Constrained robust predictive controller for uncertain processes modeled by orthonormal series functions

The present work focuses on robust predictive control (RPC) of uncertain pr ocesses and proposes a new approach based on orthonormal series function mo deling. In such unstructured modeling, the output signal is described as a weighted sum of orthonormal functions that uses approximative information a bout the time constant of the process. Due to an efficient uncertainty repr esentation, this kind of modeling is advantageous in the RPC context, even for constrained systems and processes with integral action. The stability o f the closed-loop system is guaranteed by the setting of sufficient conditi ons for the selection of the controller prediction horizon. Simulation resu lts are presented to illustrate the performance of this new RPC algorithm. (C) 2000 Elsevier Science Ltd. All rights reserved.