Download or read book Differential Neural Networks for Robust Nonlinear Control written by Alexander S. Poznyak and published by World Scientific. This book was released on 2001 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Neural Network Based Robust Nonlinear Control written by Nishant Unnikrishnan and published by . This book was released on 2006 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Online trained neural networks have become popular in recent years in the design of robust and adaptive controllers for dynamic systems with uncertainties due to their universal function approximation capabilities. This research explores the application of online neural networks for the design of model following controllers and for dynamic reoptimization of a Single Network Adaptive Critic (SNAC) optimal controller. Model following controllers for a general class of nonlinear systems with unknown uncertainties in their modeling equations have been developed in this research. A desirable characteristic of the model following controller scheme elaborated in this work is that it can be used in conjunction with any known control design technique. This research also discusses a technique that dynamically re-optimizes a Single Network Adaptive Critic controller. The SNAC based optimal controller designed for the nominal plant model no more retains optimality in the presence of uncertainties/unmodeled dynamics that may creep up in the system equations during operation. This necessitates the application of online function approximating neural networks that can help in SNAC reoptimization. Neural network weight update rules for continuous and discrete time systems have been derived using Lyapunov theory that guarantees both the stability of error dynamics and boundedness of the neural network weights. Detailed proofs and numerical simulations of the online weight update rules on various engineering problems have been provided in this document"--Abstract, leaf iii.

Download or read book Robust and Fault Tolerant Control written by Krzysztof Patan and published by Springer. This book was released on 2019-03-16 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust and Fault-Tolerant Control proposes novel automatic control strategies for nonlinear systems developed by means of artificial neural networks and pays special attention to robust and fault-tolerant approaches. The book discusses robustness and fault tolerance in the context of model predictive control, fault accommodation and reconfiguration, and iterative learning control strategies. Expanding on its theoretical deliberations the monograph includes many case studies demonstrating how the proposed approaches work in practice. The most important features of the book include: a comprehensive review of neural network architectures with possible applications in system modelling and control; a concise introduction to robust and fault-tolerant control; step-by-step presentation of the control approaches proposed; an abundance of case studies illustrating the important steps in designing robust and fault-tolerant control; and a large number of figures and tables facilitating the performance analysis of the control approaches described. The material presented in this book will be useful for researchers and engineers who wish to avoid spending excessive time in searching neural-network-based control solutions. It is written for electrical, computer science and automatic control engineers interested in control theory and their applications. This monograph will also interest postgraduate students engaged in self-study of nonlinear robust and fault-tolerant control.

Download or read book Dynamic Neural Network based Robust Control Methods for Uncertain Nonlinear Systems written by Huyen T. Dinh and published by . This book was released on 2012 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: This result is achieved by combining the DNN-identification strategy with a RISE (Robust Integral of the Sign of the Error) controller. In Chapters 4 and 5, a class of second-order uncertain nonlinear systems with partially unmeasurable states is considered. A DNN-based observer is developed to estimate the missing states in Chapter 4, and the DNN-based observer is developed for an output feedback (OFB) tracking control method in Chapter 5. In Chapter 6, an OFB control method is developed for uncertain nonlinear systems with time-varying input delays. In all developed approaches, weights of the DNN can be adjusted on-line: no off-line weight update phase is required. Chapter 7 concludes the proposal by summarizing the work and discussing some future problems that could be further investigated.

Download or read book Adaptive Dynamic Programming with Applications in Optimal Control written by Derong Liu and published by Springer. This book was released on 2017-01-04 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the most recent developments in adaptive dynamic programming (ADP). The text begins with a thorough background review of ADP making sure that readers are sufficiently familiar with the fundamentals. In the core of the book, the authors address first discrete- and then continuous-time systems. Coverage of discrete-time systems starts with a more general form of value iteration to demonstrate its convergence, optimality, and stability with complete and thorough theoretical analysis. A more realistic form of value iteration is studied where value function approximations are assumed to have finite errors. Adaptive Dynamic Programming also details another avenue of the ADP approach: policy iteration. Both basic and generalized forms of policy-iteration-based ADP are studied with complete and thorough theoretical analysis in terms of convergence, optimality, stability, and error bounds. Among continuous-time systems, the control of affine and nonaffine nonlinear systems is studied using the ADP approach which is then extended to other branches of control theory including decentralized control, robust and guaranteed cost control, and game theory. In the last part of the book the real-world significance of ADP theory is presented, focusing on three application examples developed from the authors’ work: • renewable energy scheduling for smart power grids;• coal gasification processes; and• water–gas shift reactions. Researchers studying intelligent control methods and practitioners looking to apply them in the chemical-process and power-supply industries will find much to interest them in this thorough treatment of an advanced approach to control.

