Dissertations / Theses on the topic 'Nonlinear Controller'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Nonlinear Controller.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
Murray, Nicholas Durante. "Nonlinear PID controller." Thesis, This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-03242009-040653/.
Full textDiao, Lili. "Nonlinear bounded controller design." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ59374.pdf.
Full textSimminger, Jerome C. "A constrained multivariable nonlinear predictive controller." Thesis, Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/10152.
Full textFish, Garron A. "Robust nonlinear controller based on set propagation." Master's thesis, University of Cape Town, 2003. http://hdl.handle.net/11427/5222.
Full textA novel control method, based on interval analysis, that optimises the control surface (or u-surface) for sampled systems with output disturbances is demonstrated on a driven pendulum with actuator constraints. The fitness function to be maximized is the probability of each state of the system being controlled to the setpoint without being perturbed to regions that are more iterations away from the setpoint. The u-surface is designed by finding all the states that could go to the setpoint in an interval and optimising these states. This process is repeated (backwards in time) by optimising states that go to the previously optimised states until no more states that have not been optimised are found. The proposed control method has been applied to the problem of swinging up a driven pendulum from rest to the inverted position with constraints on the torque of the motor. This method is computationally intensive and time constraints limit its current application to systems of low order.
Ronco, Eric. "Incremental polynomial controller networks two self-organising non-linear controllers /." Thesis, Connect to electronic version, 1997. http://hdl.handle.net/1905/181.
Full textSkaf, Zakwan. "Reliable controller design for a class of nonlinear systems." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/reliable-controller-design-for-a-class-of-nonlinear-systems(a6215fa6-271a-41da-b526-a072cbab74c4).html.
Full textPanjapornpon, Chanin Soroush Masoud. "Model-based controller design for general nonlinear processes /." Philadelphia, Pa. : Drexel University, 2005. http://dspace.library.drexel.edu/handle/1860/611.
Full textUstunturk, Ahmet. "Digital Controller Design For Sampled-data Nonlinear Systems." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614267/index.pdf.
Full textTan, Xiaodong. "High dimentional neural fuzzy controller for nonlinear systems." Mémoire, Université de Sherbrooke, 2007. http://savoirs.usherbrooke.ca/handle/11143/1470.
Full textAldair, Abdulshaheed Abdulhammed. "Neurofuzzy controller based full vehicle nonlinear active suspension systems." Thesis, University of Sussex, 2012. http://sro.sussex.ac.uk/id/eprint/38502/.
Full textFiorentini, Lisa. "Nonlinear Adaptive Controller Design For Air-breathing Hypersonic Vehicles." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1274986563.
Full textSongchaikul, Metin. "Nonlinear control system design using a gain scheduling technique." Ohio : Ohio University, 1993. http://www.ohiolink.edu/etd/view.cgi?ohiou1175885067.
Full textHo, Chin Keung Sammy. "Improved model and controller structure selection using genetic algorithms." Thesis, University of Sunderland, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263474.
Full textDoruk, Resat Ozgur. "Nonlinear Controller Designs For A Reaction Wheel Actuated Observatory Satellite." Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609831/index.pdf.
Full textMakovec, Kristin Lynne. "A Nonlinear Magnetic Controller for Three-Axis Stability of Nanosatellites." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/34131.
Full textMaster of Science
Leising, Sophie. "Nonlinear controller synthesis for complex chemical and biochemical reaction systems." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-050205-152657/.
Full textKeywords: model predictive control; discrete-time model; continuous-time model; nonlinear systems; Lyapunov design. Includes bibliographical references (p. 99-102).
Chopra, Shubham. "Evolved Design of a Nonlinear Proportional Integral Derivative (NPID) Controller." PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/512.
Full textSheng, Lan. "Fuzzy logic controller synthesis for electro-mechanical systems with nonlinear friction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0001/MQ35526.pdf.
Full textLipp, Andreas Martin. "Modeling and nonlinear controller development for the apache helicopter using GTNONCON." Thesis, Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/12085.
Full textHeiges, Michael W. "A helicopter flight path controller design via a nonlinear transformation technique." Diss., Georgia Institute of Technology, 1989. http://hdl.handle.net/1853/12482.
Full textAntritter, Felix [Verfasser]. "Tracking Controller Design for Nonlinear Dynamics using Differential Parameterizations / Felix Antritter." Aachen : Shaker, 2007. http://d-nb.info/1166511219/34.
