Solving optimal control problems with neural network learning

Learning control methods require a large number of iterative trainings. Therefore, it is requested that the system makes full use of the information which the training process presents. The authors have developed a new learning control algorithm to self-organize general solutions for optimal control problem families. This paper discusses the algorithm theoretically. Then numerical simulations on the optimal control of a swing robot are discussed to demonstrate the significance of the method