基于模糊推理系统的神经网络前馈补偿用于直流伺服电机的位置跃变控制。

Fuzzy inference system enabled neural network feedforward compensation for position leap control of DC servo motor.

作者信息

Huang Zhiwen, Yan Yuting, Zhu Yidan, Shao Jiajie, Zhu Jianmin, Fang Dianjun

机构信息

School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China.

College of Design and Engineering, National University of Singapore, Singapore, Singapore.

出版信息

Sci Rep. 2024 Sep 6;14(1):20814. doi: 10.1038/s41598-024-71647-1.

DOI:10.1038/s41598-024-71647-1

PMID:39242769

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11379902/

Abstract

To improve dynamic performance and steady-state accuracy of position leap control of the direct current (DC) servo motor, a fuzzy inference system (FIS) enabled artificial neural network (ANN) feedforward compensation control method is proposed in this study. In the method, a proportional-integral-derivative (PID) controller is used to generate the baseline control law. Then, an ANN identifier is constructed to online learn the reverse model of the DC servo motor system. Meanwhile, the learned parameters are passed in real-time to an ANN compensator to provide feedforward compensation control law accurately. Next, according to system tracking error and network modeling error, an FIS decider consisting of an FI basic module and an FI finetuning module is developed to adjust the compensation quantity and prevent uncertain disturbance from undertrained ANN adaptively. Finally, the feasibility and efficiency of the proposed method are verified by the tracking experiments of step and square signals on the DC servo motor testbed. Experimental results show that the proposed FIS-enabled ANN feedforward compensation control method achieves lower overshoot, faster adjustment, and higher precision than other comparative control methods.

摘要

为提高直流（DC）伺服电机位置跃变控制的动态性能和稳态精度，本研究提出了一种基于模糊推理系统（FIS）的人工神经网络（ANN）前馈补偿控制方法。在该方法中，采用比例积分微分（PID）控制器生成基准控制律。然后，构建一个ANN辨识器来在线学习直流伺服电机系统的逆模型。同时，将学习到的参数实时传递给ANN补偿器，以精确提供前馈补偿控制律。接下来，根据系统跟踪误差和网络建模误差，开发了一个由FI基本模块和FI微调模块组成的FIS决策器，以调整补偿量并自适应地防止欠训练ANN产生的不确定干扰。最后，通过在直流伺服电机试验台上对阶跃信号和方波信号的跟踪实验，验证了所提方法的可行性和有效性。实验结果表明，所提基于FIS的ANN前馈补偿控制方法比其他对比控制方法具有更低的超调量、更快的调整速度和更高的精度。