Zhou Xiaochong, Zhang Lue, Yang Zhan, Sun Lining
Jiangsu Provincial Key Laboratory of Advanced Robotics, School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215123, China.
Micromachines (Basel). 2021 Jan 15;12(1):86. doi: 10.3390/mi12010086.
In the fast tool servo (FTS) system for microstructure surface cutting, the dynamic voltage hysteresis of piezoelectric actuators (PEAs) and the cutting force produced in the manufacturing affect the driving accuracy and the cutting performance. For a multi-input-single-output (MISO) cutting system, in this paper, a dynamic hysteresis model based on a rate-dependent Prandtl-Ishlinskii model is proposed. A backpropagation neural network (BPNN) is established to describe the cross-coupling effect between the applied voltage and external load. An inverse dynamic model is developed to compensate the nonlinearity of PEAs. The accuracy of the model and its inverse is discussed and the performance of the inverse feedforward compensator is validated through experiments.
在用于微结构表面切削的快速刀具伺服(FTS)系统中,压电致动器(PEA)的动态电压滞后以及制造过程中产生的切削力会影响驱动精度和切削性能。针对多输入单输出(MISO)切削系统,本文提出了一种基于速率相关的普朗特 - 伊什林斯基模型的动态滞后模型。建立了反向传播神经网络(BPNN)来描述施加电压与外部负载之间的交叉耦合效应。开发了逆动态模型以补偿压电致动器的非线性。讨论了模型及其逆模型的精度,并通过实验验证了逆前馈补偿器的性能。