Feedback control of minimum-time optimal control problems using neural networks

This paper presents an optimal feedback controller capable of driving a non-linear control system from an arbitrary initial state to a fixed final state in minimum time. The controller is based on a feedforward multilayer neural network trained repeatedly using open-loop optimal control data which densely span the field of extremals of the non-linear system. The effectiveness of the controller is clearly demonstrated by a simulation on a two-link robot manipulator. The effect of sensor/actuator noise and parameter variation is also included to confirm the robustness of the controller.