摘要
采煤工作面液压支架的供液控制稳定性直接影响煤矿开采的安全与效率,现有控制方法在应对系统非线性和负载变化时存在局限。为此,提出了一种基于比例阀控制和神经网络的自适应反步控制方法,以提高液压支架油缸的控制性能,增强系统稳定性。首先,根据平衡力学建立系统的数值模型,并利用神经网络逼近系统的未知函数,简化了控制器设计。然后,采用反步法设计控制器和参数自适应律,以实现系统的稳定性并补偿参数不确定性。通过Matlab仿真验证,结果表明:在不同负载质量下,系统能够实现良好跟踪效果,跟踪误差收敛至零点附近的小邻域内,显示了该控制方法优异的跟踪性能和鲁棒性。该研究为矿用液压支架油缸的控制提供了理论支撑。
The stability of hydraulic support supply control in coal mining face directly affects the safety and efficiency of coal mining.Existing control methods have limitations in dealing with system nonlinearity and load changes.Therefore,an adaptive backstepping control method based on proportional valve control and neural network was proposed to improve the control performance of hydraulic support cylinders and enhance system stability.Firstly,a numerical model of the system was established based on equilibrium mechanics,and a neural network was used to approximate the unknown functions of the system,simplifying the controller design.Then,a backstepping method was used to design the controller and parameter adaptive law to achieve system stability and compensate for parameter uncertainty.Through Matlab simulation verification,the results showed that the system could achieve good tracking performance under different load masses,and the tracking error converges to a small neighborhood near the zero point,demonstrating the excellent tracking performance and robustness of the control method.This study provides theoretical support for the control of hydraulic support cylinders for mining.
作者
韩章明
HAN Zhangming(Ordos Shengxin Coal Industry Co.,Ltd.,Ordos 017000,China)
出处
《煤矿机电》
2025年第2期7-11,共5页
Colliery Mechanical & Electrical Technology
基金
国家自然科学基金面上项目(62022044)
辽宁省自然科学基金面上项目(2022-MS-356)。
关键词
矿用液压支架液压缸
反步控制
神经网络
MATLAB仿真
mining hydraulic support hydraulic cylinder
backstepping control
neural network
Matlab simulation
