Algoritmos de inteligência computacional aplicados à otimização de sistemas de controle em acionamentos elétricos

• Autor(a) principal: Santos, Guilherme Fernandes dos
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Institucional da UFG
• Texto Completo: http://repositorio.bc.ufg.br/tede/handle/tede/12793
• topic: Otimização; Inteligência computacional; Meta-heurísticas; Algoritmos evolucionários multiobjetivo; Configuração de algoritmos; Acionamentos elétricos; Controle de velocidade; Controle de posição; Motor CC; Optimisation; Computational intelligence; Metaheuristics; Multi-objective evolutionary algorithms; Algorithm configuration; Electric drives; Speed control; Position control; DC motor; ENGENHARIAS::ENGENHARIA ELETRICA

This work presents the use of different computational intelligence methods applied to the tuning of a set of PI controllers for a DC motor drive with speed and position control. For position control, three closed control loops are used: armature current, speed and position. For speed control, only the armature current and speed loops are considered. In both cases, the outputs of the PI armature current and speed regulators are limited to the rated armature voltage and current, respectively. Then, it is possible to use higher gains for the controllers, what makes the system to respond faster. However, the windup phenomenon can arise. To avoid it, anti-windup circuits are also used and therefore, the system becomes non-linear. Because of this, an optimum tuning of the controllers may become a difficult task. In order to explore different possibilities, firstly, the speed and position control problems are formulated so that only one objective is minimised. Within this single-objective optimisation context, the PSO and SA algorithms are used to tune the controller parameters, them, the capability of each one is investigated when compared to each other. Multiobjective formulations are also explored to address three objectives simultaneously. In this part of the work, the multi-objective evolutionary algorithms NSGA-II and SPEA2 are used. All algorithms were implemented in MATLAB and the electric drive models were developed in the SIMULINK environment. Simulation results are presented showing that for the single-objective formulation, for both, the for speed and position control problems, the PSO algorithm outperformed the SA. For the multi-objective formulation, the SPEA2 algorithm presented better characteristics with respect to the Spread quality indicator in the only, when compared to NSGA-II. Furthermore, it’s shown to outperform the NSGA-II with respect to the Hypervolume indicator within the position control problem. A series of tests were carried out by varying the values of the main parameters setting for each algorithm. However, in most cases, no statistically significant advantage was observed. In general, the results presented demonstrate the ability of the algorithms to find optimal tuning for the controllers, either for the single-objective or the multiple and conflicting objectives problem.