An optimizing design strategy for multiple model adaptive estimation and control

A method is proposed for designing multiple model adaptive estimators to provide combined state and parameter estimation in the presence of an uncertain parameter vector. It is assumed that the parameter varies over a continuous region and a finite number of constant gain filters are available for the estimation. The estimator elemental filters are shown by minimizing a cost functional representing the average state prediction error autocorrelation, with the average taken as the true parameter ranges over the admissible parameter set. An analogous method is proposed for designing multiple model adaptive regulators to provide stabilizing control in the presence of an uncertain parameter vector by minimizing a cost functional representing the average regulation error autocorrelation, with the average taken as the true parameter ranges over the admissible parameter set. An example is used to demonstrate the improvement in performance over previously accepted design methods