Controle descentralizado para formação de sistemas multi-agente baseado em programação dinâmica adaptativa e polígonos regulares

• Ano de defesa: 2022
• Autor(a) principal: FERREIRA, Ernesto Franklin Marçal
• Orientador(a): FONSECA NETO, João Viana da
• Banca de defesa: FONSECA NETO, João Viana da , SOUZA, Francisco das Chagas de , SERRA, Ginalber Luiz de Oliveira , OLIVEIRA, Roberto Célio Limão de , SILVEIRA, Antonio da Silva
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: controle de formação; controle descentralizado; polígonos regulares; Sistemas Multi-Agentes; programação dinâmica adaptativa; fatoração QR.
• Palavras-chave em Inglês: decentralized control; formation control; multi-agent systems; regular polygons; adaptive dynamic programming; QR factorization.
• Área do conhecimento CNPq: Ciência da Computação; Ciências Exatas e da Terra
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/4213

In multi-agent systems, control is decentralized when decision-making is done by individual agents and not by a centralized unit that processes the states of all agents. The main difference in information/data processing when working with centralized control versus decentralized control, more precisely when solving the dynamic system, is the absence of the Laplacian matrix that presents the char- acteristics of interconnection between the agents of the system, with its absence, the system does not have complete information about the number of agents and the connection mode between them. In order to overcome this lack of informa- tion, it is proposed to use shapes that have well-defined characteristics (sides and angles) such as Regular Polygons. Due to the use of these geometric figures, the shape of the multi-agent system is defined by the number of agents and the de- sired distance between them. In this methodology, there is no need to exchange information between the agents, since each agent works only with the distances of its neighbors for the assembly of the shape, which are easily decomposed to the Cartesian plane. To ensure the optimality and adaptability of the control sys- tem, the controller gain update is obtained by heuristic dynamic programming approaches, which is executed in parallel to the main algorithm proposed in this thesis. This methodology is evaluated for a formation of three and four mobile agents.