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Wu, Yue. "The design and application of a new type of adaptive fuzzy model-based controller." Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432574.
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Couch, Jeremy Robert. "An ECMS-Based Controller for the Electrical System of a Passenger Vehicle." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366076350.
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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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Sudakar, Madhavan. "Novel control techniques for a quadrotor based on the Sliding Mode Controller." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613746605628363.
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Yung, K. L. "Microprocessor based non-linear adaptive controller." Thesis, University of Plymouth, 1985. http://hdl.handle.net/10026.1/2492.
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The advent of microprocessors has created the possibility of developing low cost adaptive controllers for small process plants which in the past badly needed but could not afford such controllers. To examine the practicality of developing advanced low cost microprocessor based controller, this thesis describes the development of a non-linear adaptive controller for a nylon crimping plant which is a typical example of small process plants. In order to test the algorithm on site, an algorithm development/implement device basing on a novel multi-tasking concept was developed. This novel microprocessor based device can perform program development, on-line algorithm test and data logging at the same time, while, still maintaining its small size for easy transportation. When the control algorithm was fully developed and tested, a low cost dedicated controller using an Intel 8085 processor was designed to house the algorithm and as a direct replacement of the original analogue controller.
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Griesebner, Klaus. "Model-based Controller Development." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-34929.
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Model-based design is a powerful design technique for embedded system development. The technique enables virtual prototyping to develop and debug controllers before touching real hardware. There are many tools available covering the distinct steps of the design cycle including modeling, simulation, and implementation. Unfortunately, none of them covers all three steps. This thesis proposes a formalism coupling the model and the implementation of a controller for equation-based simulation tools. The resulting formalism translates defined controller models to platform specific code using a defined set of syntax. A case study of a line-following robot has been developed to illustrate the feasibility of the approach. The prototype has been tested and evaluated using a sequence of test scenarios of increasing difficulty. The final experiments suggest that the behaviors of both modeled and generated controllers are similar. The thesis concludes that the approach of model-implementation coupling of controllers in the simplest form is feasible for equation-based tools. This allows it to conduct the whole model-based design cycle within a single environment.
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Patkar, Abhishek. "Adaptive neural controller based on convex parametrization." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128972.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, September, 2020<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 65-67).<br>The problem of control of a class of nonlinear plants has been addressed by using neural networks together with sliding mode control to lead to global boundedness. We revisit this problem in this thesis and suggest a specific class of neural networks that employ convex activation functions. By using the algorithms that have been proposed previously for adaptive control in the presence of convex/concave parameterization for adjusting the weights of the neural network, it is shown that global boundedness of all signals can be achieved together with a better tracking error than non-adaptive controllers. It is also shown through simulation studies of an aircraft landing problem that the proposed adaptive controller can lead to better learning of the underlying nonlinearity.<br>by Abhishek Patkar.<br>S.M.<br>S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering
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Marchant, Andrew Nicholas. "An adaptive knowledge based controller for refrigerated potato stores." Thesis, University of Exeter, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393347.
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Rapley, Veronica Elizabeth. "Model-based adaptive cluster sampling." Thesis, University of Southampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433939.
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Logan, Beth Teresa. "Adaptive model-based speech enhancement." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.625004.
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Cowan, Richard Watson. "An adaptive statistically based controller for through-feed centerless grinding." Thesis, Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/17570.
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Sagiroglu, Serkan. "Adaptive Neural Network Applications On Missile Controller Design." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611106/index.pdf.
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In this thesis, adaptive neural network controllers are designed for a high subsonic cruise missile. Two autopilot designs are included in the study using adaptive neural networks, namely an altitude hold autopilot designed for the longitudinal channel and a directional autopilot designed for heading control. Aerodynamic coefficients are obtained using missile geometry<br>a 5-Degree of Freedom (5-DOF) simulation model is obtained, and linearized at a single trim condition. An inverted model is used in the controller. Adaptive Neural Network (ANN) controllers namely, model inversion controllers with Sigma-Pi Neural Network, Single Hidden Layer Neural Network and Background Learning implemented Single Hidden Layer Neural Network, are deployed to cancel the modeling error and are applied for the longitudinal and directional channels of the missile. This approach simplifies the autopilot designing process by combining a controller with model inversion designed for a single flight condition with an on-line learning neural network to account for errors that are caused due to the approximate inversion.
