Adaptive Sliding Mode Neural Network Control For Nonlinear Systems
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Adaptive Sliding Mode Neural Network Control for Nonlinear Systems by Yang Li,Jianhua Zhang,Wu Qiong Pdf
Adaptive Sliding Mode Neural Network Control for Nonlinear Systems introduces nonlinear systems basic knowledge, analysis and control methods, and applications in various fields. It offers instructive examples and simulations, along with the source codes, and provides the basic architecture of control science and engineering. Introduces nonlinear systems' basic knowledge, analysis and control methods, along with applications in various fields Offers instructive examples and simulations, including source codes Provides the basic architecture of control science and engineering
Identification and Adaptive Control for Nonlinear Systems and Applications by Jianhua Zhang,Yang Li,Qiang Chen Pdf
Identification and Adaptive Control for Nonlinear Systems and Applications: Applied Mathematics in Control Engineering introduces nonlinear systems concepts, system analysis, system control methods and applications in various fields. The major contribution of the book includes: (1) The basic concepts of nonlinear systems stability analysis and nonlinear systems control method. (2) The stability analysis of complex nonlinear system with adaptive neural networks control. (3) The nonlinear systems adaptive sliding mode controller design of complex nonlinear systems. (4) Some industrial application. The book gives an introduction to basic nonlinear systems architectures for adaptive control methods. Emphasis is placed on the mathematical analysis of these systems, on methods of controlling them for adaptive control and on their application to practical engineering problems in such areas as aircraft path planning. This book enables audience to understand the basic architectures of control science and engineering, and to master classical and advanced design method for nonlinear system. Introduces nonlinear systems concepts, system analysis, system control methods and applications in various fields Presents basic concepts of nonlinear systems stability analysis and nonlinear systems control method Offers practical examples
Stable Adaptive Control and Estimation for Nonlinear Systems by Jeffrey T. Spooner,Manfredi Maggiore,Raúl Ordóñez,Kevin M. Passino Pdf
Thema dieses Buches ist die Anwendung neuronaler Netze und Fuzzy-Logic-Methoden zur Identifikation und Steuerung nichtlinear-dynamischer Systeme. Dabei werden fortgeschrittene Konzepte der herkömmlichen Steuerungstheorie mit den intuitiven Eigenschaften intelligenter Systeme kombiniert, um praxisrelevante Steuerungsaufgaben zu lösen. Die Autoren bieten viel Hintergrundmaterial; ausgearbeitete Beispiele und Übungsaufgaben helfen Studenten und Praktikern beim Vertiefen des Stoffes. Lösungen zu den Aufgaben sowie MATLAB-Codebeispiele sind ebenfalls enthalten.
Neural Network Control of Nonlinear Discrete-Time Systems by Jagannathan Sarangapani Pdf
Intelligent systems are a hallmark of modern feedback control systems. But as these systems mature, we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities, disturbances, unforeseen dynamics, and unstructured uncertainties. Artificial neural networks offer a combination of adaptability, parallel processing, and learning capabilities that outperform other intelligent control methods in more complex systems. Borrowing from Biology Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. At every step, the author derives rigorous stability proofs and presents simulation examples to demonstrate the concepts. Progressive Development After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware. Neural Network Control of Nonlinear Discrete-Time Systems fosters an understanding of neural network controllers and explains how to build them using detailed derivations, stability analysis, and computer simulations.
Advances and Applications in Sliding Mode Control systems by Ahmad Taher Azar,Quanmin Zhu Pdf
This book describes the advances and applications in Sliding mode control (SMC) which is widely used as a powerful method to tackle uncertain nonlinear systems. The book is organized into 21 chapters which have been organised by the editors to reflect the various themes of sliding mode control. The book provides the reader with a broad range of material from first principles up to the current state of the art in the area of SMC and observation presented in a clear, matter-of-fact style. As such it is appropriate for graduate students with a basic knowledge of classical control theory and some knowledge of state-space methods and nonlinear systems. The resulting design procedures are emphasized using Matlab/Simulink software.
