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Iterative Learning Control : Robustness and Monotonic Convergence for Interval Systems
This monograph studies the design of robust, monotonically-convergent iterative learning controllers for discrete-time systems. Two key problems with the fundamentals of iterative learning control (ILC) design as treated by existing work are: first, many ILC design strategies assume nominal knowledge of the system to be controlled and; second, it is well-known that many ILC algorithms do not produce monotonic convergence, though in applications monotonic convergence is often essential. Iterative Learning Control takes account of the recently-developed comprehensive approach to robust ILC analysis and design established to handle the situation where the plant model is uncertain. Considering ILC in the iteration domain, it presents a unified analysis and design framework that enables designers to consider both robustness and monotonic convergence for typical uncertainty models, including parametric interval uncertainties, iteration-domain frequency uncertainty, and iteration-domain stochastic uncertainty.
Advances in Robot Control : From Everyday Physics to Human-Like Movements
This volume provides a unique collection of papers written in honor of the 70th birthday of Suguru Arimoto who has long been recognized as a pioneer in the field of robot control. A variety of his research is reflected in this book, which includes contributions from leading experts in the field, who have also been closely associated with Suguru Arimoto at various stages in his distinguished career. The book is build around two themes: the physics-based robot control for coping with the so-called everyday physics problems on one hand, and the challenge of reproducing beautiful, human-like movements on the other hand. These themes defined much of Arimoto’s research in the field of robot control and are the cornerstones of his perception of human robotics.