Simulations are performed both in the longitudinal and directional channels in order to demonstrate the effectiveness of the implemented control algorithms. The advantages and drawbacks of the implemented neural network based controllers are indicated.
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Panjapornpon, Chanin Soroush Masoud. "Model-based controller design for general nonlinear processes /." Philadelphia, Pa. : Drexel University, 2005. http://dspace.library.drexel.edu/handle/1860/611.
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Kaya, Ibrahim. "Relay feedback identification and model based controller design." Thesis, University of Sussex, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299955.
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Postlewaite, Bruce E. "The development of a model-based fuzzy controller." Thesis, University of Strathclyde, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358805.
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Bruner, Hugh S. Steck James. "A model following inverse controller with adaptive compensation for general aviation aircraft." Diss., A link to full text of this thesis in SOAR, 2007. http://soar.wichita.edu/dspace/handle/10057/1073.
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Thesis (Ph.D.)--Wichita State University, College of Engineering, Dept. of Aerospace Engineering.<br>"May 2007." Title from PDF title page (viewed on October 25, 2007). Thesis adviser: James Steck. Includes bibliographic references (leaves 146-149).
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Horner, Anne. "Design of a decentralized model reference adaptive controller for a mobile robot." Thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-11072008-063541/.
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Gardner, Donald Anderson. "Experimental Testing of a Decentralized Model Reference Adaptive Controller for a Mobile Robot." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/34130.
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Adaptive controllers allow robots to perform a wide variety of tasks, but the extensive computations required have generated an interest in developing decentralized adaptive controllers. Horner has designed an adaptive controller for a four-degree-of-freedom mobile robot and tested it through simulations. The study described in this thesis uses the techniques described by Horner to design and test a decentralized model reference adaptive controller (DMRAC) for a physical four-degree-of-freedom mobile robot. The study revealed several difficulties in implementing this design. Most notably, the robot available for the research did not allow for the measurement of joint velocity, so it was necessary to estimate the velocity as the derivative of the position measurement. The noise created by this estimation made completion of testing impossible. Future research should be performed on a robot that provides joint velocity measurement. Alternatively, a study could include state estimation as part of the controller, thus reducing and possibly eliminating the need for velocity measurement.<br>Master of Science
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Mohd, Ashraf bin Ahmad. "Model-Free Controller Design based on Simultaneous Perturbation Stochastic Approximation." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/199449.
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Brug, Arnold van de. "A framework for model-based adaptive training." Thesis, Heriot-Watt University, 1996. http://hdl.handle.net/10399/1177.
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Shaban-Zanjani, Hamid. "Accurate positioning of a servo DC-motor using self adjusting-adaptive sliding model controller." Thesis, University of Ottawa (Canada), 1994. http://hdl.handle.net/10393/9517.
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This study presents a strategy for position feedback control, a so-called self-adjusting adaptive sliding mode controller, to obtain a desired steady state error for a DC-motor servo in the presence of Coulomb friction and stiction. Sliding mode controllers are of the type of nonlinear controllers which have been studied extensively by Russian scientists, including V. I. Utkin and V. Itkis. The self adjusting sliding mode control method that this thesis is focused on is an adaptive discontinuous controller with an approximated sliding mode trajectory. The study is concerned with both analytical and experimental performances of such a class of control strategy and a PID (proportional integral and derivative) controller in the presence of dry friction for the purpose of comparison. The study also investigates the effect of nonlinear phenomenon such as torque saturation on the states of the variables of such a system. The result of the paper is finally based on the suitable performance of the suggested control methodology utilizing a new form of sliding mode controller for the second order class of systems, rather than the conventional forms from V. Itkis and V. I. Utkin.
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Trimmel, Stefan. "Evaluation of Model-Based Testing on a Base Station Controller." Thesis, Linköping University, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-12059.
