摘要
针对泵控系统滑模控制方面的研究,根据泵控系统的降阶数学模型中存在的未知项f(),再结合滑模控制算法设计基于RBF神经网络的滑模控制器。通过MATLAB/Simulink建立系统的仿真模型,然后进行位置指令仿真分析。研究结果表明:相比较PID控制器,基于RBF神经网络的滑模控制器获得了最小跟踪误差。在干扰条件下跟踪10 Hz频率与1 mm幅值的正弦位置信号,基于RBF神经网络的滑模控制器误差最小;施加干扰力后,控制器都出现了更大的跟踪误差,此时基于RBF神经网络构建的滑模控制器可以快速恢复跟踪误差。研究设计的基于RBF神经网络的泵控系统滑模控制器具有很好的跟踪精度和更强的鲁棒性,可以拓宽应用到其他机械传动领域。
Aiming at the research of sliding mode control of pump-controlled system, a sliding mode controller based on RBF neural network was designed according to the unknown term f() in the reduced order mathematical model of pump-controlled system, and combined with the sliding mode control algorithm. The simulation model of system was established by MATLAB/Simulink, and then the position command simulation analysis was carried out. The results show that compared with PID controller, the sliding mode controller based on RBF neural network achieves the minimum tracking error. When tracking sinusoidal position signals with 10 Hz frequency and 1 mm amplitude under interference conditions, the sliding mode controller based on RBF neural network has the smallest error. After the interference force is applied, larger tracking errors appear in all controllers, and the sliding mode controller based on RBF neural network can quickly recover the tracking errors. The sliding mode controller of pump control system based on RBF neural network designed has a good tracking accuracy and stronger robustness, which can be widely applied to other mechanical transmission fields.
作者
吴小俊
WU Xiaojun(Chongqing Automotive Power System Test Engineering Technology Research Center,Chongqing 401120,China)
出处
《机床与液压》
北大核心
2022年第8期129-132,共4页
Machine Tool & Hydraulics
基金
重庆市自然科学基金面上项目(cstc2020jcyj-msxmX0050)。
关键词
电液伺服系统
RBF神经网络
滑模控制
跟踪精度
鲁棒性
Electro-hydraulic servo system
RBF neural network
Sliding mode control
Tracking accuracy
Robustness
