论文信息 - Iterative learning control-convergence using high gain feedback

Iterative learning control-convergence using high gain feedback

The author presents a convergence theory for iterative learning control based on the use of high-gain current trial feedback for the special case of relative degree one, MIMO (multiple-input multiple-output) minimum-phase systems. The results are related to those of Padieu and Su (1990) via the notion of positive real systems. In particular, positive real systems are easily arranged to have convergent learning by simple proportional learning rules of arbitrary positive gain.<<ETX>>

D. Owens

[1] B. Anderson. A SYSTEM THEORY CRITERION FOR POSITIVE REAL MATRICES , 1967 .

[2] David H. Owens,et al. Analysis and Control of Multipass Processes , 1982 .

[3] David H. Owens,et al. Positive-Real Structure and High-Gain Adaptive Stabilization , 1987 .

[4] Hartmut Logemann,et al. Input‐output theory of high‐gain adaptive stabilization of infinite‐dimensional systems with non‐linearities , 1988 .

[5] Renjeng Su,et al. An H∞ approach to learning control systems , 1990 .

[6] Achim Ilchmann,et al. Non-identifier-based adaptive control of dynamical systems: a survey , 1991 .

[7] Eric Rogers,et al. Stability Analysis for Linear Repetitive Processes , 1992 .