多层神经网络改进Prandtl-Ishlinskii模型构建与压电迟滞补偿OA

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.