Zhong Bowen, Liu Shilin, Wang Chenjun, Jin Ziqi, Sun Lining
College of Mechanical and Electrical Engineering, Soochow University, Suzhou 215137, China.
Materials (Basel). 2023 Mar 11;16(6):2271. doi: 10.3390/ma16062271.
The piezoelectric actuator has been widely used in modern precision cutting technology due to its fast response speed and high positioning accuracy. In recent years, with the development of precision technology, modern cutting requires higher and higher cutting accuracy and efficiency. Therefore, this paper proposes a feedforward control method based on the modified Bouc-Wen (MBW) model. Firstly, a novel asymmetrical modified Bouc-Wen model with an innovative form of shape control function is developed to model the hysteresis nonlinearity property of piezoelectric actuators. Then, a self-adaptive cooperative particle swarm optimization (PSO) algorithm is developed to identify the parameters of MBW model. The comparative evaluation reveals that the MBW model outperforms the classical Bouc-Wen (CBW) model by 66.4% in modeling accuracy. Compared with traditional PSO algorithm, the self-adaptive cooperative PSO algorithm can obtain minimum fitness in parameter identification. Furthermore, the feedforward control strategy is realized to improve the position tracking accuracy. A position tracking experiment verifies that the feedforward control strategy improves the tracking accuracy of piezoelectric actuators significantly compared with the open-loop control strategy.
由于压电致动器响应速度快、定位精度高,已在现代精密切割技术中得到广泛应用。近年来,随着精密技术的发展,现代切割对切割精度和效率的要求越来越高。因此,本文提出了一种基于改进的Bouc-Wen(MBW)模型的前馈控制方法。首先,开发了一种具有创新形状控制函数形式的新型非对称改进Bouc-Wen模型,以对压电致动器的滞后非线性特性进行建模。然后,开发了一种自适应协同粒子群优化(PSO)算法来识别MBW模型的参数。对比评估表明,MBW模型在建模精度上比经典Bouc-Wen(CBW)模型高出66.4%。与传统PSO算法相比,自适应协同PSO算法在参数识别中能够获得最小适应度。此外,实现了前馈控制策略以提高位置跟踪精度。位置跟踪实验验证了前馈控制策略与开环控制策略相比,显著提高了压电致动器的跟踪精度。