Memory-Enhanced Neural Network Control of Piezoelectric Actuators With a Rate-Amplitude-Dependent Hysteresis Model
J. Z. Zhang, Yiming Fei, Jiangang Li, Yanan Li
Abstract
Due to the presence of strong hysteresis nonlinearity, achieving robust and precise control of piezoelectric actuators (PEAs) is highly challenging. In this article, a novel rate-amplitude-dependent asymmetric Prandtl-Ishlinskii (RADAPI) model is proposed for modeling the hysteresis nonlinearity in PEAs and ultimately used for feedforward control based on its inverse model. Then, an uncertainty and disturbance estimator (UDE)-based controller using radial basis function (RBF) neural network is developed to address the issue of integral windup. To overcome the issue of passive knowledge forgetting, the selective memory recursive least squares weight update law is adopted. Moreover, the stability of the closed-loop system is demonstrated. A combined control scheme, incorporating RADAPI hysteresis inverse model feedforward compensation along with RBF-UDE based closed-loop feedback control, is devised to enhance the trajectory tracking accuracy of PEAs. Both theoretical analysis and experimental results are provided to validate the proposed control scheme.