Full textRobles, Ruiz Ruben. "Contributions to nonlinear system modelling and controller synthesis via convex structures." Doctoral thesis, Universitat Politècnica de València, 2018. http://hdl.handle.net/10251/100848.
Full textThis thesis discusses different modelling methodologies to eke out best performance/stability results than conventional sector-nonlinearity Takagi-Sugeno (also known as quasi-LPV) systems modelling techniques are able to yield. Indeed, even if LMIs can prove various performance and stability bounds (decay rate, $\mathcal H_\infty$, etc.) for polytopic systems, it is well known that the proven performance depends on the chosen model and, given a nonlinear dynamic systems, the polytopic embeddings available for it are not unique. Thus, explorations on how to obtain the model which is less deletereous for performance are presented. As a last contribution, extending the polytopic Takagi-Sugeno setup to a gain-scheduled quasi-convex difference inclusion framework allows to improve the results over the polytopic models. Indeed, the non-scheduled convex difference inclusion framework was proposed by a research team in University of Seville (Fiacchini, Alamo, Camacho) as a generalised modelling methodology which included the polytopic one; this thesis poses a further generalised gain-scheduled version of some of these results.
Aquesta tesi discuteix diferents metodologies de modelatge per extreure millors prestacions o resultats d'estabilitat que aquelles que el modelatge convencional basat en sector no-lineal de sistemes Takagi-Sugeno (també anomenats quasi-LPV) és capaç de produir. En efecte, fins i tot si les LMIs poden provar diferents cotes de prestacions o marges d'estabilitat (taxa de decaïment, $\mathcal H_\infty$, etc.) per a sistemes politòpics, és ben conegut que les prestacions provades depenen del model triat i, donat un sistema no-lineal, el dit model politòpic no és únic. Per tant, es presenten exploracions cap a com obtenir el model que és menys perjudicial per a la mesura de prestacions triada. Com una darrera contribució, millors resultats són obtinguts mitjançant l'extensió del modelatge politòpic Takagi-Sugeno a un marc d'inclusions en diferències quasi-convexes amb planificació de guany. En efecte, una versió sense planificació de guany va ser proposada per un equip d'investigadors de la Universitat de Sevilla (Fiaccini, Álamo, Camacho) per a generalitzar el modelatge politòpic, i aquesta tesi proposa una versió més general d'alguns d'aquests resultats que incorpora planificació de guany.
Robles Ruiz, R. (2018). Contributions to nonlinear system modelling and controller synthesis via convex structures [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/100848
TESIS
Thienel, Julie K. "Nonlinear observer/controller designs for spacecraft attitude control systems with uncalibrated gyros." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1401.
Full textThesis research directed by: Aerospace Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
范原章. "Nonlinear vehicle longitudinal controller design." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/33837236218287696986.
Full textSutton, Gordon J. "Nonlinear model-predictive controller design." Phd thesis, 1999. http://hdl.handle.net/1885/147965.
Full textYang, Shang-Feng, and 楊尚峰. "Nonlinear PID Controller Design and Tuning." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/99000057983348827762.
Full text國立臺灣大學
化學工程學研究所
88
Nonlinear control system is used to control nonlinear process or to achieve nonlinear control objective. It is very common that instrument companies or papers bring up some nonlinear control structures, but, for the reason that the tuning method is not clear, these nonlinear controllers do not be used practically. This thesis is trying to answer the questions about how to tune these nonlinear controllers which are common in papers or instrument companies'' product manuals. In this thesis, we discuss the following five type of nonlinear controllers: 1. 3-piece PI liquid level controller 2. error to n power PI liquid level controller 3. sampling PI controller 4. on/off PI controller 5. blending PID controller For each type of controller, we try to analyze it with mathematical analysis and with numerical simulation by MATLAB, and are intended to figure out some practical parameter tuning formulae.
Hsin, Mei-Ying, and 辛美瑩. "Nonlinear Controller Design from Time Series." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/99252422321738494278.
Full text國立海洋大學
電機工程學系
87
In this paper, the parameter estimation and artificial neural network techniques are used in identification and control of nonlinear systems. It includes the computation of equilibrium points and linearized models from chaotic time series, which can then be used for the purpose of controlling chaos. In addition, using real-time learning rules, nonlinear systems can be controlled adaptively.
