Two approaches to nonlinear systems optimal control by using neural networks

In this paper we present two methods based on neural networks (NN) for resolution of nonlinear systems optimal control with arbitrary performance index. We have used the minimum time index as an example. Both methods solve the optimal problem for a region of the state space by means of a multistage optimization through a NN chain. Each NN has a fully connected feedforward multilayer structure and the training algorithm for the NN chain is the backpropagation. The chain structure is different for each method, as well as the discretization procedure: classical and block pulse function.<>