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<p>This master thesis investigates how well suited the model-based testing process is for testing a new feature of a Base Station Controller. In model-based testing the tester designs a behavioral model of the system under test, or some part of the system. This model is then given to a test generation tool that will analyze the model and produce interesting test cases. These test cases can either be run on the system in an automatic or manual way depending on what type of setup there is.</p><p>In this report it is suggested that the behavioral model should be produced in as early a stage as possible and that it should be a collaboration between the test team and the design team.</p><p>The advantages with the model-based testing process are a better overview of the test cases, the test cases are always up to date, it helps in finding errors or contradictions in requirements and it performs closer collaboration between the test team and the design team. The disadvantages with model-based testing process are that it introduces more sources where an error can occur. The behavioral model can have errors, the layer between the model and the generated test cases can have errors and the layer between the test cases and the system under test can have errors. This report also indicates that the time needed for testing will be longer compared with manual testing.</p><p>During the pilot, when a part of a new feature was tested, of this master thesis a test generation tool called Qtronic was used. This tool solves a very challenging task which is generating test cases from a general behavioral model and with a good result. This tool provides many good things but it also has its shortages. One of the biggest shortages is the debugging of the model for finding errors. This step is very time consuming because it requires that a test case generation is performed on the whole model. When there is a fault in the model then this test generation can take very long time, before the tool decides that it is impossible to cover the model.</p><p>Under the circumstances that the Qtronic tool is improved on varies issues suggested in the thesis, one of the most important issues is to do something about the long debugging time needed, then the next step can be to use model-based testing in a larger evaluation project at BSC Design, Ericsson.</p>
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Pangalos, Georg [Verfasser]. "Model-based Controller Design Methods for Heating Systems / Georg Pangalos." Berlin : Neopubli GmbH, 2016. http://d-nb.info/1103874276/34.
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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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Turner, Roy Marvin. "A schema-based model of adaptive problem solving." Diss., Georgia Institute of Technology, 1989. http://hdl.handle.net/1853/9156.
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Randall, A. "Adaptive model based control for steel rolling systems." Thesis, Coventry University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364162.
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GarcÃa, Z. Yohn E. "Fuzzy logic in process control: A new fuzzy logic controller and an improved fuzzy-internal model controller." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/2529.
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Two fuzzy controllers are presented. A fuzzy controller with intermediate variable designed for cascade control purposes is presented as the FCIV controller. An intermediate variable and a new set of fuzzy logic rules are added to a conventional Fuzzy Logic Controller (FLC) to build the Fuzzy Controller with Intermediate Variable (FCIV). The new controller was tested in the control of a nonlinear chemical process, and its performance was compared to several other controllers. The FCIV shows the best control performance regarding stability and robustness. The new controller also has an acceptable performance when noise is added to the sensor signal. An optimization program has been used to determine the optimum tuning parameters for all controllers to control a chemical process. This program allows obtaining the tuning parameters for a minimum IAE (Integral absolute of the error). The second controller presented uses fuzzy logic to improve the performance of the convention
al internal model controller (IMC). This controller is called FAIMCr (Fuzzy Adaptive Internal Model Controller). Twofuzzy modules plus a filter tuning equation are added to the conventional IMC to achieve the objective. The first fuzzy module, the IMCFAM, determines the process parameters changes. The second fuzzy module, the IMCFF, provides stability to the control system, and a tuning equation is developed for the filter time constant based on the process parameters. The results show the FAIMCr providing a robust response and overcoming stability problems. Adding noise to the sensor signal does not affect the performance of the FAIMC.The contributions presented in this work include:The development of a fuzzy controller with intermediate variable for cascade control purposes. An adaptive model controller which uses fuzzy logic to predict the process parameters changes for the IMC controller. An IMC filter tuning equation to update the filter time constant based in the process paramete
rs values. A variable fuzzy filter for the internal model controller (IMC) useful to provide stability to the control system.
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Colesky, Michael Robert. "SecMVC : a model for secure software design based on the model-view-controller pattern." Thesis, Nelson Mandela Metropolitan University, 2014. http://hdl.handle.net/10948/d1020614.