Lin, Ji Tai, and 林積泰. "Fuzzy controller design of nonlinear systems." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/69693670895492143326.
Full textLeong, Yoke Peng. "Optimal Controller Synthesis for Nonlinear Systems." Thesis, 2018. https://thesis.library.caltech.edu/10610/1/leong_yokepeng_2017.pdf.
Full textOptimal controller synthesis is a challenging problem to solve. However, in many applications such as robotics, nonlinearity is unavoidable. Apart from optimality, correctness of the system behaviors with respect to system specifications such as stability and obstacle avoidance is vital for engineering applications. Many existing techniques consider either the optimality or the correctness of system behavior. Rarely, a tool exists that considers both. Furthermore, most existing optimal controller synthesis techniques are not scalable because they either require ad-hoc design or they suffer from the curse of dimensionality.
This thesis aims to close these gaps by proposing optimal controller synthesis techniques for two classes of nonlinear systems: linearly solvable nonlinear systems and hybrid nonlinear systems. Linearly solvable systems have associated Hamilton- Jacobi-Bellman (HJB) equations that can be transformed from the original nonlinear partial differential equation (PDE) into a linear PDE through a logarithmic transformation. The first part of this thesis presets two methods to synthesize optimal controller for linearly solvable nonlinear systems. The first technique uses a hierarchy of sums-of-square programs to compute a sequence of suboptimal controllers that have non-increasing suboptimality for first exit and finite horizon problems. This technique is the first systematic approach to provide stability and suboptimal performance guarantees for stochastic nonlinear systems in one framework. The second technique uses the low rank tensor decomposition framework to solve the linear HJB equation for first exit, finite horizon, and infinite horizon problems. This technique scale linearly with dimensions, alleviating the curse of dimensionality and enabling us to solve the linear HJB equation for a quadcopter model that is a twelve-dimensional system on a personal laptop. A new algorithm is proposed for a key step in the controller synthesis algorithm to solve the ill-conditioning issue that arises in the original algorithm. A MATLAB toolbox that implements the algorithms is developed, and the performance of these algorithms is illustrated by a few engineering examples.
Apart from stability, in many applications, more complex specifications such as obstacle avoidance, reachability, and surveillance are required. The second part of the thesis describes methods to synthesize optimal controllers for hybrid nonlinear systems with quantitative objectives (i.e., minimizing cost) and qualitative objectives (i.e., satisfying specifications). This thesis focuses on two types of qualitative objectives, regular objectives, and ω-regular objectives. Regular objectives capture bounded time behavior such as reachability, and ω-regular objectives capture long term behavior such as surveillance. For both types of objectives, an abstraction-refinement procedure that preserves the cost is developed. A two-player game is solved on the product of the abstract system and the given objectives to synthesize the suboptimal controller for the hybrid nonlinear system. By refining the abstract system, the algorithms are guaranteed to converge to the optimal cost and return the optimal controller if the original systems are robust with respect to the initial states and the optimal controller inputs. The proposed technique is the first abstraction-refinement based technique to combine both quantitative and qualitative objectives into one framework. A Python implementation of the algorithms are developed, and a few engineering examples are presented to illustrate the performance of these algorithms.
Yi-Jen, Mon. "Intelligent Controller Design for Some Nonlinear Systems." 2003. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-0112200611291028.
Full textLiu, Yu-Cheng, and 劉聿程. "Nonlinear Controller Design for Magnetic Levitation Systems." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/m2ck82.
Full text國立東華大學
電機工程學系
95
This thesis is to develop the robust position tracking controllers for magnetic levitation systems (MLS). First, the input-state feedback linearization technique is used to transform the dynamic model of the magnetic levitation system to a linear model. Then, a robust controller based on the linear model is designed. In the proposed controller, the adaptive sliding mode control is used to improve the chattering behavior. In order to solve the unmatched perturbations, an adaptive integral sliding mode controller with method is further proposed. Finally, simulation and experimental results are illustrated to validate the proposed control method for practical tracking applications.
To, Minh Hoang, and 蘇明煌. "Intelligent Tracking Controller for Nonlinear Dynamic System." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/73cux3.
