Automatic tuning of GPC synthesis parameters based on multi-objective optimization

Abstract

In this paper, a strategy for automatic tuning of predictive controller synthesis parameters based on multi-objective optimization (MOO) 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.