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Current advances in the software development industry are growing more ubiquitous by the day. This has caused for security, not only in the broader sense, but specifically within the design and overall development of software itself, to become all the more important. An evidently prevalent problem in the domain of software development is that software security is not consistently addressed during design, which undermines core security concerns, and leads to the development of insecure software. This research seeks to address this issue via a model for secure software design, which is based on a software design pattern, namely, the Model-View-Controller (MVC) pattern. The use of a pattern to convey knowledge is not a new notion. However, the ability of software design patterns to convey secure software design is an idea worth investigating. Following identification of secure software design principles and concepts, as well as software design patterns, specifically those relating to the MVC pattern, a model was designed and developed. With the MVC pattern argued as being a suitable foundation for the model, the security conscious MVC (SecMVC) combines secure software design principles and concepts into the MVC pattern. Together herewith, the MVC pattern’s components in the MVC Compound pattern, namely: the Observer pattern, the Strategy pattern, and the Composite pattern, have provided further sub-models for less abstraction and greater detail. These sub-models were developed, as a result of the SecMVC model’s evaluation in the validation for this study, an expert review. Argued in the light of similar research methods, the expert review was chosen – along with a process that included the use of two expert participants to validate the SecMVC model. It was determined through the expert review that the SecMVC model is of sufficient utility, quality, and efficacy to constitute research value. The research methodology process followed was design science, in which the SecMVC model, which includes its related sub-models, serves as the artefact and research output of this study. This research study contributes evidence of the feasibility for integrating knowledge into software design patterns. This includes the SecMVC model itself. In addition, it argues for the use of an expert review, as an evaluative research method for such an artifact.
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Neves, Pedro. "An Implementation Framework for Emotion Based Adaptive Agents." Master's thesis, Department of Informatics, University of Lisbon, 2008. http://hdl.handle.net/10451/14023.
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The work presented in this document is part of the project AutoFocus: Adaptive Self-Improving Multi-Agent Systems' that is being developed at the research unit LabMAg, which objective is the implementation of multi-agent systems based on autonomous entities capable of self-optimized and adaptive behaviors. The notion of autonomic computation, like other notions that also imply pro-active computation, is based on autonomous entities that actively work to achieve their objectives and have the ability to dynamically adjust to changes in their environment, constrained by time and resource limits. In the approach used by the AutoFocus project, that adaptation to change and the regulation of the agent's capabilities, result from the combination of cognitive aspects with emotional based aspects. The agent model defined and used by the AutoFocus project is the Agent Flow Model. The task that corresponded to the work presented in this document was to develop a platform for the Agent Flow Model. It was intended, with this platform, to provide a tool that enables the rapid deployment and monitoring of agents based on this model. The developed work consisted in the analysis and design, oriented to objects, implementation and testing of components of this platform
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Zurcher, James. "Model-based knowledge acquisition using adaptive piecewise linear models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0018/NQ46956.pdf.
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Ting, Fu-Shan, and 丁輔善. "Cerebellar Model Articulation Controller-Based Adaptive Control System Design and Its Applications." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/97701345420318592883.
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碩士<br>元智大學<br>電機工程學系<br>95<br>The purpose of this thesis is to develop the adaptive robust cerebellar model articulation controller (CMAC) system by integrating CMAC with adaptive control and robust control technologies for the control application to uncertain nonlinear systems. According to Lyapunov synthesis approach, the adaptive tuning laws of CMAC can be derived and the system stability can be guaranteed. This thesis introduces the structures of CMAC first. Then, CMAC with optimal learning rate is developed for the single-input single-output (SISO) high voltage power supply (HVPS) system. Moreover, this thesis also proposes the robust CMAC control systems for the uncertain nonlinear MIMO systems. In this design, the developed multi-input multi-output (MIMO) control system is then applied to a nonlinear chaotic system. From the simulation results, the control schemes proposed in this thesis have been shown to achieve satisfactory control performance for the considered nonlinear systems.
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Yang, Chun-Chi, and 楊竣棋. "Output Tracking Control Based on adaptive Takagi-Sugeno Fuzzy Cerebellar Model Articulation Controller." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/79571347952263740496.
