Enhancing ARX-Model Based MPC by Kalman Filter and Smoother
Abstract
This paper presents approaches of enhancing a model-based predictive controller by Kalman filter. 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 propose an algorithm of updating the output history using Kalman filter data to achieve predictions equivalent to those of the state-space model, thus overcoming inherent limitations of the ARX predictor. Finally, interesting relations of this algorithm to Kalman interval smoother are given.
