C32摩擦焊机的神经网络PID自适应控制研究  

Neural Network PID Adaptive Control of C32 Friction Welding Machine

作　　者：潘晓阳 黄崇莉[1] 郭强 薛旭东 PAN Xiaoyang;HUANG Chongli;GUO Qiang;XUE Xudong(School of Mechanical Engineering,Shaanxi University of Technology,Hanzhong Shaanxi 723000,China)

机构地区：[1]陕西理工大学机械工程学院,陕西汉中723000

出　　处：《机床与液压》2022年第2期55-60,共6页Machine Tool & Hydraulics

基　　金：陕西省科技厅科技研发项目(2020GY-120)。

摘　　要：针对C32连续摩擦焊机闭环控制系统的时变性、非线性控制精度较低以及稳定性较差等问题,研究一种BP神经网络与增量式PID控制器相结合的控制算法来提高系统性能。参考C32连续摩擦焊机实际运行参数,通过AMESim软件建立物理模型;结合Simulink建立神经网络PID自适应控制器进行联合仿真,建立电液力闭环控制系统。结果表明:C32摩擦焊机非线性控制系统在神经网络PID自适应控制下的响应速度、上升时间、控制精度以及稳定性皆优于传统PID控制;BP-PID在非线性控制中快速响应的同时消除了原有控制器的超调量,极大地提高了系统稳定性。Aiming at the time variability, low nonlinear control accuracy and poor stability of C32 continuous friction welding machine closed-loop control system, a control algorithm combining BP neural network and incremental PID controller was studied to improve the system performance.Referring to the actual operating parameters of the C32 continuous friction welding machine, a physical model was established through AMESim software;combined with Simulink, the neural network PID adaptive controller was established for joint simulation, and an electro-hydraulic closed-loop control system was established.The results show that the response speed, rise time, control accuracy and stability of C32 welding machine non-linear control system under neural network PID adaptive control are all better than the traditional PID control.The nonlinear control system controlled by BP-PID controller can make the system respond quickly and eliminate the overshoot of the original controller, which greatly improves the stability of the system.

关 键 词：PID自适应控制 BP神经网络 电液力闭环控制系统 AMESim-Simulink联合仿真 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]