Precision control of piezoelectric actuated mechanism in scanning tunneling microscope

Piezoelectric actuators are popularly applied in precision positioning mechanism in Scanning Tunneling Microscope due to its advantage of nanometer resolution. Accurate tracking is difficult to be achieved owing to the intrinsic nonlinear hysteresis of piezoelectric ceramics. An adaptive control scheme combining neural network with traditional PID controller is proposed to improve the performance of precision mechanism in STMs. The mathematical model with hysteresis parameters is given. The displacement in next step is predicted by an estimator, and mechanical coefficient is identified on-line. Parameters in the traditional PID controller are modified through adjusting the weight values in the neural network with the function of self-learning and adaptability. Some experiments are made to validate the effectiveness of the proposed method. The results show that high performance dynamic characteristics are achieved, and the system transient response is improved in comparison with the traditional PID control.