Stable Adaptive Neural Network Control by S.S. Ge,C.C. Hang,T.H. Lee,Tao Zhang Pdf
Recent years have seen a rapid development of neural network control tech niques and their successful applications. Numerous simulation studies and actual industrial implementations show that artificial neural network is a good candidate for function approximation and control system design in solving the control problems of complex nonlinear systems in the presence of different kinds of uncertainties. Many control approaches/methods, reporting inventions and control applications within the fields of adaptive control, neural control and fuzzy systems, have been published in various books, journals and conference proceedings. In spite of these remarkable advances in neural control field, due to the complexity of nonlinear systems, the present research on adaptive neural control is still focused on the development of fundamental methodologies. From a theoretical viewpoint, there is, in general, lack of a firmly mathematical basis in stability, robustness, and performance analysis of neural network adaptive control systems. This book is motivated by the need for systematic design approaches for stable adaptive control using approximation-based techniques. The main objec tives of the book are to develop stable adaptive neural control strategies, and to perform transient performance analysis of the resulted neural control systems analytically. Other linear-in-the-parameter function approximators can replace the linear-in-the-parameter neural networks in the controllers presented in the book without any difficulty, which include polynomials, splines, fuzzy systems, wavelet networks, among others. Stability is one of the most important issues being concerned if an adaptive neural network controller is to be used in practical applications.
Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems by Kasra Esfandiari,Farzaneh Abdollahi,Heidar A. Talebi Pdf
The focus of this book is the application of artificial neural networks in uncertain dynamical systems. It explains how to use neural networks in concert with adaptive techniques for system identification, state estimation, and control problems. The authors begin with a brief historical overview of adaptive control, followed by a review of mathematical preliminaries. In the subsequent chapters, they present several neural network-based control schemes. Each chapter starts with a concise introduction to the problem under study, and a neural network-based control strategy is designed for the simplest case scenario. After these designs are discussed, different practical limitations (i.e., saturation constraints and unavailability of all system states) are gradually added, and other control schemes are developed based on the primary scenario. Through these exercises, the authors present structures that not only provide mathematical tools for navigating control problems, but also supply solutions that are pertinent to real-life systems.
Application of Neural Networks to Adaptive Control of Nonlinear Systems by G. W. Ng Pdf
This book investigates the ability of a neural network (NN) to learn how to control an unknown (nonlinear, in general) system, using data acquired on-line, that is during the process of attempting to exert control. Two algorithms are developed to train the neural network for real-time control applications. The first algorithm is known as Learning by Recursive Least Squares (LRLS) algorithm and the second algorithm is known as Integrated Gradient and Least Squares (IGLS) algorithm. The ability of these algorithms for training the NN controller for real-time control is demonstrated on practical applications and the local convergence and stability requirements of these algorithms are analysed. In addition, network topology, learning algorithms (particularly supervised learning) and neural network control strategies including a new classification system for them, are presented.
Differential Neural Networks for Robust Nonlinear Control by Alexander S. Poznyak,Edgar N. Sanchez,Wen Yu (profesor titular.) Pdf
This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.). Contents: Theoretical Study: Neural Networks Structures; Nonlinear System Identification: Differential Learning; Sliding Mode Identification: Algebraic Learning; Neural State Estimation; Passivation via Neuro Control; Neuro Trajectory Tracking; Neurocontrol Applications: Neural Control for Chaos; Neuro Control for Robot Manipulators; Identification of Chemical Processes; Neuro Control for Distillation Column; General Conclusions and Future Work; Appendices: Some Useful Mathematical Facts; Elements of Qualitative Theory of ODE; Locally Optimal Control and Optimization. Readership: Graduate students, researchers, academics/lecturers and industrialists in neural networks.
Deep Reinforcement Learning with Guaranteed Performance by Yinyan Zhang,Shuai Li,Xuefeng Zhou Pdf
This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances. It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution. Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.
