GENETIC ALGORITHMS AND PARTICLE SWITCHING OPTIMIZATION TO DEFINE THE MATRICES OF WEIGHT OF THE LINEAR QUADRATIC REGULATOR METHODOLOGY

Abstract

The linear quadratic regulation problem, its modern control strategy where controller parameters are found that minimize unwanted deviations through the configuration of the weight matrices Q and R; taking care of the tedious work done by the specialist in the optimization of the controller. However, there are no simple analytical methods that help the designer to define the values of these matrices, which are a function of the system, the control that is desired and the efforts of the control variables; being fundamental knowledge of the process on the part of the engineer. Classic approaches such as trial and error, Bryson's method, and pole allocation are labor intensive, time-consuming and do not guarantee the expected performance. This research proposed a methodology based on genetic algorithms and optimization by particle swarm to define the weight matrices Q and R. Achieving optimal controllers design easily, fast and with only knowing basically the system to control.

Keywords: Quadratic linear regulator, Q and R matrices, genetic algorithms, particle swarm optimization

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