Download or read book Applications of Nonlinear Control written by Meral Altınay and published by BoD – Books on Demand. This book was released on 2012-06-13 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: A trend of investigation of Nonlinear Control Systems has been present over the last few decades. As a result the methods for its analysis and design have improved rapidly. This book includes nonlinear design topics such as Feedback Linearization, Lyapunov Based Control, Adaptive Control, Optimal Control and Robust Control. All chapters discuss different applications that are basically independent of each other. The book will provide the reader with information on modern control techniques and results which cover a very wide application area. Each chapter attempts to demonstrate how one would apply these techniques to real-world systems through both simulations and experimental settings.

Download or read book Neural Network Based State Estimation of Nonlinear Systems written by Heidar A. Talebi and published by Springer. This book was released on 2009-12-04 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.

Download or read book Neural Network Control Of Robot Manipulators And Non Linear Systems written by F W Lewis and published by CRC Press. This book was released on 1998-11-30 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: There has been great interest in "universal controllers" that mimic the functions of human processes to learn about the systems they are controlling on-line so that performance improves automatically. Neural network controllers are derived for robot manipulators in a variety of applications including position control, force control, link flexibility stabilization and the management of high-frequency joint and motor dynamics. The first chapter provides a background on neural networks and the second on dynamical systems and control. Chapter three introduces the robot control problem and standard techniques such as torque, adaptive and robust control. Subsequent chapters give design techniques and Stability Proofs For NN Controllers For Robot Arms, Practical Robotic systems with high frequency vibratory modes, force control and a general class of non-linear systems. The last chapters are devoted to discrete- time NN controllers. Throughout the text, worked examples are provided.

Download or read book Applications of Neural Adaptive Control Technology written by Jens Kalkkuhl and published by World Scientific. This book was released on 1997 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the results of the second workshop on Neural Adaptive Control Technology, NACT II, held on September 9-10, 1996, in Berlin. The workshop was organised in connection with a three-year European-Union-funded Basic Research Project in the ESPRIT framework, called NACT, a collaboration between Daimler-Benz (Germany) and the University of Glasgow (Scotland).The NACT project, which began on 1 April 1994, is a study of the fundamental properties of neural-network-based adaptive control systems. Where possible, links with traditional adaptive control systems are exploited. A major aim is to develop a systematic engineering procedure for designing neural controllers for nonlinear dynamic systems. The techniques developed are being evaluated on concrete industrial problems from within the Daimler-Benz group of companies.The aim of the workshop was to bring together selected invited specialists in the fields of adaptive control, nonlinear systems and neural networks. The first workshop (NACT I) took place in Glasgow in May 1995 and was mainly devoted to theoretical issues of neural adaptive control. Besides monitoring further development of theory, the NACT II workshop was focused on industrial applications and software tools. This context dictated the focus of the book and guided the editors in the choice of the papers and their subsequent reshaping into substantive book chapters. Thus, with the project having progressed into its applications stage, emphasis is put on the transfer of theory of neural adaptive engineering into industrial practice. The contributors are therefore both renowned academics and practitioners from major industrial users of neurocontrol.

