A self-tuning controller for chemical processes

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.