Neural Network Control of Nonlinear Discrete-Time Systems by Sarangapani Jagannathan – A Comprehensive Guide to Intellig

Introduction

Modern control systems demand intelligent solutions capable of handling complex nonlinearities, uncertainties, and dynamic disturbances. Neural Network Control of Nonlinear Discrete-Time Systems by Sarangapani Jagannathan offers a rigorous yet accessible exploration of how artificial neural networks can revolutionize feedback control design. This book bridges the gap between theoretical foundations and practical implementation, making it an essential resource for graduate students, researchers, and practicing engineers in India and beyond.

Book Overview

This hardcover volume from CRC Press presents a comprehensive treatment of neural network-based control for discrete-time nonlinear systems. Unlike continuous-time approaches, discrete-time control is critical for digital implementation and real-world embedded systems. The author systematically develops neurocontrollers that guarantee closed-loop stability, using Lyapunov theory to prove convergence and robustness. With simulation examples and step-by-step derivations, the book equips readers to design adaptive controllers for robotic manipulators, autonomous vehicles, and industrial processes.

Key Highlights

• First comprehensive text dedicated to neural network control in discrete-time domain
• Rigorous stability proofs using Lyapunov analysis for each control scheme
• Practical simulation examples that illustrate theoretical concepts
• Coverage of both direct and indirect adaptive control architectures
• Focus on handling uncertainties, disturbances, and unmodeled dynamics

Inside the Book

The book begins with foundational concepts in neural networks, including multilayer perceptrons, radial basis functions, and dynamic neural networks. It then moves to dynamical systems theory and feedback linearization techniques. Core chapters address neurocontroller design for regulation, tracking, and optimal control problems. Special attention is given to actuator nonlinearities, input constraints, and output feedback scenarios. Each chapter concludes with simulation studies that demonstrate real-time performance.

Key Topics

• Discrete-time nonlinear system modeling and analysis
• Neural network architectures for control applications
• Direct and indirect adaptive neurocontrol
• Robust control against parameter uncertainties and external disturbances
• Optimal control using neural network approximators
• Output feedback control with observer-based designs

Reader Benefits

Readers will gain the ability to design stable, high-performance neurocontrollers for complex systems. The book provides ready-to-use algorithms and design guidelines that can be directly applied in robotics, aerospace, and process control industries. The clear mathematical exposition makes advanced concepts accessible, while the simulation examples offer hands-on insight. Indian students will find the discrete-time focus particularly relevant for digital controller implementations common in local industries.

Learning Outcomes

• Understand the mathematical foundations of neural network control
• Derive stability conditions for discrete-time neurocontrollers
• Implement adaptive control schemes for nonlinear systems
• Analyze robustness and convergence properties
• Design output feedback controllers with neural observers

Who Should Read

This book is ideal for postgraduate students in electrical, electronics, and mechanical engineering specializing in control systems. Researchers working in intelligent control, adaptive systems, and neural networks will find it invaluable. Practicing engineers in automation, robotics, and automotive sectors who need to implement advanced control algorithms will benefit from the practical design methods. It also serves as a reference for faculty teaching advanced control courses.

About the Author

Sarangapani Jagannathan is a distinguished professor and researcher in the field of control systems and neural networks. With decades of academic and industrial experience, he has authored numerous influential papers and books on adaptive control, neural networks, and cyber-physical systems. His work is recognized globally for its depth and practical relevance.

About the Publisher

CRC Press is a premier publisher of scientific and technical books, known for its high-quality engineering and technology titles. Their catalog includes authoritative works that serve as standard references for students and professionals worldwide. This book upholds CRC Press's tradition of excellence in control systems literature.

Conclusion

Neural Network Control of Nonlinear Discrete-Time Systems is a definitive guide for anyone seeking to master intelligent control design. By combining rigorous theory with practical examples, it empowers readers to tackle real-world control challenges. Order your hardcover copy from Bookshops.in today and add this essential resource to your library.