Implementation of self-tuning regulators with variable forgetting factors

A modified version of the self-tuning regulator having limited adaptability has been successfully implemented on a large-scale chemical pilot plant and the use of a variable forgetting factor with correct choice of information bound can avoid one of the major difficulties associated with constant exponential weighting of past data.

Abstract

A modified version of the self-tuning regulator having limited adaptability has been successfully implemented on a large-scale chemical pilot plant. The new algorithm uses a least-squares estimator with variable weighting of past data; at each step a weighting factor is chosen to maintain constant a scalar measure of the information content of the estimator. It is shown that, for nearly deterministic systems, such an approach enables the parameter estimates to follow both slow and sudden changes in the plant dynamics. Furthermore, the use of a variable forgetting factor with correct choice of information bound can avoid one of the major difficulties associated with constant exponential weighting of past data—namely, 'blowing-up' of the covariance matrix of the estimates and subsequent unstable control. Accordingly, the control algorithm described here may be well suited to the regulation of plants which would otherwise require periodic re-tuning of control constants.