English | PDF | 2009 | 204 Pages | ISBN : 1848821743 | 5.58 MB

Iterative learning control (ILC) has been a major control design methodology for twenty years; numerous algorithms have been developed to solve real- control problems, from MEMS to batch reactors, characterised by repetitive control operations.

Real- 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- 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- domains;

design in the frequency and 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- Iterative Learning Control will interest control eeers 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, acads and graduate students working in control will find it a useful reference to the current status of ILC.