Neural-Network-Based Nonlinear Model Predictive Control for Piezoelectric Actuators

doi:10.1109/TIE.2015.2455026

	Neural-Network-Based Nonlinear Model Predictive Control for Piezoelectric Actuators
	Cheng, Long; Liu, Weichuan; Hou, ZengGuang; Yu, Junzhi; Tan, Min
发表期刊	IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
	2015-12-01
卷号	62 期号:12 页码:7717-7727
文章类型	Article
摘要	Piezoelectric actuators (PEAs) have been widely used in nanotechnology due to their characteristics of fast response, large mass ratio, and high stiffness. However, hysteresis, which is an inherent nonlinear property of PEAs, greatly deteriorates the control performance of PEAs. In this paper, a nonlinear model predictive control (NMPC) approach is proposed for the displacement tracking problem of PEAs. First, a "nonlinear autoregressive-moving-average with exogenous inputs" (NARMAX) model of PEAs is implemented by multilayer neural networks; second, the tracking controlproblem is converted into an optimization problem by the principle of NMPC, and then, it is solved by the Levenberg-Marquardt algorithm. The most distinguished feature of the proposed approach is that the inversion model of hysteresis is no longer a necessity, which avoids the inversion imprecision problem encountered in the widely used inversion-based control algorithms. To verify the effectiveness of the proposed modeling and control methods, experiments are made on a commercial PEA product (P-753.1CD, Physik Instrumente), and comparisons with some existing controllers and a commercial proportional-integral-derivative controller are conducted. Experimental results show that the proposed scheme has satisfactory modeling and control performance.
关键词	Neuralnetworks Nonlinearautoregressive-moving-average With Exogenous Inputs (Narmax) Piezoelectric Actuator (Pea) Predictive Control
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TIE.2015.2455026
关键词[WOS]	PIEZOCERAMIC ACTUATOR ; INVERSE-FEEDFORWARD ; TRACKING CONTROL ; PREISACH MODEL ; HYSTERESIS ; COMPENSATION ; IDENTIFICATION
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 ; Instruments & Instrumentation
WOS类目	Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000365019500040
引用统计	被引频次：203[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/173211/10517
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	Cheng, Long,Liu, Weichuan,Hou, ZengGuang,et al. Neural-Network-Based Nonlinear Model Predictive Control for Piezoelectric Actuators[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2015,62(12):7717-7727.
APA	Cheng, Long,Liu, Weichuan,Hou, ZengGuang,Yu, Junzhi,&Tan, Min.(2015).Neural-Network-Based Nonlinear Model Predictive Control for Piezoelectric Actuators.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,62(12),7717-7727.
MLA	Cheng, Long,et al."Neural-Network-Based Nonlinear Model Predictive Control for Piezoelectric Actuators".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 62.12(2015):7717-7727.

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