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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 iterative learning control (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 in a modular approach by integration with other control techniques; and

- design by means of classical tools based on Bode plots and state space.

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.