Bøger af Jian-Xin Xu

Iterative learning control (ILC) has been a major control design methodology for twenty years; numerous algorithms have been developed to solve real-time control problems, from MEMS to batch reactors, characterised by repetitive control operations.Real-time Iterative Learning Control demonstrates how the latest advances in ILC can be applied to a number of plants widely encountered in practice. The authors provide a hitherto lacking systematic introduction to real-time ILC design and source of illustrative case studies for ILC problem solving; the fundamental concepts, schematics, configurations and generic guidelines for ILC design and implementation are enhanced by a well-selected group of representative, simple and easy-to-learn example applications. Key issues in ILC design and implementation in the linear and nonlinear plants that pervade mechatronics and batch processes are addressed. In particular, the book discusses:¿ ILC design in the continuous- and discrete-time domains;¿ design in the frequency and time domains;¿ design with problem-specific performance objectives including robustness and optimality;¿ design by means of classical tools based on Bode plots and state space; and¿ iterative-learning-based parametric identification. Real-time Iterative Learning Control will interest control engineers looking for examples of how this important control technique can be applied to a variety of real-life problems. With its systematic formulation and analysis of different system properties and performance and its exposition of open problems, academics and graduate students working in control will find it a useful reference to the current status of ILC.

The fifth approach is based on Iterative learning control (ILC) theory and is aimed at uncertain linear/nonlinear systems with repeatable tasks and the final approach is based on fuzzy logic control (FLC) and is intended for highly uncertain systems with heuristic control knowledge.

A timely guide using iterative learning control (ILC) as a solution for multi-agent systems (MAS) challenges, showcasing recent advances and industrially relevant applications* Explores the synergy between the important topics of iterative learning control (ILC) and multi-agent systems (MAS)* Concisely summarizes recent advances and significant applications in ILC methods for power grids, sensor networks and control processes* Covers basic theory, rigorous mathematics as well as engineering practice

This monograph summarizes the recent achievements made in the field of iterative learning control. It opens a new avenue towards a new paradigm in deterministic learning control theory accompanied by detailed examples.