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.