磁滞
执行机构
振幅
人工神经网络
控制理论(社会学)
压电
材料科学
计算机科学
控制(管理)
声学
物理
人工智能
凝聚态物理
量子力学
作者
J. Z. Zhang,Yiming Fei,Jiangang Li,Yanan Li
出处
期刊:IEEE Transactions on Industrial Electronics
[Institute of Electrical and Electronics Engineers]
日期:2024-03-26
卷期号:71 (11): 14875-14885
标识
DOI:10.1109/tie.2024.3376802
摘要
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.
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