Learning Strategy for Optimal Fuzzy Control

In this paper, a new scheme of fuzzy optimal control for discrete-time nonlinear systems based on the Pontryagin’s Minimum Principle is proposed. Using back propagation from the final co-state error and gradient descent, a method which allows training an adaptive fuzzy inference system to estimate values for the co-state variables converging to the optimal ones is devised. This approach allows finding a solution to the optimal control problem on-line by training the system, rather than by pre-computing it. Finally, this optimal approach is applied to nonlinear control benchmark problems. The results demonstrate the effectiveness of the approach towards achieving the optimal control objective.

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