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碩士<br>清雲科技大學<br>電機工程研究所<br>94<br>This study introduces an adaptive Takagi-Sugeno fuzzy CMAC (TS-FCMAC) for output tracking control of nonlinear systems. First of all, figure out the learning algorithm of TF-FCMAC, and identify the system, output tracking control rules individually. Its result shows a significantly increase on adaptive learning speed, and the convergence is guaranteed. We use different methods to complete system identification by cases. On output tracking, take nonlinear continuous/ discrete time system for examples first, to illustrate three cases by simulation. Finally, the validity and practicability of the proposed method in this article is verified through experiment of table position control of linear ultrasonic motor.
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Liao, Kuo-Hsiang, and 廖國享. "Output Tracking Control Based on Adaptive Takagi-Sugeno Fuzzy Cerebellar Model Articulation Controller." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/20307240997595033987.
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碩士<br>清雲科技大學<br>電機工程研究所<br>98<br>This paper investigates the problem of sliding mode adaptive TS-CMAC output tracking control for a class of nonlinear system via LMI approach. First of all, figure out the learning algorithm of TS-FCMAC, and output tracking control rules individually. Controller design method is proposed based on TS-CMAC method. In addition, TS-CMAC controller are derived based on the Lyapunov stability theorem, thus tracking error will be asymptotically converge to zero and the stability of the system can be guaranteed. Finally simulation results demonstrate the effective of the proposed control scheme for haotic system with unknown uncertainties.
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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.
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Dehghan, Behrad. "POSITION CONTROL OF A PNEUMATIC SYSTEM USING ADAPTIVE INTELLIGENT METHODS." Thesis, 2012. http://hdl.handle.net/1974/7282.
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Behrad Dehghan: Position Control of a Pneumatic System using Adaptive Intelligent Methods.
M.A.Sc. Thesis, Queen’s University, June, 2012.
A large body of research is devoted to the development of advanced control techniques to
improve the positioning performance of pneumatic systems, which are known to be highly
nonlinear systems. Although model based controllers show good results, the requirement for a
system model makes these methods difficult to implement. So-called intelligent algorithms, such
as neural networks and fuzzy rule based controllers, are attractive since they do not require a
model. The performance of these controllers can be enhanced by adding an adaptive mechanism
to adjust controller parameters in a continuous on-line fashion.
The objective of this thesis was to explore different adaptive intelligent controllers for position
control of a pneumatic system. The application was the x-axis and z-axis of a pneumatic gantry
robot. They were tested independently for their ability to track step and sine wave trajectories.
The rodded x-axis cylinder was an example of a short stroke low friction application. The rodless
z-axis cylinder was an example of a long stroke high friction application.
Five different controllers were tested: 1) PID, 2) Fuzzy, 3) PID+Adaptive Neural Network
Compensator (ANNC), 4) ANNonly and 5) Fuzzy Adaptive PID (FAPID). Results with FAPID
and PID+ANNC showed improvement in tracking performance over PID by 60% for the rodded
and 35% for the rodless cylinder. This level of improvement was expected given the adaptive
nature of the controller. Unfortunately, both required significant effort to setup and tune.
In order to reduce the tuning effort, a second adaptive mechanism was added to FAPID, to adjust
output weights. Results with adaptive PID and modified FAPID (MFAPID) showed further
improvement performance over PID by 87% for the rodded and 70% for the rodless cylinder (in
addition to being easier to tune). To provide a measure of robustness, experiments were
conducted at two supply pressures and three tracking frequencies. The fact that MFAPID was able
to improve performance for both cylinders, is considered further evidence of its robustness.
MFAPID is considered novel for two reasons: 1) fuzzy rule set is reduced in size relative previous
work and 2) addition of an adaptive mechanism for output weights is new.<br>Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2012-06-20 11:09:19.694
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Dai, Reza A. "Improved Lyapunov-based decentralized adaptive controller." Thesis, 1991. http://hdl.handle.net/1957/38027.
Full textAbstract:
An improved robot manipulator decentralized non-linear adaptive
controller that performs well in the presence of disturbances with
unknown parameters and non-linearities is presented in this work.