Full text中國文化大學
機械工程學系數位機電碩士班
101
In this thesis, an adaptive fuzzy PID sliding mode control (AFPIDSMC) scheme is proposed for a certain class of unknown nonlinear dynamical system. The proposed controller comprises of two types of controllers. One is fuzzy PID sliding-mode controller (FPIDSMC), which gives robust stability for system in the presence of parameter variations, uncertainties, and disturbances, and the other one is an adaptive tuner. The FPIDSMC acts as the main tracking controller, which is designed via a fuzzy system to mimic the merits of a PID sliding-mode controller (PIDSMC). While the adaptive tuner, which is derived in the sense of Lyapunov stability theorem, is utilized to adjust the parameter on-line for further assuring robust and optimal performance. In the proposed FPIDSMC, the fuzzy rule base is compact and only one parameter needs to be adjusted. To verify its effectiveness and extend its application, the proposed AFPIDSMC is applied to path tracking for a control robots and to balance control for a two-wheel robot. In the first application, just only simulation results which are provided by MATLAB and whose advantages are presented in comparison with conventional AFSMC under the same environment. In the other application, the results are provided not only in simulation, but also in real world. The simulation results are also provided by MATLAB and are compared with conventional AFSMC while the experimental results are provided by using the E-NUVO platform.
Lin, yao-wen, and 林耀文. "Nonlinear Controller Design of A Flexible Beam." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/00410649557197120924.
Full text國立海洋大學
機械與輪機工程學系
86
Due to their fast response and low energy consumption, light-weight structures are widely employed in certain areas , such as aeronautic and aerospace industries. However, since the stiffness and damping coefficient are lower for these structures, passive and/or active vibration suppression methods need to be introduced to reduce the vibrations when such structures undergo movements. A flexible beam rotating around one end and driven by a DC servo-motor was used to study the effects of reducing the beam vibration by using the same DC motor that rotates the beam. A strain gague was cemented to the root of the cantilevered beam as sensor to detect the first vibration mode. Several design methods , which include variable structure control and feedback linearization control , were used to develop control algorithms . Finally , different control algorithms were implemented in a pc-based control system to study the feasibility of the proposed control scheme and to compare the performance of different control algorithms through experiment.
Chen, Yun-Yo, and 陳俞佑. "Nonlinear controller design for the inverted pendulum." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/42094654684893519611.
Full textChen, Yu-You, and 陳俞佑. "Nonlinear controller design for the inverted pendulum." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/49408539363769065445.
Full textTsai, Meng-In, and 蔡孟吟. "The Controller Synthesis for Nonlinear Grey System." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/qv7utb.
Full text國立成功大學
造船及船舶機械工程學系
90
A nonlinear system containing disturbances and uncertainties are concerned in thus study. The parameter variations in the nonlinear terms of the system and treated as “ gray numbers”, which are parts unknown. The nonlinear system will be linearlized first by means of the so-called “the feedback linearization” technique and then be formulated into the so called ”H∞ standard problem”, which can be controlled by an H∞-optimal controller. The system matrices in the linearized system are usually gray ones because of the presence of plant parameter variations and uncertainties. Due to the properties of an H∞-control law, the proposed gray H∞ controller is able to surpass the H∞-norm of the closed loop transfer function between the exogenous input (e.g., disturbances and uncertainties) and the controller outputs (e.g., tracking errors and control energies). Thus, the proposed controller is robust to plant uncertainties and disturbances. Furthermore, a nonlinear gray-H∞ controller is proposed in this reach by simplify recover the nonlinear form of the controller from the feedback linearization process. To ensure the closed – loop stability, a MIMO circle criterion is applied to analyze the allowable sectors of the nonlinear uncertainties. Finally, a nonlinear robot manipulator, which has uncertain loads, is used to attest the feasibility of the proposed controller.
Mon, Yi-Jen, and 蒙以正. "Intelligent Controller Design for Some Nonlinear Systems." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/32336146236986399336.
