液压支架顶梁位姿调控仿真分析

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英文篇名：Simulation analysis of roof beam position and attitude control of hydraulic support 作者：任怀伟 ; 李帅帅 ; 李勰 ; 付庄 英文作者：REN Huaiwei;LI Shuaishuai;LI Xie;FU Zhuang;Coal Mining and Designing Department,Tiandi Science and Technology Co.,Ltd.;Coal Mining Branch,China Coal Research Institute;School of Mechanical Engineering,Shanghai Jiao Tong University; 关键词：液压支架 ; 稳态支护 ; 顶梁位姿调控 ; 顶梁高度调节 ; 顶梁姿态角调节 ; 自适应调控 ; RBF神 英文关键词：hydraulic support;;stable support;;roof beam position and attitude control;;roof beam height control;;roof beam attitude angle control;;self-adaptive control;;RBF neural network control 中文刊名：工矿自动化 英文刊名：Industry and Mine Automation 机构：天地科技股份有限公司开采设计事业部;煤炭科学研究总院开采研究分院;上海交通大学机械与动力工程学院; 出版日期：2019-10-11 15:18 出版单位：工矿自动化 年：2019 期：10 基金：国家重点研发计划项目(2017YFC0603005);; 国家自然科学基金面上项目(518741774) 语种：中文; 页：14-19 页数：6 CN：32-1627/TP ISSN：1671-251X 分类号：TD355.4

摘要

针对液压支架稳态支护对液压支架控制器自适应控制快速性和强抗干扰能力要求,以ZY10800/28/63支顶掩护式液压支架为研究对象,建立了液压支架运动学模型和阀控液压缸液压系统传递函数,设计了一种包含速度补偿的RBF神经网络控制器,基于该控制器提出了一种液压支架顶梁位姿调控系统,通过控制立柱和平衡千斤顶长度,保证支护状态下液压支架顶梁高度和姿态角稳定。在Matlab/Simulink中建立了液压支架顶梁位姿调控系统仿真模型并进行实验,结果表明:RBF神经网络控制器对阶跃信号的响应时间短,未出现超调现象;在施加顶梁冲击力情况下,RBF神经网络控制器较PID控制器和滑模控制器具有更快的响应速度、更小的超调量和更强的抗干扰能力,能维持支架顶梁位姿处于目标位置,具有良好的稳定性。
        For rapidity and strong anti-interference requirements of self-adaptive control of hydraulic support controller under stable support state,a kinematics model of hydraulic support and a transfer function of hydraulic system in valve-controlled hydraulic cylinder were built taking ZY10800/28/63 roofstanding and shielding hydraulic support as a research object,and a RBF neural network controller including speed compensation was designed.Based on the controller,a roof beam position and attitude control system of hydraulic support was proposed,which ensured stability of roof beam height and attitude angle of hydraulic support under supporting condition through controlling length of column cylinder and equilibrium jack.A simulation model of the system was built in Matlab/Simulink and experiments were taken out.The experimental results show that the RBF neural network controller has short response time to step signal and no over-shoot.When impact force is loaded on roof beam,the RBF neural network controller has faster response speed,smaller over-shoot and stronger anti-interference performance than PID controller and sliding mode controller,which can keep roof beam position and attitude in target value with good stability.

引文

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