http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, to establish its properties; in addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. It provides solutions for the output trajectory tracking problem of unknown nonlinear systems based on sliding modes and inverse optimal control scheme--
Cover; Half Title; Title Page; Copyright Page; Dedication; Table of Contents; Preface; Acknowledgments; Authors; SECTION I: Analyses; Chapter 1: Introduction; 1.1 Preliminaries; 1.2 Motivation; 1.3 Objectives; 1.4 Book Structure; 1.5 Notation; 1.6 Acronyms; Chapter 2: Mathematical Preliminaries; 2.1 Optimal Control; 2.2 Lyapunov Stability; 2.3: Robust Stability Analysis; 2.3.1 Optimal Control for Disturbed Systems; 2.4 Passivity; 2.5 Discrete-time High Order Neural Networks; 2.6 The EKF Training Algorithm; 2.7 Separation Principle for Discrete-time Nonlinear Systems.
Chapter 3: Neural Block Control3.1 Identification; 3.2 Illustrative Example; 3.3 Neural Block Controller Design; 3.4 Applications; 3.4.1 Neural Network Identification; 3.4.2 Neural Block Controller Design; 3.4.3 Reduced Order Nonlinear Observer; 3.4.4 Simulation Results; 3.5 Conclusions; Chapter 4: Neural Optimal Control; 4.1 Inverse Optimal Control via CLF; 4.1.1 Example; 4.1.2 Inverse Optimal Control for Linear Systems; 4.2 Robust Inverse Optimal Control; 4.3 Trajectory Tracking Inverse Optimal Control; 4.3.1 Application to the Boost Converter; 4.3.1.1 Boost Converter Model.
4.3.1.2 Control Synthesis4.3.1.3 Simulation Results; 4.4 CLF-based Inverse Optimal Control for a Class of Nonlinear Positive Systems; 4.5 Speed-gradient for the Inverse Optimal Control; 4.5.1 Speed-gradient Algorithm; 4.5.2 Summary of the Proposed SG Algorithm to Calculate Parameter pk; 4.5.3 SG Inverse Optimal Control; 4.5.3.1 Example; 4.5.4 Application to the Inverted Pendulum on a Cart; 4.5.4.1 Simulation Results; 4.6 Speed-gradient Algorithm for Trajectory Tracking; 4.6.1 Example; 4.7 Trajectory Tracking for Systems in Block-control Form; 4.7.1 Example; 4.8 Neural Inverse Optimal Control.
4.8.1 Stabilization4.8.1.1 Example; 4.8.2 Trajectory Tracking; 4.8.2.1 Application to a Synchronous Generator; 4.8.2.2 Comparison; 4.9 Block-control Form: A Nonlinear Systems Particular Class; 4.9.1 Block Transformation; 4.9.2 Block Inverse Optimal Control; 4.10 Conclusions; SECTION II: Real-Time Applications; Chapter 5: Induction Motors; 5.1 Neural Identifier; 5.2 Discrete-time Super-twisting Observer; 5.3 Neural Sliding Modes Block Control; 5.4 Neural Inverse Optimal Control; 5.5 Implementation; 5.6 Prototype; 5.6.1 RCP System; 5.6.2 Power Electronics; 5.6.3 Signal Conditioning for ADC.
5.6.4 Real-time Controller Implementation5.6.4.1 Induction Motor Inputs and Outputs; 5.6.4.2 Flux Observer; 5.6.4.3 Neural Identifier; 5.6.4.4 Serial Communication Interface; 5.6.5 Neural Sliding Mode Real-time Results; 5.6.6 Neural Inverse Optimal Control Real-time Results; 5.7 Conclusions; Chapter 6: Doubly Fed Induction Generator; 6.1 Neural Identifiers; 6.1.1 DFIG Neural Identifier; 6.1.2 DC Link Neural Identifier; 6.2 Neural Sliding Mode Block Control; 6.2.1 DFIG Controller; 6.2.1.1 Simulation Results; 6.2.2 DC Link Controller; 6.2.2.1 Simulation Results.
"A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa plc."
Includes bibliographical references and index.
Boca Raton, FL : CRC Press/Taylor & Francis Group, 2018.