Elman neural network–based identification of rate-dependent hysteresis in piezoelectric actuators. (April 2020)

• Record Type: Journal Article
• Authors: Zhao, Xinlong; Shen, Shuai; Su, Liangcai; Yin, Xiuxing
• Is Part Of: Journal of intelligent material systems and structures. Volume 31:Number 7(2020)
• Page Start: 980
• Page End: 989
• Publication Date: 2020-04
• Subjects: Improved dynamic hysteretic operator -- rate-dependent hysteresis -- Elman neural network; Smart materials -- Periodicals; Intelligent control systems -- Periodicals; Artificial intelligence -- Periodicals; Matériaux intelligents -- Périodiques; Commande intelligente -- Périodiques; Intelligence artificielle -- Périodiques; 620.11
• DOI: 10.1177/1045389X20905987
• Languages: English
• ISSNs: 1045-389X

Abstract

Rate-dependent hysteresis nonlinearity in piezoelectric actuators severely limits micro- and nanoscale system performance. It is necessary to establish a dynamic model to describe the full behavior of rate-dependent hysteresis. In this article, the Elman neural network–based hysteresis model is developed for piezoelectric actuators. An improved dynamic hysteretic operator is proposed to transform the multi-valued mapping of hysteresis into one-to-one mapping on a newly constructed expanded input space. Then, Elman neural network incorporated with the improved dynamic hysteretic operator is utilized to approximate the behavior of rate-dependent hysteresis. The combination of Elman neural network and the improved dynamic hysteretic operator can dually embody the dynamic property and is capable of fully extracting the characteristics of rate-dependent hysteresis. The experimental results are presented to illustrate the potential of the proposed modeling technique.