Motion control with deadzone estimation and compensation using GRNN for TWUSM drive system
The traveling wave ultrasonic motor (TWUSM) drive has a strong nonlinearity, which varies with driving conditions and possesses variable deadzone in the control input associated with the phase difference of applied two-phase AC driving voltages. This deadzone is a problem as an accurate positioning actuator for industrial application, and it is important to eliminate the deadzone in order to improve the control performance. To overcome the above problems, a new motion control scheme with deadzone estimation and compensation using generalized regression neural network (GRNN) is proposed in this paper to improve the control performance of the TWUSM drive system. One of them is to approach the nonlinearity deadzone part of the TWUSM drive system, and another one with extension form is to approach the inverse of nonlinearity deadzone to realize the dynamic compensation. Once the nonlinearity of deadzone is compensated exactly by its inverse compensation, the whole TWUSM drive system can be treated as linear, and the estimated parameters of the linear system can be adopted to design a controller for a desired reference command.
traveling wave ultrasonic motor generalized regression neural network