Full text元智大學
電機工程學系
91
This dissertation focuses on the intelligent control system design for some nonlinear systems based on the fuzzy logic control, fuzzy-parallel-distributed-compensation (fuzzy-PDC) control, fuzzy sliding-mode control and fuzzy neural-network control. For the fuzzy logic control system design, the missile command to line-of-sight guidance problem is presented, and the fuzzy-logic control is explored to achieve satisfactory guidance performance. For fuzzy-PDC control, the fuzzy-PDC scheme is illustrated for a robotic system to perform as a nonlinear-regulator. Alternatively, by introducing a new fuzzy sliding surface based on the PDC concept, an effective fuzzy-sliding-mode controller is developed and applied to an ecological system control. For fuzzy sliding-mode control, a design method of adaptive fuzzy sliding-mode control is proposed, and applied to a robotic system control. Moreover, a design method of hierarchical fuzzy sliding-mode decoupling control is proposed to deal with single-input multi-output control systems. By applying this design method, the inverted pendulum systems are demonstrated to achieve favorable control performance with guaranteed stability. For fuzzy-neural-network control, the combination of adaptive-network-based fuzzy inference system and fuzzy-Gaussian-neural-network is used to design a fuzzy-neural-network controller for mobile robot control. Meanwhile, a recurrent fuzzy neural network control system is introduced for torpedo guidance system. Finally, a design method of recurrent fuzzy neural network control system for multi-input multi-output nonlinear systems is developed and applied to robotic system and ecological system controls. From the simulation results, the intelligent control techniques proposed in this dissertation have been shown to achieve satisfactory control performance for the considered nonlinear systems.
Lee, Ming-Wei, and 李銘偉. "Identification and Controller Design for Nonlinear Processes." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/56403346273132038961.
Full text國立臺灣大學
化學工程學研究所
90
One of the basic principles of process control is identification especially in nonlinear processes. Many chemical processes can be approximated by models that has a static nonlinear gain and a linear subsystem in series. Two types of such models for nonlinear processes fall into this category. i.e.: Wiener and Hammerstein models. Each of these two types of model mentioned comprises a nonlinear static element preceded/followed by a linear dynamic one, and, is usually given in a block-oriented form. Due to the nonlinearity and the differences in model structures, the estimation of the parameters vary with structure of the model being identified. Therefore, in the beginning of identification, the model structure has to be determined. In this work, a strategy based on the results of consecutive relay feedback experiments is proposed to select a model among those block oriented ones aforementioned. After the model structure has been determined, estimation of parameters can then proceed. The identified procedures consists of two stages as follows: 1. In the first stage, the static nonlinearity is identified, using relay feedback test. An objective function which arm at penalizing the asymmetry in the system output is formulated. Standard optimization procedures are then used to minimize this objective function by adjusting the parameters in a given function or polynomial to represent the nonlinear gain in the model. 2. In the second stage, a linear model of FOPDT or of SOPDT is identified to represent the dynamics of the linear subsystem. The advantage of using these models to describe nonlinear process for controller design is that the process can be easily adapted to use existing methods for linear system. This is possible because the static nonlinearity in the process can be cancelled by inserting the nonlinear inverse of the static nonlinearity in the loop. Therefore, the conventional linear controller synthesis methods can be used.
Liao, Jian-Hao, and 廖建豪. "Reduced SA Fuzzy-neural Controller for Nonlinear Systems." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/27418802411099300466.
Full text國立臺灣師範大學
工業教育學系
97
In this thesis, a reduced simulated annealing algorithm used to tune the parameters of fuzzy neural networks is proposed for function approximation and adaptive control of nonlinear systems. For the design of adaptive controller, the reduced simulated annealing algorithm does not require the procedure of off-line learning and the complicated mathematical form. Compared with traditional adaptive controllers, computation loading can be effectively alleviated. In adaptive control procedure for nonlinear systems, the weights of the fuzzy neural controller are online adjusted by the reduced simulated annealing algorithm in order to generate the appropriate control input. For the purpose of on-line evaluating the stability of the closed-loop systems, an energy cost function derived from Lyapunov function is involved in the reduced simulated annealing algorithm. In addition, the system states may go into the unsafe region if the reduced simulated annealing algorithm can not instantaneously generate the appropriate weights. In order to guarantee the stability of the closed-loop nonlinear system, a supervisory controller is incorporated into the fuzzy neural controller. Finally, some computer simulation examples and a servo motor experiment are provided to demonstrate the feasibility and effectiveness of the proposed method.
Hsu, Chia-Shin, and 許嘉訓. "Nonlinear IMC Controller Design - Application of Extended Linearization." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/4tyyhg.
