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New concepts of 'covariance matrix normalization' and the 'cascade structure' of the adaptive least-squares parameter estimator are shown to generalize and extend the use of internal information feeback 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 covariance zeroing and blowup are effectively eliminated and the celebrated square root update of the covariance matrix is no longer needed. Consequently, a new, partly heuristic ALS MIMO estimation algorithm, enabling to effectively track both slow and jump parameter variations, is presented. The algorithm is coupled with a simple but robust predictive control scheme, offering a new adaptive MIMO control strategy.
This paper is concerned with the stabilising constrained receding-horizon predictive control algorithm (CRHPC) for multivariable processes. The optimal inputprofile is calculated by means of a new method the purpose of witch is to avoid inverting usually ill-conditioned matrices. additionally, ralatively simple formulae for calculating free and forced output predictions for the ARX process model, as well as the analytical stabilising control law in the unconstained case are derived, without the necessity of solving a matrix Diophantine equation.
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