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Abstract
An approach to enhancing a model-based predictive controller by Kalman filter is proposed. The controller uses an ARX process model and the structure of the controller is assumed fixed; some of its internal variables – past values of controlled variables (output history) are accessible and can be modified to achieve better performance in disturbance attenuation and noise rejection. We present an algorithm of updating the output history using Kalman filter to achieve predictions equivalent to those of the state-space model, thus overcoming the limitations of the ARX predictor. Interesting relations of this algorithm to Kalman interval smoother are given.
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