Approximating optimal state feedback using neural networks

The training and usage of multilayer neural networks on discontinuous (e.g. bang-bang) feedback control problems are discussed. Training sets are created from optimal open loop trajectory information and a heuristic for trimming the base data set is presented. A priori knowledge about solution trajectories is seen to improve the training process.<>