Nonlinear controller with local approximator for active magneti bearing

Abstract eng:
Algorithm of locally weighted regression is presented in this contribution. Local approximator repeatedly uses the locally linear model based on least square method. Simulation study describing both behavior and learning of small active magnetic bearing control through nonlinear PD controller which uses parallel nonlinear compensation realized by given local approximator is further presented. The goal of on-line learning process is to minimize the mean square of control error. Obtained results are compared with results reached with nonlinear analytically designed PD controller. Here described controller is robust to high level noise and also able to work with relative high delay.

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