Towards Robust Adaptive Least-Squares Parameter Estimation with Internal Feedback

Abstract

The new concepts of the ‘covariance matrix normalization’ and the ‘cascade’ structure of the adaptive least-squares estimator are shown to generalize and extend the use of internal information feedback in various robustness/alertness-oriented modifications to the standard ALS estimation algorithm. In the cascade estimation structure it is possible to ‘naturally’ stabilize, rather than maximize, the information matrix so that the covariance windup and blowup are effectively eliminated and the celebrated square root update of the covariance matrix is no longer needed Consequently, a new, ‘single-loop/cascade’ ALS MIMO estimation algorithm, enabling to effectively track both slow and jump parameter variations, is presented. The algorithm is coupled with a new, simple but robust predictive control scheme, offering a new adaptive MIMO control strategy.