An efficient algorithm for infinite-norm robust predictive controller with guaranteed stability

Predictive controllers that explicitly consider structured uncertainties in the model for calculating the control law are called robust predictive controllers (RPC). The present paper proposes and analyses a RPC algorithm based on the use of orthonormal basis functions (OBF) for modeling the process and on the solution of a min-maa: optimisation problem with an infinite norm cost function for deriving the control law. A strategy for reducing the computational burden of the algorithm is presented. This strategy allows to eliminate the exponential dependence of the number of constraints in the control law computation on the number of parameters in the model. It is shown that the robust stability of the closed loop system is guaranteed by the setting of sufficient conditions for the selection of the controller prediction horizon. Simulation results are presented to illustrate the performance of the proposed RPC-OBF algorithm. Copyright (C) 1998 IFAC.