The proposed decentralized adaptive structure is a modification of
the controller developed by Seraji [13-17] and is characterized by an
auxiliary signal that compensates for the unmodeled dynamics and
improves the tracking performance, by a feedforward component based on
the inverse system to ensure high performance over a wide range and by
a PD feedback component of constant gain to improve the speed of
response of the system. As a result, a very accurate and fast path
tracking is achieved despite the non-linearities.
The scheme requires only the measurement of angular speed and
displacement of each joint, and it does not require any knowledge about
the mathematical model of the manipulator. Due to its decentralized
structure, it can be implemented on parallel processors to speed up the
operation.
The main advantages of the proposed control scheme over similar
controllers are that the control activity is smoother, it is less
sensitive to sampling size and to the time period elapsed when the whole
trajectory is traversed, as verified by simulations of several test
conditions of-two of the joints of the PUMA 560 robot arm.<br>Graduation date: 1991
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Chao, Chang-Chu, and 曹昌竹. "Observer-Based Indirect Adaptive Fuzzy Controller Design." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/55885022058143067869.
Full textAbstract:
碩士<br>大同大學<br>電機工程學系(所)<br>94<br>In this paper, we build a new simplify model and design an observer-based fuzzy
adaptive VSS tracking controller with time delay and external disturbance for our model. The simplify model can avoid complicated Lagrange’s equations, and get quite approximate dynamics for the actual system. The fuzzy approximation with adaptive algorithms are used to learn the dynamics of our model and observer design are introduced into our model. The observer is designed for the error dynamic and Lyapunov type stability is established under certain condition. Then, we apply a Riccati-like equation to solve the approximate error on the observer-based fuzzy adaptive VSS tracking control. The state with time delay effect on tracking error can be bounded via appropriate control force, and the error of state of observer can convergence to a bound at the same time. Finally, simulation result present the controller we design can control the model we propose to desired trajectory.
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Lien, Te Chi, and 連得祺. "Observer Based Adaptive T-S Fuzzy Controller Design." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/86259592133043911001.
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CHEN, WU-ZHANG, and 陳武璋. "Discrete-time model reference adaptive controller design for robot." Thesis, 1988. http://ndltd.ncl.edu.tw/handle/79507376215607905477.
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TANG, LI, and 湯蠡. "A robust adaptive model reference dead-beat controller design." Thesis, 1989. http://ndltd.ncl.edu.tw/handle/50349995153857918775.
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Lan, Shih-Wei, and 藍世緯. "Adaptive Echo Cancellation Using Deep Cerebellar Model Articulation Controller." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/80404587248894079955.
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碩士<br>元智大學<br>電機工程學系<br>105<br>ABSTRACT
It had the very big breakthrough in the machine learning development in recent years. No matter the Watson which developed by IBM or Google’s AlphaGo, they both are based on depth of neural networks. And the cerebellar model articulation controller (CMAC) has been widely used in various applications of neural networks, such as: inverted pendulum, nonlinear channel equalization and robot control. It has great generalization and learning fast characteristics enough to deal with the basic applications of neural network. But if the complexity of the higher non-linear task, there will be learning learning-difficulty situation. And the CMAC was originally designed for simple control applications, so high-dimensional input processing, such as: speech recognition which can’t be used by normal CMAC, so it need to improved.
This paper proposed the deep cerebellar model articulation controller(DCMAC) for echo cancellation and the MIMO-DCMAC with the Softmax function for speech recognition. We stack the conventional single-layered CMAC models into multiple layers to form a DCMAC model, and re-modify the back propagation algorithm to get the update of DCMAC’s parameter. Due to the deep structure, DCMAC can have a better generalization error than the normal CMAC. The experimental results also show that DCMAC can build model more effectively than CMAC in signal processing.
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Zeng, Hong Hang, and 曾虹翰. "Adaptive fuzzy controller based on variable universe of discourse." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/87764537645384787540.
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碩士<br>中州技術學院<br>工程技術研究所<br>96<br>Fuzzy logic controller with variable region is a kind of fine self- reacting controller. With a proper expandable and shrinkable factor, its region can change accordingly when input error changes. Thus, its fuzzy rules increase greatly and it becomes more and more sensitive to error when output reaches the expectation. On both dynamic performance indices and stable performance indices, it is found that fuzzy logic controller with variable region is a great improvement upon common fuzzy logic controller, especially on precision of controlling. However, the improvement has close relations with expandable and shrinkable factor. Whether the controller is fine or not, even steady or not, depends greatly on whether the expandable and shrinkable factor is appropriate or not.
