Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus, the purpose of this work is the automatic adjustment of the MGPC synthesis by simultaneously minimizing a set of closed loop performances (the overshoot and the settling time for each output of the MIMO system). First, we adopt the Weighted Sum Method (WSM), which is an aggregative method combined with a Genetic Algorithm (GA) used to minimize a single criterion generated by the WSM. Second, we use the NonDominated Sorting Genetic Algorithm II (NSGA-II) as a Pareto method and we compare the results of both the methods. The performance of the two strategies in the adjustment of multivariable predictive control is illustrated by a simulation example. The simulation results confirm that a multiobjective, Pareto-based GA search yields a better performance than a single objective GA.

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.

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.

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