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.