In this paper, a kind of proportional expandable and shrinkable factor is defined to replace conventional exponential factors and integral factors at the antecedent part and consequent part of inference. And a class of simple fuzzy control algorithm is presented based on variable universe of discourse. The existing algorithm’s advantage of high precision is inherited by the controller consisted of the new algorithm. And comparing with existing algorithm’s, the new algorithm’s computation is significantly reduced. So a useful way to problems in practical applications is obtained by using the simper fuzzy control algorithm.
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Chang, Fang-Wei, and 張芳瑋. "Design of Fuzzy Model-Based Controller with Sliding Mode Supervisory Controller." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/50467667342657201159.
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碩士<br>大同大學<br>電機工程研究所<br>88<br>This thesis presents a fuzzy on-line identification algorithm and a fuzzy model-based controller for a nonlinear system.
Firstly, we represent the nonlinear system by the Takagi-Sugeno fuzzy model and then use the identification algorithm to find the optimal consequent parameter of the fuzzy model. A convergence analysis is carried out too.
Then in the controller design procedure, we construct a global fuzzy logic controller by blending all local feedback controllers with a supervisory based on the sliding mode control theory. Unlike the commonly used parallel-distributed compensation (PDC) technique, we can design a global stabilizing fuzzy controller without finding a common Lyapunov function for all local control systems, and can obtain a good tracking performance.
Some simulation results are given to show the effectiveness and feasibility of the proposed fuzzy on-line identification algorithm and the fuzzy controller design method.
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Wang, Yun-Ching, and 王雲慶. "A Genetic Fuzzy Logic Controller-based Adaptive Ramp Metering System." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/74429443654188589565.
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碩士<br>逢甲大學<br>交通工程與管理所<br>93<br>Ramp metering might be the most popular and effective strategy for freeway traffic control. Thus, the researches and practices regarding ramp metering have been conducted and implemented over thirty years. Especially, due to the break-through development of fuzzy logic controller (FLC), many researches explore the applicability and performance of FLC ramp metering. However, most of these studies developed the FLC ramp-metering strategy by subjectively setting the membership functions of linguistic variables and selecting the inference rules. As a result, the performance and applicability of these models is negatively affected. Thus, hundreds of studies employed genetic algorithms to design an adaptive fuzzy logic controller (FLC) by learning from examples, namely genetic fuzzy logic controller (GFLRC). GFLC models can not only avoid the subjective settings of membership functions and inference rules, but also enhance the performance of control. Based on that, this paper attempts to develop and validate a genetic fuzzy logic ramp-metering controller (GFLRC).
The present paper proposes two GFLRC strategies: independent and integrated. The independent GFLRC strategy uses mainline average traveling speed and on-ramp queuing length as two state variables, metering rate as control variable. For considering the effects of upstream metering traffic, the integrated GFLRC strategy uses an additional state variable, the metering rate of upstream ramp, based on the independent strategy. The performance of the models is measured by the total travel time of all users during the control period under given traffic flow rates. Besides, the techniques of macroscopic traffic simulation and fluid approximation are adopted to simulate and compute the performances.
For investigating the applicability and control performance of proposed models, studies on an exemplified example with four interchanges and field case with six interchanges (the northern network of the Taiwan first freeway) are conducted. Five-minute flow rates are simulated for the exemplified example or collected for the field case, respectively. In corresponding to the interval of traffic flow data, the decision horizon of the models is also set as five minutes. The performance of without ramp-metering, FLRC model and optimal single ramp metering strategies are also conducted and analyzed for the comparisons. The results of case study on exemplified example shows that independent and integrated GFLRC can curtail the total travel time by 6.93% and 7.80%, respectively; while the proposed optimal single ramp metering strategy can reduce the total travel time by 4.81% and 7.22%, the FLRC model can reduce the total travel time only by 0.45% at most, comparing with the strategy of without ramp-metering. The field study also concludes the similar results. We also find that the GFLRC models perform better in heavy traffic than in light traffic. At last, we also analyze the unfairness of users caused by implementing these ramp-metering strategies. The results shows FLRC model is rather fairness, while the independent-GFLRC is the most unfairness.
