A Method Toward Comprehensive Identification for Piezoelectric Dynamic System With Multimodal Hysteresis and Uncertainty Compensation

An accurate model of the piezoelectric actuator is important for the controller design to realize high-performance closed-loop control. However, the piezoelectric actuator exhibits inherent fast, slow modes, hysteresis nonlinearity, and system uncertainty at the same time. In this article, a two-stage modeling approach called Slow Fast Hysteresis with Uncertainty Compensation (SFHUC) for the piezoelectric dynamic system is proposed to achieve a high-accuracy model between input voltage and output displacement. The fast, slow modes and hysteresis nonlinearity of the piezoelectric actuator are estimated firstly based on a linear-linear-nonlinear cascade model. Then the system uncertainty of the piezoelectric actuator is compensated by a nonlinear artificial neural network, where the input is voltage signal, and the output is residual error of this linear-linear-nonlinear cascade model. The corresponding comprehensive identification algorithm including fast, slow modes, hysteresis nonlinearity, and system uncertainty is developed for accurate displacement prediction. Experimental results on a typical piezoelectric actuator demonstrate the effectiveness of the proposed comprehensive identification approach.