Publicação
Quadratic optimal fuzzy control
| 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. Using back propagation from the final co-state error and gradient descent, a method is devised which allows for training an adaptive fuzzy inference system to estimate values for the co-state variables converging to the optimal ones. In turn this implies that the controlled variables trajectories also converge to the optimal ones. The approach allows finding a solution to the optimal control problem on-line, by training of the system, rather than by pre computing it. In particular, the use of an adaptive fuzzy inference system also will allow incorporating a priori knowledge about the optimal behavior of the co-state variable and track changes in the system. |
|---|---|
| Autores principais: | Salgado, Paulo |
| Outros Autores: | Igrejas, Getúlio; Garrido, Paulo |
| Assunto: | Fuzzy systems Optimal |
| Ano: | 2006 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| 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. Using back propagation from the final co-state error and gradient descent, a method is devised which allows for training an adaptive fuzzy inference system to estimate values for the co-state variables converging to the optimal ones. In turn this implies that the controlled variables trajectories also converge to the optimal ones. The approach allows finding a solution to the optimal control problem on-line, by training of the system, rather than by pre computing it. In particular, the use of an adaptive fuzzy inference system also will allow incorporating a priori knowledge about the optimal behavior of the co-state variable and track changes in the system. |
|---|