail to utilize current engine state or health information for reducing conservatism and improving engine performance. NMPC is selected because it depends on a model that can be adapted to the current engine conditions, it can explicitly handle the nonlinearities, both input and output constraints of many variables, and determine the optimal control that will meet the requirements for any engine condition all in a single control formulation. A physics based component level model is developed as the heart of the architecture. The state or health of the engine is determined using a joint state and parameter estimator utilizing extended Kalman filter (EKF) techniques. With the necessary engine information in hand, a constrained NMPC is used to determine the optimal actuator commands. Results regarding steady state performance improvements are presented.
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Yebi, Adamu, Bin Xu, Xiaobing Liu, John Shutty, Paul Anschel, Simona Onori, Zoran Filipi, and Mark Hoffman. "Nonlinear Model Predictive Control Strategies for a Parallel Evaporator Diesel Engine Waste Heat Recovery System." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9801.

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This paper discusses the control challenges of a parallel evaporator organic Rankine cycle (ORC) waste heat recovery (WHR) system for a diesel engine. A nonlinear model predictive control (NMPC) is proposed to regulate the mixed working fluid outlet temperature of both evaporators, ensuring efficient and safe ORC system operation. The NMPC is designed using a reduced order control model of the moving boundary heat exchanger system. In the NMPC formulation, the temperature difference between evaporator outlets is penalized so that the mixed temperature can be controlled smoothly without exceeding maximum or minimum working fluid temperature limits in either evaporator. The NMPC performance is demonstrated in simulation over an experimentally validated, high fidelity, physics based ORC plant model. NMPC performance is further validated through comparison with a classical PID control for selected high load and low load engine operating conditions. Compared to PID control, NMPC provides significantly improved performance in terms of control response time, overshoot, and temperature regulation.
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Yi, Jingang, Jianbo Li, and Hao Lin. "Spherical Modeling and Nonlinear Model Predictive Control of Electroporation." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-5984.

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Electroporation is an effective means to deliver molecules into the cellular cytoplasm, and has been widely applied in both biological research and clinical applications. The process however suffers from low viability and efficiency. In this work, we present a multi-pulse feedback control scheme for enhancing viability and efficiency of electroporation process. We first present a spherical model for cell transmembrane potential (TMP) dynamics to describe the spatial distribution of electric potential on cell membrane. Then we analyze the models and discuss the bifurcation property of the system. Based on the dynamic models, we design a nonlinear model predictive controller (NMPC) to track pore size profiles in electroporation. We demonstrate that by using NMPC, the pore size can be precisely regulated to targeted values for drug delivery applications. The control design is illustrated through simulation examples.
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Taniguchi, Tomoki, Jun Umeda, Toshifumi Fujiwara, Kangsoo Kim, Takumi Sato, and Shogo Inaba. "Path Following Control of Autonomous Underwater Vehicle Using Nonlinear Model Predictive Control." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18241.

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Abstract The path following control of an AUV considering arrival times at waypoints is proposed in this paper. The temporal constraint is considered by adding the surge velocity and the nominal thrust force as reference trajectory in the objective function of the nonlinear model predictive control (NMPC). The proposed control strategy uses fewer reference variables than conventional trajectory tracking problems. The simulated results of the proposed control strategy are compared to the NMRI Cruising AUV#4 actual dive data. The simulated arrival times of waypoints were matched well to the measured data. Two guidance laws, the line of sight with lookahead-based steering law and the pure pursuit guidance law, are also applied to NMPC to determine reference yaw angle.
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Wang, Yunlai, and Xi Wang. "Fast Model Predictive Control for Aircraft Engine Based on Automatic Differentiation." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-16161.

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Abstract Nonlinear model predictive control (NMPC) is a strategy suitable for dealing with highly complex, nonlinear, uncertain, and constrained dynamics involved in aircraft engine control problems. Because of the complexity of the algorithm and the real-time performance of the predictive model, it has thus far been infeasible to implement model predictive control in the realtime control system of aircraft engine. In most nonlinear model predictive control, nonlinear interior point methods (IPM) are used to calculate the optimal solution, which iterate to the optimal solution based on the Jacobian and Hessian matrix. Most nonlinear IPM solver, such as MATLAB fmincon and IPOPT, cannot calculate the Jacobian and Hessian matrix precisely and quickly, instead of using numerical differentiation to calculate the Jacobian matrix and BFGS method to approach to the Hessian matrix. From what has been discussed above, we will 1) improve the real-time performance of predictive model by replacing the time-consuming component level model (CLM) with a neural network model, which is trained based on the data of component level model, 2) precisely calculate the Jacobian and Hessian matrix using automatic differentiation, and propose a group of algorithms to make NMPC strategy quicker, which include making use of the structure of predictive model, and the integrity of weighted sums of Hessian matrix in IPM. Finally, considering input and output constraints, the fast NMPC strategy is compared with normal NMPC. Simulation results with mean time of 19.3% – 27.9% of normal NMPC on different platforms, verify that the fast NMPC proposed can improve the real-time performance during the process of acceleration, deceleration for aircraft engine.
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Cao, Xiaoqing, and Beshah Ayalew. "Robust Nonlinear Model Predictive Control for Infrared Drying of Automotive Coatings." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9736.

