Modeling, identification, and high-speed compensation study of dynamic hysteresis nonlinearity for piezoelectric actuator

IF 2.4 3区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY Journal of Intelligent Material Systems and Structures Pub Date : 2024-03-07 DOI:10.1177/1045389x231225492
Minrui Zhou, Zhihui Dai, Zhenhua Zhou, Xin Liu, Taishan Cao, Zhanhui Li
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Abstract

Hysteresis nonlinearity widely exists in the piezoelectric actuator (PEA), and the hysteresis nonlinearity has strong dynamic characteristics that lead to deterioration of tracking performance. To decrease the positioning error caused by hysteresis nonlinearity, a generalized Bouc-Wen (GBW) hysteresis model and its compensation method are proposed in this paper. First, based on the Bouc-Wen hysteresis model, two asymmetric terms and a second-order IIR filter are applied to describe the asymmetric hysteresis and high-frequency phase lag characteristics of PEA. Then, the model parameters with strong relevance to frequency variation are modified as frequency-dependent parameters. Meanwhile, based on the particle swarm optimization (PSO) algorithm, a novel parameter identification algorithm is designed for identifying the parameters of GBW hysteresis model. Then, an inverse feedforward controller is constructed based on the GBW hysteresis model, and a composite compensation control algorithm combining PID controller and repetitive controller is developed to reduce the unmodeled dynamics errors and unknown external disturbances. Finally, the comparison experiment results show that the accuracy and performance of the GBW model proposed in this paper are much better than the classical Bouc-Wen (CBW) model and the enhanced Bouc-Wen (EBW) model, and the developed compensation controller has excellent control performance and robustness.
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压电致动器动态滞后非线性的建模、识别和高速补偿研究
压电致动器(PEA)中广泛存在磁滞非线性,磁滞非线性具有很强的动态特性,会导致跟踪性能下降。为了减小磁滞非线性引起的定位误差,本文提出了广义布克-文(GBW)磁滞模型及其补偿方法。首先,基于 Bouc-Wen 磁滞模型,应用两个非对称项和一个二阶 IIR 滤波器来描述 PEA 的非对称磁滞和高频相位滞后特性。然后,将与频率变化密切相关的模型参数修改为频率相关参数。同时,基于粒子群优化(PSO)算法,设计了一种新颖的参数识别算法,用于识别 GBW 磁滞模型的参数。然后,基于 GBW 磁滞模型构建了反前馈控制器,并开发了结合 PID 控制器和重复控制器的复合补偿控制算法,以减少未建模的动力学误差和未知的外部干扰。最后,对比实验结果表明,本文提出的 GBW 模型的精度和性能远优于经典布克文(CBW)模型和增强布克文(EBW)模型,所开发的补偿控制器具有优异的控制性能和鲁棒性。
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来源期刊
Journal of Intelligent Material Systems and Structures
Journal of Intelligent Material Systems and Structures 工程技术-材料科学:综合
CiteScore
5.40
自引率
11.10%
发文量
126
审稿时长
4.7 months
期刊介绍: The Journal of Intelligent Materials Systems and Structures is an international peer-reviewed journal that publishes the highest quality original research reporting the results of experimental or theoretical work on any aspect of intelligent materials systems and/or structures research also called smart structure, smart materials, active materials, adaptive structures and adaptive materials.
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