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A cautious self-tuning controller for chemical processes

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Abstract

An adaptive controller is presented that is able to perform effectively in the presence of nonlinearities and of model uncertainty typical of industrial processes. The controller incorporates a minimum variance part and a caution part. The latter is independent of the estimated model and becomes dominant in the case of a large mismatch between plant and estimated model. Application to a simulated nonlinear continuous stirred-tank reactor demonstrates its effectiveness, even when other adaptive controllers give unsatisfactory performance.

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