Neural-Network-Based Nonlinear Model Predictive Control for Piezoelectric Actuators
Cheng, Long; Liu, Weichuan; Hou, ZengGuang; Yu, Junzhi; Tan, Min
2015-12-01
发表期刊IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
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
DOI10.1109/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
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/10517
专题复杂系统管理与控制国家重点实验室_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
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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