煤炭工程 ›› 2022, Vol. 54 ›› Issue (4): 156-161.doi: 10.11799/ce202204028

• 研究探讨 • 上一篇    下一篇

基于BP神经网络的掘进机行进轨迹跟踪控制研究

詹宇,方明正,胡锦辉,王迪妮,瞿圆媛   

  1. 中国矿业大学(北京)
  • 收稿日期:2021-05-19 修回日期:2021-06-23 出版日期:2022-04-15 发布日期:2022-07-06
  • 通讯作者: 瞿圆媛 E-mail:quyuanyuan2014@cumtb.edu.cn

Research on path tracking of road-header based on BP neural network

  • Received:2021-05-19 Revised:2021-06-23 Online:2022-04-15 Published:2022-07-06

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。

关键词: 掘进机, 轨迹跟踪, 纠偏控制, BP神经网络

Abstract: 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 moving line speed and turning angle speed as control inputs, the control law of path tracking scheduling is designed and simplified by using principle of Lyapunov and backstepping 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 which is 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. This proves that the tracking effect under the control of this model is good and has great potential for application.

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