A Fast Off-Line Learning Approach to Rejection of Periodic Disturbances

I.-J. Ha, J.-H. Yeom, J.-K. Jang, and J.-W. Park (Korea)


Disturbance rejection, periodic disturbance, off-line learn ing control, iterative update.


The recently-developed off-line learning control ap proaches for the rejection of periodic disturbances utilize the specific property that the learning system tends to os cillate in steady state. Unfortunately, the prior works have not clarified how closely the learning system should ap proach the steady state to achieve the rejection of periodic disturbances to satisfactory level. In this paper, we address this issue extensively for the class of linear systems. We also attempt to remove the effect of other aperiodic distur bances on the rejection of the periodic disturbances effec tively. In fact, the proposed learning control algorithm can provide very fast convergence performance in the presence of aperiodic disturbance. The effectiveness and practicality of our work is demonstrated through mathematical perfor mance analysis as well as various simulation results.

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