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

Author

Zanchetta, Pericle

Author_Institution

Univ. of Nottingham, Nottingham, UK

fYear

2011

fDate

14-15 Oct. 2011

Firstpage

70

Lastpage

75

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.

Keywords

active filters; cost optimal control; finite state machines; genetic algorithms; power filters; predictive control; FS-MPC; Matlab-Simulink simulation test; cost function; finite state model predictive control; genetic algorithm multiobjective optimization approach; heuristic multiobjective optimization; shunt active power filter; Active filters; Cost function; Minimization; Switches; Voltage control;

fLanguage

English

Publisher

ieee

Conference_Titel

Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2011 Workshop on

Conference_Location

Munich

Print_ISBN

978-1-4577-1912-7

Type

conf

DOI

10.1109/PRECEDE.2011.6078690

Filename

6078690