Full text國立成功大學
化學工程學系碩博士班
92
Nonlinear processes can be classified into two types. The first type of processes consists of nonlinear static and dynamic parts, whereas the second type consists of a linear dynamic part and a nonlinear static part. For the first type of processes, we can obtain a parameterized transfer function via extended linearization, where the parameter represents the steady-state input or output of the process. We then design a nonlinear IMC (internal model control) controller based on the IMC theory. To realize the parameterized IMC controller, we propose to convert the transfer function representation of the controller into a state-space representation by means of the residual matrix approach. Simulation results with high-order level-tank systems reveal that the proposed nonlinear IMC controller outperforms the corresponding linear IMC controller for set-point changes. We have the third-order level-tank system model by process identification. The proposed IMC design can also be incorporated with a parameterized model provided by process identification. This is verified on a third-order level-tank process. For the second type of processes, we assume that identification as a Hammerstein model is appropriate and provide an inverse function to cancel out the static nonlinearity of the model. A linear IMC controller can then be used to control the resulting system. In this thesis, three approaches are investigated to arrive at an inverse function of the static nonlinearity. Simulation with a continuous stirred tank reactor system reveals that the proposed method works well for set point changes.
Chen, Po-Cheng, and 陳柏成. "GA-Based Intelligent Controller Design for Nonlinear Systems." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/6s9u7f.
Full text國立中央大學
土木工程研究所
97
Abstract In this thesis, we investigate and discuss some intelligent controller designs for nonlinear systems, proposing several strategies. Generally, the biggest difficulty encountered when designing an adaptive controller which is actually capable of rapidly and robustly controlling nonlinear systems is the selection of the control rules and of the most appropriate initial values for the parameter vector. The first step is to reduce the stability analysis of the nonlinear system into linear matrix inequality (LMI) problem solutions, for which we then propose an adaptive control strategy incorporated into an tracking control scheme. The control rules and the consequent parameters are decided via the use of genetic algorithms (GA). Then, based on the Lyapunov’s stability criterion, we utilize the rules and parameters that guarantee the best tracking performance throughout the entire system states. After this we use the most singular perturbation scheme to decouple a non-square multi-variable system into several reduced-order isolated square multi-variable subsystems for multi-variable nonlinear system stability analysis. The initial values of the consequent parameter vector are decided via genetic algorithms. The boundary-layer function is introduced into these modified updating laws to cover parameter errors and modeling errors, and to guarantee that the state errors converge into a specified error bound. Finally, we look at the type of robust problem often met with in designing a neural network controller for complex and nonlinear systems, wherein we use radial basis function networks to approximate the control plant for nonlinear systems. The initial values of the consequent weight vector are decided via GA after which a modified adaptive law is derived based on Lyapunov’s stability criterion to simultaneously stabilize and control the nonlinear systems. Focusing on the fore-mentioned general topics, we mention the following control strategies: (1) the GA-Based Adaptive Fuzzy Sliding Mode Controller (GA-Based AFSMC); (2) the GA-Based Adaptive Fuzzy Sliding Mode Controller (GA-Based AFSMC); (3) the GA-Based Adaptive Neural Network Controller (GA-Based ANNC). The design procedure for the proposed intelligent control systems (with some modified methods) is illustrated by computer simulations the results of which are utilized to demonstrate the control methodology, as well as the robustness and efficiency of the constructed controllers.
Liu, Sung-Chieh, and 劉松傑. "Nonlinear Controller Design for Induction Motor Servo System." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/44655114057767592247.
Full text蔡文祥. "Sliding mode controller design with nonlinear switching surfaces." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/72432676460181496556.
Full textXie, Yao Nan, and 謝耀南. "Adaptive dynamical sliding mode controller of nonlinear systems." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/60753744518489073057.
Full textSHI, CHUN-YU, and 施純育. "Robust controller design for discrete nonlinear perturbed systems." Thesis, 1988. http://ndltd.ncl.edu.tw/handle/92646441329725100571.
Full textEconomou, Constantin George. "An Operator Theory Approach to Nonlinear Controller Design." Thesis, 1986. https://thesis.library.caltech.edu/1009/1/Economou_cg_1986.pdf.
Full textStrong similarities between control theory and the theory on the solution of operator equations have been observed and basic results in control theory have been derived from operator theory arguments. The purpose of this work is to investigate the theory of controller design as an application of basic operator theory principles and to establish a unified framework in which control theory can benefit from a "rich" operator theory. The major impact is anticipated in nonlinear feedback control theory: controller design can be formulated as selection of an iterative algorithm to solve a nonlinear operator equation corresponding to the control objective. As an example, controllers induced by the method of successive substitution and the Newton method are introduced and the corresponding analysis and synthesis issues are studied. Applied to linear systems, the proposed concepts have a straightforward interpretation in terms of familiar notions in linear controller design theory. Applications are presented and extensions of the current results are suggested to conclude the thesis.
