Abstract
This paper proposes a new model reference adaptive control algorithm which has good robustness properties in the presence of unmodeled plant dynamics. The new algorithm requires filtering of the plant input and output with low pass first order filters, prior to their use in the adaptive algorithms. In the case where the dominant transfer function of the plant has a relative degree n* = 1 a global convergence result has been proven in the presence of unmodeled dynamics. When n* > 1 the algorithm employs an adaptive law with normalized signals in order to improve robustness with respect to unmodeled dynamics. It is shown that two of the most crucial factors for robustness, the speed of adaptation and the magnitude of the estimated parameters relative to the speed of the parasitics can be adjusted using the normalized adaptive law.
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