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

An accurate model of the piezoelectric actuator (PEA) is important for the controller design to realize high-performance closed-loop control. However, the PEA exhibits inherent fast, slow modes (SMs), 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, SMs and hysteresis nonlinearity of the PEA are estimated first based on a linear-linear-nonlinear cascade model. Then the system uncertainty of the PEA is compensated by a nonlinear artificial neural network, where the input is the voltage signal and the output is the residual error of this linear-linear-nonlinear cascade model. The corresponding comprehensive identification algorithm including fast, SMs, hysteresis nonlinearity, and system uncertainty is developed for accurate displacement prediction. Experimental results on a typical PEA demonstrate the effectiveness of the proposed comprehensive identification approach.