Applications of optimal control theory using artificial neural networks

This paper shows neural networks capabilities in optimal control applications of nonlinear dynamic systems. Our method is based on a classical method concerning the direct research of the optimal control using gradient techniques. We show that neural approach and backpropagation paradigm are able to solve efficiently equations relative to necessary conditions for an optimizing solution. We have taken into account the known capabilities of neural networks in approximation functions. And for dynamic systems, we have generalized the indirect learning of inverse model adaptive architecture that is capable of defining an optimal control in relation to a temporal criterion