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In this paper, a scheme for estimation and control of moisture content in infrared (IR) drying of paints/coatings is proposed. To deal with the infinite-dimensional nature of the process model associated with the moisture diffusion in the coating film, POD-Galerkin method is first applied for model reduction. Then, an unscented Kalman filter (UKF) is devised for distributed moisture content estimation and nonlinear model predictive control (NMPC) system is designed for tracking a desired average moisture content profile with optimized energy needs. To enhance the control performance in the presence of potential modeling uncertainties, a robust design is also included in the proposed NMPC scheme. The effectiveness of this approach is demonstrated via simulated applications to IR drying of automotive waterborne coatings.
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Wahid, Abdul, and Sendy Winata. "Application of conventional nonlinear model predictive control (NMPC) and economic nonlinear model predictive control (E-NMPC) for technical and economical optimization of biochemical reactor system." In INTERNATIONAL CONFERENCE ON TRENDS IN MATERIAL SCIENCE AND INVENTIVE MATERIALS: ICTMIM 2020. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0013655.

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Ono, Takeyuki, Ryosuke Eto, Junya Yamakawa, and Hidenori Murakami. "Nonlinear Model Predictive Control of a Stewart Platform Motion Stabilizer." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23725.

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Abstract In an operating room of a hospital ship, to remotely perform surgery on a patient laid on an operating table utilizing the surgical manipulators attached to the table, the rotation and translation of the operating table must be properly isolated from the wave-induced motion of the floor. Similarly, on a moving vehicle, when a sensitive equipment is transported or a manipulator is utilized to perform precise positioning tasks, it becomes necessary to isolate them from undesirable motion of the vehicle. In response to the need for a motion stabilizer, which isolates a manipulator from undesirable ship or vehicle motion, we present a nonlinear model predictive control (NMPC) of a six degrees-of-freedom, base-moving Stewart platform. Analytical nonlinear equations of motion are utilized for nonlinear model predictive control, wherein an optimization problem in a finite time horizon at each time step is solved adopting C/GMRES algorithm. To predict the future motion caused by a ship or a moving vehicle, we employ an auto regressive moving average model which forecasts future behavior based on past behavior. Furthermore, to incorporate prediction errors as disturbance at each time step, we endow NMPC with the robustness. As a result, even if prediction errors exist, the set of all possible output states are predicted using the equations of motion in a finite time, while the system kinematic constraints are precisely satisfied. In order to assess the performance of the proposed controller, numerical simulations are presented for a base-moving Stewart platform.
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Reports on the topic "Nonlinear Model Predictive Control - NMPC"

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Nishimura, Masatsugu, Yoshitaka Tezuka, Enrico Picotti, Mattia Bruschetta, Francesco Ambrogi, and Toru Yoshii. Study of Rider Model for Motorcycle Racing Simulation. SAE International, January 2020. http://dx.doi.org/10.4271/2019-32-0572.

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Various rider models have been proposed that provide control inputs for the simulation of motorcycle dynamics. However, those models are mostly used to simulate production motorcycles, so they assume that all motions are in the linear region such as those in a constant radius turn. As such, their performance is insufficient for simulating racing motorcycles that experience quick acceleration and braking. Therefore, this study proposes a new rider model for racing simulation that incorporates Nonlinear Model Predictive Control. In developing this model, it was built on the premise that it can cope with running conditions that lose contact with the front wheels or rear wheels so-called "endo" and "wheelie", which often occur during running with large acceleration or deceleration assuming a race. For the control inputs to the vehicle, we incorporated the lateral shift of the rider's center of gravity in addition to the normally used inputs such as the steering angle, throttle position, and braking force. We compared the performance of the new model with that of the conventional model under constant radius cornering and straight braking, as well as complex braking and acceleration in a single (hairpin) corner that represented a racing run. The results showed that the new rider model outperformed the conventional model, especially in the wider range of running speed usable for a simulation. In addition, we compared the simulation results for complex braking and acceleration in a single hairpin corner produced by the new model with data from an actual race and verified that the new model was able to accurately simulate the run of actual MotoGP riders.
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