基于PID的掘进机横摆速度智能控制

The Intelligent Control of Roadheaders Yaw Velocity Is Established Based on Neural Network PID Control Method

为实现掘进机横摆速度的有效控制,提出一种基于神经网络的PID智能调速系统。首先,利用MATLAB神经网络模块建立了掘进机横摆摆速v与煤岩坚固性系数f、截齿截深B的拟合模型,并选取了16组数据对拟合模型进行残差分析,结果显示拟合的横摆速度最大残差为0.005 m/min,证明了拟合模型的精准性。然后,在已建立的神经网络拟合模型基础上,基于PID控制技术建立了掘进机摆速的智能调速系统。最后,以煤岩坚固性系数为8、截割深度为0.6 m的工况为例,分析了煤岩坚固性系数f突变情况下智能调速系统的横摆速度。结果表明:掘进机的横摆速度能够快速、准确地随着煤岩坚固性的改变而自动调节。证明该智能调速系统的有效性和优越性,为掘进自动化的研究提供理论参考。

In order,,to realize the effective control of the yaw rate of mining machine,a PID intelligent speed control system based on neural network is proposed.Firstly,a MATLAB neural network module was used to establish a fitting model of the yaw rate of coal mining machinery and the coal rock solidity coefficient and the pick cutting depth, and 16 sets of data were used to perform residual analysis on the fitted model.The results showed the fitting.The maximum yaw rate residual is 0.005 m/min,which proves the accuracy of the fitting model.Then,based on the established neural network fitting model,based on PID control technology,art intelligent speed control system of the mining machine's pendulum speed was established.Finally,taking the coal rock's sturdiness coefficient of 8 and the cutting depth of 0.6m as an example,the yawing speed of the intelligent governor system in the case of abrupt change of the coal rock stability coefficient was analyzed.The results show that the yaw rate of the mining machine can be automatically and quickly adjusted with the change of the stability of coal and rock.Prove the effectiveness and superiority of the intelligent speed control system,provide theoretical reference for the study of mining automation.