Multi-objective Tuning of Generalized Predictive Controller: A Trade-off Between Performance and Robustness
This paper investigates the application of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to the Generalized Predictive Controller (GPC) parameter tuning. GPC stands out for using predictions of a future system behavior in order to evaluate an optimal control law according to certain performance criteria. GPC deals with multiple conflicting objectives such as minimizing set-point error, reducing variation of control actions and greater robustness. In this work, NSGA-II is applied to the problem of parameter tuning considering the trade-off between performance and robustness. A rule is deduced for choosing an adequate solution in the Pareto front which leads to overall best compromise between performance and robustness criteria. From the results we can observe that NSGA-II reaches satisfactory solutions that allows a better decision-making.
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