|dc.description.abstract||An intelligent complementary sliding-mode control (ICSMC) system using a recurrent wavelet-based Elman neural network (RWENN) estimator is proposed to control the mover position of a linear ultrasonic motors (LUSMs)-based X-Y-Ө motion control stage and to achieve high precise position control with robustness in this study. First, the structure and operating principles of the LUSM were introduced. Since the motor parameters are highly non-linear and time-varying due to increase in temperature and change in operating conditions, the exact mathematical model of the LUSM is very difficult to obtain. Moreover, the control accuracy of the LUSM is influenced easily by the existence of uncertainties, which usually comprises system parameter variations, external disturbances, cross-coupled interference and friction force. In order to develop high performance and robust position control systems for LUSMs-based X-Y-Ө motion control stage under the occurrence of the uncertainties, five control systems including Elman neural network (ENN) control, recurrent wavelet-based Elman neural network (RWENN) control, sliding-mode control (SMC), complementary sliding-mode control (CSMC), and intelligent complementary sliding-mode control (ICSMC) systems, are proposed. Furethermore, to demonstrate the different control performances of various control systems, the circle, butterfly contours and sinusoid, trapezoid trajectories are designed for X-Y axes and Ө-axis, recepectively, using NURBS curve interpolator. Finally, some experimental results of various contours and trajectories tracking show that the proposed ICSMC owns the best control performance and robustness compared with the ENN, RWENN, SMC and CSMC systems.