Abstract
In the design of linear (model) predictive controllers (MPC), tuning plays a very important role. However, there is a problem not yet fully resolved: how to determine the best strategy for the selection of the optimal tuning parameters in order to obtain good performance with a large feasibility region, but maintaining a low computational load of the control algorithm? Because these objectives determine the proper functioning of the controller and are committed to each other, adjusting the controller parameters becomes a difficult task. The main contribution of this paper is to revise a method that uses the Nondominated Sorting Genetic Algorithm II (NSGA-II) for the parameter selection of a predictive control algorithm that has been parameterized with Laguerre functions (LOMPC) in order to explore the efficiency and provide statistical significance of the algorithm. Numerical simulations show that NSGA-II is a useful tool to obtain consistently good solutions for the selection of MPC tuning parameters.
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Gutiérrez-Urquídez, R.C., Valencia-Palomo, G., Rodríguez-Elías, O.M., López-Estrada, F.R., Orrante-Sakanassi, J.A. (2019). On the Selection of Tuning Parameters in Predictive Controllers Based on NSGA-II. In: Trujillo, L., Schütze, O., Maldonado, Y., Valle, P. (eds) Numerical and Evolutionary Optimization – NEO 2017. NEO 2017. Studies in Computational Intelligence, vol 785. Springer, Cham. https://doi.org/10.1007/978-3-319-96104-0_7
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