倾斜巷道掘进机纠偏运动分析与控制研究
Study on roadheader rectification running performance and control in the in-cline coalmine roadway
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摘要: 掘进机作为煤矿巷道掘进的核心机电设备,其自动行驶与纠偏是巷道截割前的重要步骤,因煤矿综掘工作面工况复杂,导致巷道底板潮湿、泥泞或凹凸不平,针对传统掘进机行驶性能与纠偏控制未考虑滑移及巷道倾角的问题,为准确计算煤矿巷道实际工况下掘进机纠偏运动学与动力学参数,建立了综合考虑巷道倾角与履带滑移的掘进机纠偏运动学模型,根据综掘巷道实际工况与履带车辆地面力学基本原理,基于微元法分析了掘进机履带阻力模型,得到了掘进机履带阻力与力矩方程。基于掘进机纠偏运动学方程与阻力模型,建立了倾斜巷道内掘进机纠偏行驶动力学模型,结合掘进机履带运动系统液压模型建立了掘进机纠偏控制运动传递函数模型。针对综掘巷道底板复杂工况、掘进机纠偏动力学模型及掘进机液压行走系统非线性特征,提出了基于神经网络PID的掘进机纠偏运动控制算法,可通过神经网络的非线性映射和自学习能力,实现控制参数的在线实时修正与调整,保证控制效果的最优性。通过Matlab搭建仿真模型,验证了所建立的纠偏动力学模型的准确性,并将神经网络PID控制与传统PID控制算法进行对比,验证了神经网络PID控制算法的有效性与优越性。并搭建了基于掘进机试验样机、模拟巷道与智能控制系统的综掘巷道模拟试验系统,验证了纠偏动力学模型的准确性,通过智能工控机基于神经网络实现PID控制参数的在线获取,实现掘进机纠偏控制的实时控制,通过与传统PID控制对比,验证了控制系统的可行性与优越性。Abstract: The boom-type roadheader is the key equipment in the coal mine roadway excavation, and the au-to-movement and auto-rectification of the roadheader are the key step in the roadway auto-cutting.In terms of the severe working condition of the fully mechanized excavation face, the roadway floor is full of humidity muddy and irregularity.Aimed on the problem in driving performance and rectification control without the consideration of track slippage and roadway angle, the kinematics and dynamic parameters could be determined under the severe working condition.At first, the rectification kinematic equations are obtained based on the incline roadway angle and track slippage.Given the actual working conditions and tracked vehicle terramechanics, the resistance model of the track are proposed by the infinitesimal method.The resistance and force moment equation are also obtained.Based on the kinematics and resistance model, the dynamic model of the roadheader rectification in the incline roadway is proposed.Combining the above model and the roadheader driving hydraulic model, the transfer function of the roadheader rectification is obtained.Aimed at the severe working condition, rectification dynamic model and the nonlinearity of the driving system, the roadheader rectification PID control method based on the neural network is proposed.The PID parameters of the control system could be corrected by the nonlinear mapping and self-learning of the neural network to ensure the optimality of the control parameters.Then the simulation model is built in Matlab to verify the rectification dynamic model and the validity of the control algorithm.Also the experimental system including the intelligent roadheader prototype, simulated roadway and intelligent control system is built to verify the rectification dynamic model and control algorithm.The control parameters could be calculated in the intelligent industrial control computer.The rectification control system is operated in the experimental system.