Download or read book Lyapunov based Robust and Adaptive Control Design for Nonlinear Uncertain Systems written by Kun Zhang and published by . This book was released on 2015 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: The control of systems with uncertain nonlinear dynamics is an important field of control science attracting decades of focus. In this dissertation, four different control strategies are presented using sliding mode control, adaptive control, dynamic compensation, and neural network for a nonlinear aeroelastic system with bounded uncertainties and external disturbance. In Chapter 2, partial state feedback adaptive control designs are proposed for two different aeroelastic systems operating in unsteady flow. In Chapter 3, a continuous robust control design is proposed for a class of single input and single output system with uncertainties. An aeroelastic system with a trailingedge flap as its control input will be considered as the plant for demonstration of effectiveness of the controller. The controller is proved to be robust by both mathematical proof and simulation results. In Chapter 3, a robust output feedback control strategy is discussed for the vibration suppression of an aeroelastic system operating in an unsteady incompressible flowfield. The aeroelastic system is actuated using a combination of leading-edge (LE) and trailing-edge (TE) flaps in the presence of different kinds of gust disturbances. In Chapter 5, a neural-network based model-free controller is designed for an aeroelastic system operating at supersonic speed. The controller is shown to be able to effectively asymptotically stabilize the system via both a Lyapunov-based stability proof and numerical simulation results.

Download or read book Differential Neural Networks For Robust Nonlinear Control Identification State Estimation And Trajectory Tracking written by Alex Poznyak and published by World Scientific. This book was released on 2001-09-28 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.).

Download or read book Nonlinear H2 H Infinity Constrained Feedback Control written by Murad Abu-Khalaf and published by Springer Science & Business Media. This book was released on 2006-08-02 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides techniques to produce robust, stable and useable solutions to problems of H-infinity and H2 control in high-performance, non-linear systems for the first time. The book is of importance to control designers working in a variety of industrial systems. Case studies are given and the design of nonlinear control systems of the same caliber as those obtained in recent years using linear optimal and bounded-norm designs is explained.

Download or read book Lectures on Stochastic Programming written by Alexander Shapiro and published by SIAM. This book was released on 2009-01-01 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization problems involving stochastic models occur in almost all areas of science and engineering, such as telecommunications, medicine, and finance. Their existence compels a need for rigorous ways of formulating, analyzing, and solving such problems. This book focuses on optimization problems involving uncertain parameters and covers the theoretical foundations and recent advances in areas where stochastic models are available. Readers will find coverage of the basic concepts of modeling these problems, including recourse actions and the nonanticipativity principle. The book also includes the theory of two-stage and multistage stochastic programming problems; the current state of the theory on chance (probabilistic) constraints, including the structure of the problems, optimality theory, and duality; and statistical inference in and risk-averse approaches to stochastic programming.

Download or read book Nonlinear and Adaptive Control with Applications written by Alessandro Astolfi and published by Springer Science & Business Media. This book was released on 2007-12-06 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: The authors here provide a detailed treatment of the design of robust adaptive controllers for nonlinear systems with uncertainties. They employ a new tool based on the ideas of system immersion and manifold invariance. New algorithms are delivered for the construction of robust asymptotically-stabilizing and adaptive control laws for nonlinear systems. The methods proposed lead to modular schemes that are easier to tune than their counterparts obtained from Lyapunov redesign.

Download or read book Neural Network Control of Nonlinear Discrete Time Systems written by Jagannathan Sarangapani and published by CRC Press. This book was released on 2018-10-03 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems 3rd ICAUS 2023 written by Yi Qu and published by Springer Nature. This book was released on with total page 589 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Neural Systems for Control written by Omid Omidvar and published by Elsevier. This book was released on 1997-02-24 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly applicable to control or making use of modern control theory. The book covers such important new developments in control systems such as intelligent sensors in semiconductor wafer manufacturing; the relation between muscles and cerebral neurons in speech recognition; online compensation of reconfigurable control for spacecraft aircraft and other systems; applications to rolling mills, robotics and process control; the usage of past output data to identify nonlinear systems by neural networks; neural approximate optimal control; model-free nonlinear control; and neural control based on a regulation of physiological investigation/blood pressure control. All researchers and students dealing with control systems will find the fascinating Neural Systems for Control of immense interest and assistance. Focuses on research in natural and artifical neural systems directly applicable to contol or making use of modern control theory Represents the most up-to-date developments in this rapidly growing application area of neural networks Takes a new and novel approach to system identification and synthesis