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PENG, CHUN-HSIUNG, and 彭俊雄. "Adaptive Controller Design for TORA based on Function Approximation Technique." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/85291601189546878713.
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碩士<br>國立臺灣科技大學<br>機械工程系<br>91<br>An adaptive multiple-surface sliding controller is proposed in this thesis to a TORA (Translational Oscillations by a Rotational Actuator) system. The function approximation technique is introduced in the controller design to cope with the problem of time-varying uncertainties. Simulation results show that the proposed scheme gives good performance even with external disturbances.
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JIANG, JHENG NAN, and 姜政男. "An Observer Based Adaptive Fuzzy Controller with Modulated Membership Function." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/4azmau.
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碩士<br>國立臺北科技大學<br>電機工程系研究所<br>96<br>An observer based adaptive modulated membership fuzzy controller (OAMMFC) for uncertain nonlinear systems is proposed in this thesis. By including micro-genetic algorithm (MGA), the width of membership functions are modulated based on fuzzy orthogonal condition. The proposed fuzzy controller can online adjust not only weighting factors in the consequence part but also the membership functions in the antecedent part. Computation time is shortened to improve controller performance. Moreover, we use fitness function for online tuning the parameter vector of the fuzzy controller. The fitness function is based on stability criterion established by Lyapunov method. For meeting stability condition, a supervisory controller is implemented in a closed-loop nonlinear system to smoothen controller operation. Finally, a Multi-inputs multi-outputs controller is developed and applied on an Automatic Guided Vehicle dynamic model. Favorable results are obtained when the proposed method is applied on a path tracking experiment.
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Gao, Fei. "Controller in core an adaptive microarchitectural model for system-level optimization /." 2006. http://www.lib.ncsu.edu/theses/available/etd-07162006-223806/unrestricted/etd.pdf.
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Chiu-Hsiung, Chen. "Adaptive Robust Cerebellar Model Articulation Controller Design for Uncertain Nonlinear Systems." 2007. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-2901200723001300.
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LIN, YANG, and 林昜. "Adaptive Fuzzy Cerebellar Model Articulation Controller Tracking Control for Mobile Robot." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/wgccu2.
Full textAbstract:
碩士<br>國立宜蘭大學<br>電機工程學系碩士班<br>107<br>This paper proposes an adaptive fuzzy cerebellar mode controller design scheme, which is applied to three-wheel omnidirectional mobile robots to perform nonlinear trajectory tracking control tasks. In this study, the adaptive fuzzy cerebellar mode controller (AFCMAC) is a combination of cerebellar mode controller and fuzzy logic and robust controller. As long as the appropriate adaptive law and control law are designed, the instability of the nonlinear system can be adjusted. Precision with system control. By Lyapunov stability analysis, it can be proved that the closed-loop system is bounded and the tracking error can gradually converge to a controllable endgame, which can solve the problem of unstable tracking control of multi-dimensional nonlinear systems. At the beginning of the study, the kinematics model was derived based on the geometric architecture of the three-wheeled omnidirectional mobile robot. Then, the fuzzy logic and cerebellar mode calculus are integrated to construct a multi-variable fuzzy cerebellar mode unit (FCMAC) which can reduce the input dimension and simplify the system structure. Secondly, the appropriate adaptive law and control law are designed and combined with the output feedback with sliding surface characteristics to develop a multi-dimensional fuzzy cerebellar mode control system to adjust the uncertainty of the nonlinear system. Finally, the adaptive fuzzy cerebellar mode controller is integrated to simulate the trajectory tracking, and the AFCMAC controller is implemented on the Arduino control version to complete a set of Arduino-driven three-wheel omni-directional AFCMAC motion control system. Finally, the correctness and feasibility of the adaptive fuzzy cerebellar mode controller proposed in this paper is verified by the trajectory tracking experiment results of Arduino three-wheel omnidirectional robot.