An Inversion-Free Predictive Controller for Piezoelectric Actuators Based on a Dynamic Linearized Neural Network Model

doi:10.1109/TMECH.2015.2431819

	An Inversion-Free Predictive Controller for Piezoelectric Actuators Based on a Dynamic Linearized Neural Network Model
	Liu, Weichuan; Cheng, Long; Hou, ZengGuan; Yu, Junzhi; Tan, Min
发表期刊	IEEE-ASME TRANSACTIONS ON MECHATRONICS
	2016-02-01
卷号	21 期号:1 页码:214-226
文章类型	Article
摘要	Piezoelectric actuators (PEAs) are widely used in high-precision positioning applications. However, the inherent hysteresis nonlinearity seriously deteriorates the tracking performance of PEAs. To deal with it, the compensation of the hysteresis by using its inverse model (called inversion-based) is the popular method in the literature. One major disadvantage of this method is that the tracking performance of PEAs highly relies on its inverse model. Meanwhile, the computational burden of obtaining the inverse model is overwhelming. In addition, the physical constraints of the input voltage of PEAs is hardly handled by the inversion-based method. This paper proposes an inversion-free predictive controller, which is based on a dynamiclinearized multilayer feedforward neural network (MFNN) model. By the proposed method, the inverse model of the inherent hysteresis is not required, and the control law can be obtained in an explicit form. By using the technique of constrained quadratic programming, the proposed method still works well when dealing with the physical constraints of PEAs. Moreover, an error compensation term is introduced to reduce the steady-state error if the dynamic linearized MFNN cannot approximate the PEA's dynamical model satisfactorily. To verify the effectiveness of the proposed method, experiments are conducted on a commercial PEA. The experiment results show that the proposed method has a satisfactory tracking performance even with high-frequency references. Comparisons demonstrate that the proposed method outperforms some existing results.
关键词	Dynamic Linearization Hysteresis Model Predictive Control (Mpc) Neural Network Modeling Piezoelectric Actuators
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TMECH.2015.2431819
关键词[WOS]	HYSTERESIS COMPENSATION ; VIBRATION COMPENSATION ; ITERATIVE CONTROL ; SYSTEMS ; DESIGN ; CREEP ; PIEZOACTUATORS ; IDENTIFICATION ; FEEDFORWARD ; STAGE
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61422310 ; Beijing Nova Program(Z121101002512066) ; 61370032 ; 61375102 ; 61225017 ; 61421004)
WOS研究方向	Automation & Control Systems ; Engineering
WOS类目	Automation & Control Systems ; Engineering, Manufacturing ; Engineering, Electrical & Electronic ; Engineering, Mechanical
WOS记录号	WOS:000372013900023
引用统计	被引频次：99[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11373
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Cheng, Long
作者单位	State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Liu, Weichuan,Cheng, Long,Hou, ZengGuan,et al. An Inversion-Free Predictive Controller for Piezoelectric Actuators Based on a Dynamic Linearized Neural Network Model[J]. IEEE-ASME TRANSACTIONS ON MECHATRONICS,2016,21(1):214-226.
APA	Liu, Weichuan,Cheng, Long,Hou, ZengGuan,Yu, Junzhi,&Tan, Min.(2016).An Inversion-Free Predictive Controller for Piezoelectric Actuators Based on a Dynamic Linearized Neural Network Model.IEEE-ASME TRANSACTIONS ON MECHATRONICS,21(1),214-226.
MLA	Liu, Weichuan,et al."An Inversion-Free Predictive Controller for Piezoelectric Actuators Based on a Dynamic Linearized Neural Network Model".IEEE-ASME TRANSACTIONS ON MECHATRONICS 21.1(2016):214-226.

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