STATE-SPACE PREDICTIVE CONTROL

This paper deals with a predictive control strategy based on state-space models. Important issues concerning inherent model identification and optimal control computation are briefly discussed. Predictive control relies heavily on a model with satisfactory predictive capabilities. An off-line identification procedure must be accomplished to obtain a proper model structure and a parameter set, which is required for on-line adjustment. The control calculation is based on a general performance index and parameterization of the control variables in a nonlinear model, which includes the relevant constraints. This results in a finite-dimensional optimization problem which can be repetitively solved on-line. Simulation studies on two very different, typical industrial processes are presented. The simulations show that this MPC technique offers a major improvement in the control of many industrial processes.