Abstract:
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 in...Show MoreMetadata
Abstract:
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.
Published in: IEEE Transactions on Instrumentation and Measurement ( Volume: 73)