Bøger i Intelligent Control and Learni serien

This book provides recent theoretical results and applications of the consensus control for nonlinear leader-following systems. Combining adaptive control technique, fuzzy logic systems, neural networks with the other control techniques or approaches, this book investigates the consensus control problem of leader-following systems and proposes the corresponding control scheme. This book intends to provide the readers a good understanding on consensus control based on adaptive control technology. This book can serve as a reference for the main research issues and results on nonlinear multi-agent systems including leader-following systems for researchers devoting to various areas of control theory, as well as a material for graduate and undergraduate students interested in nonlinear multi-agent systems including leader-following systems and their applications. Some prerequisites for reading this book include nonlinear system theory, matrix theory, mathematics, basic graph theory, and soon.

This book provides up-to-date developments in the stability analysis and (anti-)synchronization control area for complex-valued neural networks systems with time delay. It brings out the characteristic systematism in them and points out further insight to solve relevant problems. It presents a comprehensive, up-to-date, and detailed treatment of dynamical behaviors including stability analysis and (anti-)synchronization control. The materials included in the book are mainly based on the recent research work carried on by the authors in this domain.The book is a useful reference for all those from senior undergraduates, graduate students, to senior researchers interested in or working with control theory, applied mathematics, system analysis and integration, automation, nonlinear science, computer and other related fields, especially those relevant scientific and technical workers in the research of complex-valued neural network systems, dynamic systems, and intelligent control theory.

This book belongs to the subject of control and systems theory. It studies a novel data-driven framework for the design and analysis of iterative learning control (ILC) for nonlinear discrete-time systems. A series of iterative dynamic linearization methods is discussed firstly to build a linear data mapping with respect of the system's output and input between two consecutive iterations. On this basis, this work presents a series of data-driven ILC (DDILC) approaches with rigorous analysis. After that, this work also conducts significant extensions to the cases with incomplete data information, specified point tracking, higher order law, system constraint, nonrepetitive uncertainty, and event-triggered strategy to facilitate the real applications. The readers can learn the recent progress on DDILC for complex systems in practical applications. This book is intended for academic scholars, engineers, and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.