Predictive adaptive control of plants with online structural changes based on multiple models

The objective of this paper is to present a new algorithm to improve the adaptation rate of a predictive adaptive controller. For that sake, the possible plant dynamic outcomes are covered by a bank of models. Each model is used to re-initialize the adaptive controller every time there is a large change in dynamics. The contribution of the paper consists in the development of a procedure that includes additional models in the bank when found suitable according to defined criteria. The algorithm is demonstrated in a benchmark problem consisting of the position control of two masses coupled by a spring of varying stiffness. Copyright © 2008 John Wiley & Sons, Ltd.

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