| 引用本文: | 范家华,马磊,周攀,刘佳彬,周克敏.基于径向基神经网络的压电作动器建模与控制[J].控制理论与应用,2016,33(7):856~862.[点击复制] |
| FAN Jia-hua,MA Lei,ZHOU Pan,LIU Jia-bin,ZHOU Ke-min.Modeling and control of piezoelectric actuator based on radial basis function neural network[J].Control Theory and Technology,2016,33(7):856~862.[点击复制] |
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| 基于径向基神经网络的压电作动器建模与控制 |
| Modeling and control of piezoelectric actuator based on radial basis function neural network |
| 摘要点击 2859 全文点击 2238 投稿时间：2015-11-26 修订日期：2016-03-21 |
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| DOI编号 10.7641/CTA.2016.50940 |
| 2016,33(7):856-862 |
| 中文关键词 率相关 迟滞 RBF神经网络 压电作动器 Hammerstein模型 |
| 英文关键词 rate-dependent hysteresis RBF neural network piezoelectric actuator Hammerstein model |
| 基金项目 国家自然科学基金重点项目(61433011)资助. |
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| 中文摘要 |
| 针对压电作动器(piezoelectric actuator, PEA)的率相关迟滞非线性特性, 构建了Hammerstein模型对压电作
动器建模. 采用径向基(radial basis function, RBF)神经网络模型表征迟滞非线性, 利用自回归历遍模型(auto-regressive
exogenous, ARX)表征频率的影响, 并对模型参数进行了辨识. 此模型可以在信号频率在1 300 Hz范围内时,
较好地描述压电作动器的迟滞特性, 建模相对误差为1:99% 4:08%. 采用RBF神经网络前馈逆补偿控制, 结合PI反
馈的复合控制策略实现跟踪控制, 控制误差小于2:98%, 证明了控制策略的有效性. |
| 英文摘要 |
| For the rate-dependent hysteresis nonlinearity of piezoelectric actuators, a Hammerstein model is established.
Using a radial-basis-function (RBF) neural network to represent the hysteresis nonlinearity, an auto-regressive exogenous
(ARX) model to represent the impact of frequency, and parameter identification is also accomplished. The proposed model
describes the hysteresis characteristics of frequency ranged from 1 to 300 Hz of the signals, and the relative error is 1:99%
4:08%. A compound control strategy with RBF neural network feedforward inverse compensation and PI feedback is
utilized for position tracking control, and the relative error less than 2:98%. Validity of the control strategy is proved by
experimental results. |
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