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This book provides readers with a comprehensive coverage of iterative learning control. The book can be used as a text or reference for a course at graduate level and is also suitable for self-study and for industry-oriented courses of continuing education.
Ranging from aerodynamic curve identification robotics to functional neuromuscular stimulation, Iterative Learning Control (ILC), started in the early 80s, is found to have wide applications in practice. Generally, a system under control may have uncertainties in its dynamic model and its environment. One attractive point in ILC lies in the utilisation of the system repetitiveness to reduce such uncertainties and in turn to improve the control performance by operating the system repeatedly. This monograph emphasises both theoretical and practical aspects of ILC. It provides some recent developments in ILC convergence and robustness analysis. The book also considers issues in ILC design. Several practical applications are presented to illustrate the effectiveness of ILC. The applied examples provided in this monograph are particularly beneficial to readers who wish to capitalise the system repetitiveness to improve system control performance.
List of contents
High-order iterative learning control of uncertain nonlinear systems with state delays.- High-order P-type iterative learning controller using current iteration tracking error.- Iterative learning control for uncertain nonlinear discrete-time systems using current iteration tracking error.- Iterative learning control for uncertain nonlinear discrete-time feedback systems with saturation.- Initial state learning method for iterative learning control of uncertain time-varying systems.- High-order terminal iterative learning control with an application to a rapid thermal process for chemical vapor deposition.- Designing iterative learning controllers via noncausal filtering.- Practical iterative learning control using weighted local symmetrical double-integral.- Iterative learning identification with an application to aerodynamic drag coefficient curve extraction problem.- Iterative learning control of functional neuromuscular stimulation systems.- Conclusions and future research.
Summary
From aerodynamic curve identification robotics to neuromuscular stimulation, Iterative Learning Control (ILC), has many applications. A system may have uncertainties in its dynamic model and its environment. Using system repetitiveness, ILC reduces uncertainties and improves control performance.
