控制理论与应用2016，Vol.33Issue(7)：856-862,7.DOI:10.7641/CTA.2016.50940
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
Abstract
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.关键词
率相关/迟滞/RBF神经网络/压电作动器/Hammerstein模型Key words
rate-dependent/hysteresis/RBF neural network/piezoelectric actuator/Hammerstein model分类
信息技术与安全科学引用本文复制引用
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):856-862,7.基金项目
国家自然科学基金重点项目(61433011)资助 （61433011）
