In this paper we examine the robustness of norm optimal ILC with quadratic cost criterion for discrete-time, linear time-invariant, single-input single-output systems. A bounded multiplicative uncertainty model is used to describe the uncertain system and a sufficient condition for robust monotonic convergence is developed. We find that, for sufficiently large uncertainty, the performance weighting can not be selected arbitrarily large, and thus overall performance is limited. To maximize available performance, a time-frequency design methodology is presented to shape the weighting matrix based on the initial tracking error. The design is applied to a nanopositioning system and simulation results are presented.
D. A. Bristow, "Weighting Matrix Design for Robust Monotonic Convergence in Norm Optimal Iterative Learning Control," Proceedings of the 2008 American Control Conference, 2008, Institute of Electrical and Electronics Engineers (IEEE), Jun 2008.
The definitive version is available at https://doi.org/10.1109/ACC.2008.4587213
2008 American Control Conference, 2008
Mechanical and Aerospace Engineering
Keywords and Phrases
Adaptive Control; Control System Synthesis; Discrete Time Systems; Iterative Methods; Learning Systems; Optimal Control
Article - Conference proceedings
© 2008 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jun 2008