Development of a Recurrent Fuzzy CMAC With Adjustable Input Space Quantization and Self-Tuning Learning Rate for Control of a Dual-Axis Piezoelectric Actuated Micromotion Stage

This paper proposes a recurrent fuzzy cerebellar model articulation controller (RFCMAC) for a dual-axis micromotion stage powered by piezoelectric actuators. The proposed RFCMAC employs an adjustable input space quantization method with a learning rate capable of self-tuning. The proposed RFCMAC is aimed at dealing with the adverse effects due to unknown hysteresis nonlinearity, modeling uncertainty, and external disturbance that are commonly found in the piezoelectric actuator systems. The proposed RFCMAC mainly focuses on overcoming the drawbacks of the conventional CMAC control scheme when used to control a piezoelectric actuator system. In particular, the adjustable input space quantization method uses a simple repartitioning decision function to determine an appropriate input space quantization. In addition, the self-tuning law for the learning rate of the proposed RFCMAC is derived based on the discrete-time Lyapunov function so that convergence and faster learning can be guaranteed. Finally, in order to validate the effectiveness of the proposed control methodology, several contour-following experiments were conducted on a dual-axis piezoelectric actuated micromotion stage. Experimental results show that the proposed control scheme is feasible, and its performance is superior to that of the conventional CMAC control scheme.