论文信息 - Semi-active Iterative Learning Control

Semi-active Iterative Learning Control

This paper presents an Iterative Learning Control (ILC) algorithm for iterative parameter update in a semi-active system. The ILC law is designed to minimize a cost function, for example, the mean squared tracking error. First, a parametrized lifted domain representation of a linear parameter-varying system is developed explicitly. Based on this lifted domain representation and a cost function, gradient- based laws for the parameter update from iteration to iteration are proposed. Stability, monotonicity, steady state error, and robustness properties of these algorithms are presented. Finally, an application of the proposed algorithm is illustrated through the simulation of a plastic blow molding system. Index Terms-Iterative Learning Control, Semi-Active Systems.

Sandipan Mishra | Andrew Alleyne

[1] Shirley J. Dyke,et al. PHENOMENOLOGICAL MODEL FOR MAGNETORHEOLOGICAL DAMPERS , 1997 .

[2] Svante Gunnarsson,et al. Time and frequency domain convergence properties in iterative learning control , 2002 .

[3] Robert D. Hanson,et al. Electrorheological dampers and semi-active structural control , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[4] Andrew G. Alleyne,et al. Nonlinear control of an electrohydraulic injection molding machine via iterative adaptive learning , 1999 .

[5] Suguru Arimoto,et al. Bettering operation of Robots by learning , 1984, J. Field Robotics.

[6] H. E. Tseng,et al. Semi-Active Control Laws - Optimal and Sub-Optimal , 1994 .

[7] Okko H. Bosgra,et al. Extrapolation of optimal lifted system ILC solution, with application to a waferstage , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).