Bøger af Zhong-Ping Jiang

This book presents a study on the novel concept of "event-triggered control of nonlinear systems subject to disturbances", discussing the theory and practical applications.

Provides the reader with an accessible primer on a new direction in control theory still in its infancy, namely Learning-Based Control Theory, that is closely tied to the literature of safe Reinforcement Learning and Adaptive Dynamic Programming.

The subject of nonlinear control systems analysis has grown rapidly and this book provides a simple presentation of their stability and feedback stabilization. It enables the reader to learn and understand major techniques used in mathematical control theory.

A comprehensive look at state-of-the-art ADP theory and real-world applicationsThis book fills a gap in the literature by providing a theoretical framework for integrating techniques from adaptive dynamic programming (ADP) and modern nonlinear control to address data-driven optimal control design challenges arising from both parametric and dynamic uncertainties.Traditional model-based approaches leave much to be desired when addressing the challenges posed by the ever-increasing complexity of real-world engineering systems. An alternative which has received much interest in recent years are biologically-inspired approaches, primarily RADP. Despite their growing popularity worldwide, until now books on ADP have focused nearly exclusively on analysis and design, with scant consideration given to how it can be applied to address robustness issues, a new challenge arising from dynamic uncertainties encountered in common engineering problems.Robust Adaptive Dynamic Programming zeros in on the practical concerns of engineers. The authors develop RADP theory from linear systems to partially-linear, large-scale, and completely nonlinear systems. They provide in-depth coverage of state-of-the-art applications in power systems, supplemented with numerous real-world examples implemented in MATLAB. They also explore fascinating reverse engineering topics, such how ADP theory can be applied to the study of the human brain and cognition. In addition, the book:* Covers the latest developments in RADP theory and applications for solving a range of systems' complexity problems* Explores multiple real-world implementations in power systems with illustrative examples backed up by reusable MATLAB code and Simulink block sets* Provides an overview of nonlinear control, machine learning, and dynamic control* Features discussions of novel applications for RADP theory, including an entire chapter on how it can be used as a computational mechanism of human movement controlRobust Adaptive Dynamic Programming is both a valuable working resource and an intriguing exploration of contemporary ADP theory and applications for practicing engineers and advanced students in systems theory, control engineering, computer science, and applied mathematics.