Controle flexível de sistemas a eventos discretos utilizando simulação de ambiente e aprendizado por reforço

Detalhes bibliográficos
Ano de defesa: 2021
Autor(a) principal: Zielinski, Kallil Miguel Caparroz
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Tecnológica Federal do Paraná
Pato Branco
Brasil
Programa de Pós-Graduação em Engenharia Elétrica
UTFPR
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/25701
Resumo: Discrete Event Systems (DESs) are classically modeled as Finite State Machines (FSMs), and controlled in a maximally permissive, controllable, and nonblocking way using Supervisory Control Theory (SCT). While SCT is powerful to orchestrate events of DESs, it fail to process events whose control is based on probabilistic assumptions. In this research, we show that some events can be approached as usual in SCT, while others can be processed apart using Artificial Intelligence. We first present a tool to convert SCT controllers into Reinforcement Learning (RL) simulation environments, from where they become suitable for intelligent processing. Then, we propose a RL-based approach that recognizes the context under which a selected set of stochastic events occur, and treats them accordingly, aiming to find suitable decision making as complement to deterministic outcomes of the SCT. The result is an efficient combination of safe and flexible control, which tends to maximize performance for a class of DES that evolves probabilistically. Two RL algorithms are tested, State-Action-Reward-State-Action (SARSA) and N-step SARSA, over a flexible automotive plant control. Results suggest a performance improvement 9 times higher when using the proposed combination in comparison with non-intelligent decisions.