Chen, Ming-Chao, and 陳明照. "Adaptive PID Controller Design for Nonlinear Chaotic Systems." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/01585242901577524200.
Full text元智大學
電機工程學系
96
The subject of this thesis is to design a robust adaptive Proportional-Integral-Derivative (PID) controller to deal with an uncertainty chaotic system tracking control. In this thesis, we first use sliding mode control method to control a multi-input multi-output system. Although the sliding mode control can achieve the final control results; it is far from ideal. Thus, we design an adaptive robust PID controller to mimic an ideal controller. The control gains KP, KI, KD of PID controller are adjustable parameters which can be updated online with an adequate adaptation mechanism to optimize the previously designed sliding condition. A nearly ideal controller surely can not eliminate an approximate error, thus a supervisory controller is necessarily added as a system compensated controller to reduce the approximate error. Finally, we applied the proposed control technique to a Chua’s chaotic circuit system. From the simulation results show the satisfactory control performance.
Yang, Chih-Yao, and 楊智堯. "CMAC-Based Robust Controller for Uncertain Nonlinear Systems." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/30549109576624062399.
Full text元智大學
電機工程學系
96
The purpose of this thesis is to develop an adaptive robust cerebellar model articulation controller (CMAC) system by integrating CMAC with adaptive control and robust control technologies for the control application to nonlinear systems. According to Lyapunov synthesis approach, the adaptive tuning laws of CMAC can be derived and the system stability can be guaranteed. The control system can be applied to uncertain nonlinear systems. This thesis designs the CMAC first; then, CMAC is integrated with three kinds of compensation controllers. For applications, we consider the single-input single-output (SISO) system first, and simulate in wing rock control system. Moreover, this thesis also proposes the robust CMAC control system using sliding-mode technology for the uncertain nonlinear multi-input multi-output (MIMO) system and its simulation for satellite attitude control is demonstrated. From the simulation results, the control schemes proposed in this thesis have been shown to achieve satisfactory control performance for the considered nonlinear systems.
Chang, Shih-tse, and 張世澤. "Identification of Nonlinear System and Fuzzy Controller Design." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/99460633859414383792.
Full text義守大學
電子工程學系碩士班
93
The main purpose of this thesis is to present the design procedure of fuzzy controller for nonlinear systems. In order to obtain the Takagi-Sugeno (T-S) fuzzy model for nonlinear systems, the projective fuzzy clustering using the Gustafson-Kessel (G-K) algorithm is applied. The fuzzy controller design is constructed from the concept of parallel-distributed compensation (PDC) via linear matrix inequality (LMI). The principle of PDC is that for each local linear model, there exists an associated linear feedback. Interior-point polynomial algorithms are utilized to solve LMIs in order to find the feedback gains. Various fuzzy controller designs are studied including decay-rate constrained and optimal control. However, if the amounts of rule were large, it might be infeasible to find a solution for LMIs. Therefore, the fuzzy control proposed is based on relaxed stability conditions. Moreover, the simulation results show that the overall system is stable and the performance is fairly satisfactory.
Wen, Huang Yih, and 黃義文. "Applications of Natural Controller in Nonlinear Uncertain System." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/13746402434120795287.
Full text國立雲林科技大學
電機工程技術研究所
86
In this thesis, we discuss the convergence of fuzzy controllers as the number of fuzzy rules tends to infinity. The kind of fuzzy controllers is so-called the limit fuzzy controllers.The main objective is that the limit fuzzy controller can be used to examine the stability of fuzzy control systems. An inverted pendulum unstable system without friction is used to illustrate the efficiency of the fuzzy control. Besides, we also compare our the fuzzy controller with the traditional PID controllers. From the results of simulation, the robustness of the disturbance on an inverted pendulum system is over the traditional PID controller.Next, the fuzzy controller with national control law is used to stabilize the nonlinear uncertain system in the thesis. The nonlinear uncertain system is constructed by the inverted pendulum, uncertainties, saturation acturator and the disturbance. From the results of simulation, the fuzzy controller with natural controller and limit fuzzy controller can obtain the desired response.