Selective intermittent adaptive control of processes subject to large infrequent changes

In this paper, we consider the indirect adaptive control of processes subje ct to large, infrequent changes in the process parameters. We attempt to im prove the estimation and control by adopting a Gaussian-sum model that fits the characteristics of the parameter changes - better than the usual Gauss ian stochastic description leading to the least squares. The optimal estima tor that gives the minimum mean-squared error estimate of the parameters ha s the attractive properties of selectively tuning the estimator gain matrix for different types of parameter changes and being robust to measurement n oises. The optimal estimator, however, is computationally infeasible; hence a suboptimal approach that retains the attractive properties of the optima l estimator is proposed. We evaluate the performance of the proposed multiv ariable adaptive controller by applying it to chemical processes modeled by fundamental and input/output models, with single- and multiple-parameter v ariations. (C) 2000 Elsevier Science Ltd. All rights reserved.