Radial basis function neural network control of an XY micropositioning stage without exact dynamic model

In this paper, an adaptive neural sliding mode control based on radial basis function (RBF) neural network (NN) is implemented on a piezo-driven XY parallel micro-positioning stage for a sub-micron accuracy motion tracking control. The controller is designed to map the relationship between the sliding surface variable and voltage applied to piezoelectric actuator (PZT). Hence, neither a hysteresis model nor an exact system dynamic model is required for the control purpose. The weight parameters of RBF NN are updated by an adaptive adjustment law via on-line learning. The effectiveness of the realized controller over traditional PID controller is demonstrated through experimental studies and the influences of design parameter variations on control performances are evaluated as well. Experimental results show that the intelligent controller can compensate for the hysteresis effectively and lead to a well-performance motion tracking within a specific input rate.