Sarangapani Neural Network Control of Nonlinear Discrete-Time Systems

Erscheinungsjahr 2006
ISBN: 978-1-4200-1545-4
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 622 Seiten

Reihe: Automation and Control Engineering

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.

Zielgruppe

Students and engineers in electrical engineering, control engineering, intelligent control systems, artificial intelligence, neural networks, and aerospace, automotive, vehicular, industrial, manufacturing, and process control engineering.

Autoren/Hrsg.

Sarangapani, Jagannathan

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Überwachungstechnik

Inhaltsverzeichnis

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