Control of dual-stage actuator system based on neural networks

In order to solve the contradiction between the large stroke and high precision which are usually required in micro-manipulation systems, the dual-stage actuator structure is adopted and the corresponding controller based on neural networks is proposed. PID self-tuning controller based on BP neural network is designed for the first actuator. The parameters can be adjusted adaptively to achieve the optimal value. The second actuator is controlled by radial-basis-function network forward control and PID feedback control, which enhance robustness of the control system. Finally, the effectiveness of this method is verified.