光学精密工程2026，Vol.34Issue(2)：255-266,12.DOI:10.37188/OPE.20263402.0255
多层神经网络改进Prandtl-Ishlinskii模型构建与压电迟滞补偿
Improvement of a Prandtl-Ishlinskii model via multilayer neural network and hysteresis compensation of piezoelectric actuators
摘要
Abstract
Piezoelectric actuators(PEA)are widely used for micro-nano-positioning and precision manu-facturing due to their high resolution and rapid response.Inherent hysteresis nonlinearity affects control performance and restricts high-accuracy applications.To overcome the limitations of the classical Prandtl-Ishlinskii(P-I)model in representing complex nonlinear hysteresis phenomena,a multilayer neural net-work-enhanced P-I modeling approach was proposed.The method used a neural network to dynamically map the weights of Play operators while ensuring that the model remained invertible and physically inter-pretable.Bayesian regularization was adopted during training to improve the ability to fit nonlinear systems and enhance generalization.Based on the improved model,an inverse-model-based feedforward controller was designed and validated in real-time experiments.Experimental results show that the proposed feedfor-ward compensation reduces the normalized RMSE to 0.65%,0.76%,and 1.82%under triangular,sinu-soidal,and hybrid inputs,significantly outperforming the classical and its polynomial variants.The meth-od exhibits strong robustness across diverse input conditions and demonstrates good engineering applicabili-ty in complex hysteresis modeling and high-precision control.关键词
压电作动器/非线性建模/多层神经网络/迟滞补偿Key words
piezoelectric actuators/nonlinear modeling/multilayer neural network/hysteresis compensation分类
信息技术与安全科学引用本文复制引用
黄卫清,汪文晋,安大伟,张宸,陈晓婷,邹涛..多层神经网络改进Prandtl-Ishlinskii模型构建与压电迟滞补偿[J].光学精密工程,2026,34(2):255-266,12.基金项目
国家自然科学基金(No.52105177,No.52075108) （No.52105177,No.52075108）
广东省普通高校青年创新人才类项目(No.2021KQNCX067) （No.2021KQNCX067）
