Iterative Learning Control : Robustness and Monotonic Convergence for Interval Systems
- Author
- Hyo-Sung Ahn, YangQuan Chen, Kevin L. Moore
- Publication Year
- 2007
- Publisher
- Springer
- Language
- English
- Document Type
- Book
- Faculty / Subject Heading
- Engineering
- Download Book Read book
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.
Keywords: Engineering / Kalman-Filter / Algorithms / Learning / Linear optimization / Robot / Robotics / Uncertainty
