基于BP神经网络的掘进机行进轨迹跟踪控制研究  

Path tracking of road-header based on BP neural network

作　　者：詹宇 方明正 胡锦辉 王迪妮 瞿圆媛[1] ZHAN Yu;FANG Ming-zheng;HU Jin-hui;WANG Di-ni;QU Yuan-yuan(School of Mechanical Electronic and Information Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China)

机构地区：[1]中国矿业大学(北京)机电与信息工程学院,北京100083

出　　处：《煤炭工程》2022年第4期156-161,共6页Coal Engineering

基　　金：国家自然科学基金资助项目(61803374,51874308);中央高校基本科研业务费专项资金资助项目(8000150A087)。

摘　　要：根据井下掘进机行进特点,建立了掘进机履带式行走位姿偏差模型;以履带移动线速度和转向角速度作为路径跟踪控制输入量,利用李雅普诺夫稳定原则和反演法设计并简化了路径跟踪调度的控制律。利用BP神经网络实现对控制律中关键系数的动态优化更新,以实时补偿机身位姿相对于所设计轨迹的跟踪偏差。仿真结果表明,提出的基于BP神经网络的掘进机行进纠偏控制模型结构简单易实现,机身位姿偏差均能在有限的跟踪步骤内收敛为零且转速调整过程平稳,证明本模型控制下的轨迹跟踪效果良好。According to the working characteristics of underground road-header,the walking position and posture deviation model of a road-header is established.Taking the caterpillar linear velocity and turning angular velocity as control inputs,the control law of path tracking scheduling is designed and simplified using Lyapunov stability principle and inversion method.The BP neural network is used to update the dynamic optimization of the key factors in the control law to compensate the tracking deviation of the body position and posture from the designed track in real time.The simulation results show that the proposed working control model of road-header based on BP network is simple and easy to implement.The position and posture deviation of the road-header can converge to zero within a limited tracking step and the angular speed adjustment process is stable,indicating the favorable tracking effect under the control of the proposed model.

关 键 词：掘进机 轨迹跟踪 纠偏控制 BP神经网络 

分 类 号：TD421.5[矿业工程—矿山机电]