DESIGN OF A MULTIOBJECTIVE PREDICTIVE CONTROLLER FOR MULTIVARIABLE SYSTEMS

In this paper, a strategy for automatic tuning of decentralized predictive controller synthesis parameters based on multiobjective optimization for multivariable systems is proposed. This strategy integrates the genetic algorithm to generate the synthesis parameters (the prediction horizon, the control horizon and the cost weighting factor) making a compromise between closed loop performances (the overshoot, the variance of the control and the settling time). A simulation example is presented to illustrate the performance of this strategy in the on-line adjustment of generalized predictive control parameters.