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.
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