Sliding Mode Control Using MATLAB by Jinkun Liu Pdf
Sliding Mode Control Using MATLAB provides many sliding mode controller design examples, along with simulation examples and MATLAB® programs. Following the review of sliding mode control, the book includes sliding mode control for continuous systems, robust adaptive sliding mode control, sliding mode control for underactuated systems, backstepping, and dynamic surface sliding mode control, sliding mode control based on filter and observer, sliding mode control for discrete systems, fuzzy sliding mode control, neural network sliding mode control, and sliding mode control for robot manipulators. The contents of each chapter are independent, providing readers with information they can use for their own needs. It is suitable for the readers who work on mechanical and electronic engineering, electrical automation engineering, etc., and can also be used as a teaching reference for universities. Provides many sliding mode controller design examples to help readers solve their research and design problems Includes various, implementable, robust sliding mode control design solutions from engineering applications Provides the simulation examples and MATLAB programs for each sliding mode control algorithm
Parameter Estimation and Adaptive Control for Nonlinear Servo Systems by Shubo Wang,Jing Na,Xuemei Ren Pdf
Parameter Estimation and Adaptive Control for Nonlinear Servo Systems presents the latest advances in observer-based control design, focusing on adaptive control for nonlinear systems such as adaptive neural network control, adaptive parameter estimation, and system identification. This book offers an array of new real-world applications in the field. Written by eminent scientists in the field of control theory, this book covers the latest advances in observer-based control design. It provides fundamentals, algorithms, and it discusses key applications in the fields of power systems, robotics and mechatronics, flight and automotive systems. Presents a clear and concise introduction to the latest advances in parameter estimation and adaptive control with several concise applications for servo systems Covers a wide range of applications usually not found in similar books, such as power systems, robotics, mechatronics, aeronautics, and industrial systems Contains worked examples which make it ideal for advanced courses as well as for researchers starting to work in the field, particularly suitable for engineers wishing to enter the field quickly and efficiently
Robust Control for Nonlinear Time-Delay Systems by Changchun Hua,Liuliu Zhang,Xinping Guan Pdf
This book reports on the latest findings concerning nonlinear control theory and applications. It presents novel work on several kinds of commonly encountered nonlinear time-delay systems, including those whose nonlinear terms satisfy high-order polynomial form or general nonlinear form, those with nonlinear input or a triangular structure, and so on. As such, the book will be of interest to university researchers, R&D engineers and graduate students in the fields of control theory and control engineering who wish to learn about the core principles, methods, algorithms, and applications of nonlinear time-delay systems.
Nonlinear and Adaptive Control by Alan S.I. Zinober,David H. Owens Pdf
The objective of the EU Nonlinear Control Network Workshop was to bring together scientists who are already active in nonlinear control and young researchers working in this field. This book presents selectively invited contributions from the workshop, some describing state-of-the-art subjects that already have a status of maturity while others propose promising future directions in nonlinear control. Amongst others, following topics of nonlinear and adaptive control are included: adaptive and robust control, applications in physical systems, distributed parameter systems, disturbance attenuation, dynamic feedback, optimal control, sliding mode control, and tracking and motion planning.
Advances in Sliding Mode Control by B Bandyopadhyay,S Janardhanan,Sarah K. Spurgeon Pdf
The sliding mode control paradigm has become a mature technique for the design of robust controllers for a wide class of systems including nonlinear, uncertain and time-delayed systems. This book is a collection of plenary and invited talks delivered at the 12th IEEE International Workshop on Variable Structure System held at the Indian Institute of Technology, Mumbai, India in January 2012. After the workshop, these researchers were invited to develop book chapters for this edited collection in order to reflect the latest results and open research questions in the area. The contributed chapters have been organized by the editors to reflect the various themes of sliding mode control which are the current areas of theoretical research and applications focus; namely articulation of the fundamental underpinning theory of the sliding mode design paradigm, sliding modes for decentralized system representations, control of time-delay systems, the higher order sliding mode concept, results applicable to nonlinear and underactuated systems, sliding mode observers, discrete sliding mode control together with cutting edge research contributions in the application of the sliding mode concept to real world problems. This book provides the reader with a clear and complete picture of the current trends in Variable Structure Systems and Sliding Mode Control Theory.
