Neural Network Control of Nonlinear Discrete-Time Systems: 1st Edition (Hardback) book cover

Neural Network Control of Nonlinear Discrete-Time Systems

1st Edition

By Jagannathan Sarangapani

CRC Press

624 pages | 171 B/W Illus.

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pub: 2006-04-24
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Description

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.

Table of Contents

BACKGROUND ON NEURAL NETWORKS

NN Topologies and Recall

Properties of NN

NN Weight Selection and Training

NN Learning and Control Architectures

References

Problems

BACKGROUND AND DISCRETE-TIME ADAPTIVE CONTROL

Dynamical Systems

Mathematical Background

Properties of Dynamical Systems

Nonlinear Stability Analysis and Controls Design

Robust Implicit STR

References

Problems

Appendix 2.A

NEURAL NETWORK CONTROL OF NONLINEAR SYSTEMS AND FEEDBACK LINEARIZATION

NN Control with Discrete-Time Tuning

Feedback Linearization

NN Feedback Linearization

Multilayer NN for Feedback Linearization

Passivity Properties of the NN

Conclusions

References

Problems

NEURAL NETWORK CONTROL OF UNCERTAIN NONLINEAR DISCRETE-TIME SYSTEMS WITH ACTUATOR NONLINEARITIES

Background on Actuator Nonlinearities

Reinforcement NN Learning Control with Saturation

Uncertain Nonlinear System with Unknown Deadzone and Saturation Nonlinearities

Adaptive NN Control of Nonlinear System with Unknown Backlash

Conclusions

References

Problems

Appendix 4.A

Appendix 4.B

Appendix 4.C

Appendix 4.D

OUTPUT FEEDBACK CONTROL OF STRICT FEEDBACK NONLINEAR MIMO DISCRETE-TIME SYSTEMS

Class of Nonlinear Discrete-Time Systems

Output Feedback Controller Design

Weight Updates for Guaranteed Performance

Conclusions

References

Problems

Appendix 5.A

Appendix 5.B

NEURAL NETWORK CONTROL OF NONSTRICT FEEDBACK NONLINEAR SYSTEMS

Introduction

Adaptive NN Control Design Using State Measurements

Output Feedback NN Controller Design

Conclusions

References

Problems

Appendix 6.A

Appendix 6.B

SYSTEM IDENTIFICATION USING DISCRETE-TIME NEURAL NETWORKS

Identification of Nonlinear Dynamical Systems

Identifier Dynamics for MIMO Systems

NN Identifier Design

Passivity Properties of the NN

Conclusions

References

Problems

DISCRETE-TIME MODEL REFERENCE ADAPTIVE CONTROL

Dynamics of an mnth-Order Multi-Input and Multi-Output System

NN Controller Design

Projection Algorithm

Conclusions

References

Problems

NEURAL NETWORK CONTROL IN DISCRETE-TIME USING HAMILTON-JACOBI-BELLMAN FORMULATION

Optimal Control and Generalized HJB Equation in Discrete-Time

NN Least-Squares Approach

Numerical Examples

Conclusions

References

Problems

NEURAL NETWORK OUTPUT FEEDBACK CONTROLLER DESIGN AND EMBEDDED HARDWARE IMPLEMENTATION

Embedded Hardware-PC Real-Time Digital Control System

SI Engine Test Bed

Lean Engine Controller Design and Implementation

EGR Engine Controller Design and Implementation

Conclusions

References

Problems

Appendix 10.A

Appendix 10.B

INDEX

About the Series

Automation and Control Engineering

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Subject Categories

BISAC Subject Codes/Headings:
TEC007000
TECHNOLOGY & ENGINEERING / Electrical
TEC008000
TECHNOLOGY & ENGINEERING / Electronics / General