Genetic Algorithms for Satellite Launcher Attitude Controller Design

Abstract

For proper attitude control of space-crafts conventional optimal Linear Quadratic (LQ) controllers are designed via trial-and-error selection of the weighting matrices. This time consuming method is inefficient and usually results in a high order complex controller. Therefore, this work proposes a genetic algorithm (GA) for the search problem of the attitude controller gains of a satellite launcher. The GA's fitness function considers some control features as eigenstructure, control goals and constraints. According to simulation results, the search problem of controller parameters with evolutionary algorithms was faster than usual approaches and the designed controller reached all the specifications with satisfactory time responses. These results could improve engineering tasks by speeding up the design process and reducing costs.

Projeto de compensador LQR de atitude de veículo lançador de satélite através de uma abordagem híbrida genética – neuronal

“…Este trabalho considera um modelo dinâmico linear simplificado de um VLS, dado por (Silva et al, 2019), ilustrado na Figura 2, como segue abaixo:…”

“…Esta pesquisa é uma extensão do trabalho de (Silva et al, 2019) e propõe uma abordagem híbrida genética-neuronal para realizar a busca das matrizes de ponderação da Equação Algébrica de Riccati (EAR) que resultará em ganhos do controlador de atitude. O artigo está organizado da seguinte maneira.…”

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

“…A more recently derived rule of thumb is that for "small" parameter sets, the population size is effective if scaled with the number of parameters with 10 m, and for larger spaces the population size scales with ln m ðÞ [42], where the definition of large is different for each author. For a simple parameter set GA can be quite effective for optimization problems in many space based applications [23,24,28,[43][44][45][46][47] which stem from aeronautical control [20,22], and ground-based robotic systems [48][49][50].…”