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Quadratic optimal fuzzy control

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Detalhes bibliográficos
Resumo:One presents a fuzzy logic approach for optimal control of discrete-time nonlinear dynamic systems with a quadratic criterion. The approach is based on Pontryagin’s Minimum Principle. It uses back-propagation from the final co-state error and gradient descent to estimate a sequence of values for the co-state variables converging to the optimal ones. This implies that the controlled variables trajectories converge to the optimal ones. The estimator is implemented through an adaptive fuzzy inference system. The approach allows one to find a solution to the optimal control problem on-line by training the system, rather than by pre computing it. The use of an adaptive fuzzy inference system will allow to incorporate a priori knowledge about the optimal behavior of the co-state variables and to track changes in the system.
Autores principais:Salgado, Paulo
Outros Autores:Igrejas, Getúlio Paulo Peixoto; Garrido, Paulo
Assunto:Optimal control Fuzzy inference Pontryagin principle Fuzzy control
Ano:2006
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso aberto
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho
Descrição
Resumo:One presents a fuzzy logic approach for optimal control of discrete-time nonlinear dynamic systems with a quadratic criterion. The approach is based on Pontryagin’s Minimum Principle. It uses back-propagation from the final co-state error and gradient descent to estimate a sequence of values for the co-state variables converging to the optimal ones. This implies that the controlled variables trajectories converge to the optimal ones. The estimator is implemented through an adaptive fuzzy inference system. The approach allows one to find a solution to the optimal control problem on-line by training the system, rather than by pre computing it. The use of an adaptive fuzzy inference system will allow to incorporate a priori knowledge about the optimal behavior of the co-state variables and to track changes in the system.