Heuristic multi-objective optimization for cost function weights selection in finite states model predictive control

Pericle Zanchetta

doi:10.1109/precede.2011.6078690

Heuristic multi-objective optimization for cost function weights selection in finite states model predictive control

Pericle Zanchetta

https://doi.org/10.1109/precede.2011.6078690

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Publication Date: Oct 1, 2011

Citations: 70

Affiliation: University of Nottingham

#Finite States Model Predictive Control #Shunt Active Power Filter + Show 8 more

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Abstract

This research work investigates an automated and optimal procedure for the selection of the cost function weights in Finite States Model Predictive Control (FS-MPC). This is particularly useful where the cost function is composed by more variables and where other control parameters need to be carefully designed. A Genetic Algorithm (GA) multi-objective optimization approach is here proposed and tested on a case study represented by the FS-MPC of a Shunt Active Power Filter (SAF). The results of this weights optimization procedure are reported and discussed with the aid of Matlab-Simulink simulation tests.

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