ISBN 9780071333276,Digital Control And State Variable Methods

Digital Control And State Variable Methods

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ISBN 9780071333276
Publisher

Tata McGraw - Hill Education

Publication Year 2012
ISBN-13

ISBN 9780071333276

ISBN-10 0071333274
Binding

Paperback

Edition 4th
Number of Pages 928 Pages
Language (English)
Subject

Programming languages||

The book provides an Equal weightage on both control of Continuous-Time & Discrete- Time linear systems, based on frequency-domain & state space methods of The book provides an Equal weightage on both control of Continuous-Time & Discrete- Time linear systems, based on frequency-domain & state space methods of design.This edition introduces detailed coverage on Intelligent Control with Neural Networks/Support Vector Machines, Fuzzy Logic and Neuro-Fuzzy Systems and Optimization with Genetic Algorithm for better comprehension of the subject. Salient Features 2 Chapters dedicated to the coverage of Nonlinear systems MATLAB/ Simulink tools are present as part of the Web Supplements New chapters on: Intelligent Control with Neural Networks/Support Vector Machines, Fuzzy Logic and Neuro-Fuzzy Systems and Optimization with Genetic Algorithm TABLE OF CONTENTS Part I Digital Control: Principles and Design in Transform Domain 1 1. Introduction 2. Signal Processing in Digital Control 3. Models of Digital Control Devices and Systems 4. Design of Digital Control Algorithms Part II State Variable Methods in Automatic Control: Continuous-Time and Sampled-Data Systems 5. Control System Analysis Using State Variable Methods 6. State Variable Analysis of Digital Control Systems 7. Pole-Placement Design and State Observers 8. Linear Quadratic Optimal Control through Lyapunov Synthesis Part III Nonlinear Control Systems: Conventional and Intelligent 9. Nonlinear Systems Analysis 10. Nonlinear Control Structures 11. Intelligent Control with Neural Networks/Support Vector Machines 12. Fuzzy Logic and Neuro-Fuzzy Systems 13. Optimization with Genetic Algorithms 14. Intelligent Control with Reinforcement Learning
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