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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Iterative Learning Control: Robustness and Monotonic Convergence for Interval Systems

Iterative Learning Control: Robustness and Monotonic Convergence for Interval Systems

Ahn, Hyo-Sung
Moore, Kevin L.
Chen, Yangquan

Paperback

Series: Communications and Control Engineering

Technology & EngineeringGeneral ComputersGeneral Mathematics

ISBN10: 1849966583
ISBN13: 9781849966580
Publisher: Springer Nature
Published: Oct 19 2010
Pages: 230
Weight: 0.78
Height: 0.53 Width: 6.14 Depth: 9.21
Language: English

This monograph studies the design of robust, monotonically convergent iterative learning controllers (ILC) for discrete-time systems. It 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. It presents solutions to three fundamental robust interval computational problems (used as basic tools for designing robust ILC controllers): finding the maximum singular value of an interval matrix, determining the robust stability of interval polynomial matrix, and obtaining the power of an interval matrix.

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