煤矿综掘悬臂式掘进机行走、纠偏及截割精准智能测控原理与方法

In view of the difficulties in obtaining coal and rock properties, equipment postures, and operating condition parameters in the process of coal mines' fully-mechanized excavation work, there are various challenges, such as accurate and reliable measurement and control, and safe and efficient formation of sections under the circumstances where the floor is inclined and complex and load is unknown and suddenly changed. Aiming at the problem, this project studies the principle and method of accurate intelligent measurement and control for walking, deviation rectification and cutting of boom-type roadheader in fully-mechanized coal mining. The research expects to obtain the following achievements: (1) putting forward the theory of roadheader precise positioning and pose calculation in closed and narrow space, (2) analyzing the position and pose response laws in cutting process, and formulating the control strategy of autonomous deviation rectification, (3) establishing the multi-objective optimization model of cutting trajectory, and proposing the adaptive control method for cutting. The target is to realize local autonomy and self-adaptive control of tunneling, which lays an important theoretical foundation for realizing robotization of the entire excavation face, parallel operation of tunneling, support, anchorage and carrying, visual remote control, and less or even unmanned humanization of the integrated tunneling process. The research results could fundamentally solve the problems of positioning, orientation, and adaptive control faced in the tunneling work of fully-mechanized excavation face, significantly improve the autonomy and intelligence degree of process control, expand the application scope of intelligent optimization and control theory and method in complex real processes, and provides a technical basis for the effective resolution of large-scale equipment robotization and intelligent control in the coal mine underground. Therefore, the project is of considerable significance in theory and application.

针对煤矿综掘作业过程中的煤岩性状、装备姿态、作业工况参数难以获取等问题，面向底板倾斜复杂、载荷未知突变的环境，及测控精准可靠、断面安全高效成型等挑战，本项目研究煤矿综掘悬臂式掘进机行走、纠偏及截割精准智能测控原理与方法。提出封闭狭长空间掘进机精准定位及位姿解算理论，分析截割过程位姿响应规律，制定自主纠偏控制策略，建立截割轨迹多目标优化模型，提出截割自适应控制方法。以实现掘进本地自主及自适应控制，从而为实现整个综掘工作面机器人化、掘支锚运并行作业、远程可视化遥控以及综掘过程少人甚至无人化奠定重要理论基础。其研究成果能够从根本上解决综掘工作面掘进过程面临的定位、定向与自适应控制难题，大幅度提高掘进过程控制的自主性与智能化程度，显著扩大智能优化与控制理论和方法在实际复杂过程的应用范围，并为煤矿井下大型装备机器人化和智能控制等的有效解决提供技术基础，具有重要的理论意义和实用价值。

针对煤矿综掘作业过程中的煤岩性状、装备姿态、作业工况参数难以获取等问题，面向底板倾斜复杂、载荷未知突变的环境，及测控精准可靠、断面安全高效成型等挑战，本项目研究煤矿综掘悬臂式掘进机行走、纠偏及截割精准智能测控原理与方法。提出了基于UWB的封闭狭长空间掘进机精准定位方法，推导了基于TOA、TDOA、TSOA三种定位模型的8种定位解算方法，得出了不同精度等级及空间变化对定位算法的误差影响规律，并提出了基于GIS的掘进机位姿可视化方法，实现了掘进机位置状态在巷道三维场景内的三维模型可视化监测；建立了掘进机截割过程位姿动力学耦合模型，分析得到了位姿响应规律、截割臂径向跳动规律，提出了基于掘进机行驶性能与巷道路况信息的掘进机自主纠偏规划与跟踪方法，并建立了掘进机支撑机构液压数学模型与液压实体模型，实现了掘进机支撑机构液压缸位移的精准实时控制；建立了复杂构造断面截割轨迹多目标优化模型，基于智能算法对截割轨迹进行了多约束多目标动态优化，设计了IPSO-BP控制器实现了对不同煤岩状态下截割载荷的智能精确识别，并提出了截割头转速智能分档控制、截割臂摆速控制以及联合截割智能控制方法。上述方法实现了掘进本地自主及自适应控制，从而为实现整个综掘工作面机器人化、掘支锚运并行作业、远程可视化遥控以及综掘过程少人甚至无人化奠定重要理论基础。项目培养博士生5名，硕士生7名。其中3名博士生、1名硕士生获得国家奖项金。共发表论文22篇，其中SCI检索8篇，EI检索9篇。获得授权发明专利5项。此外，2项发明专利处于实审状态。其研究成果能够从根本上解决综掘工作面掘进过程面临的定位、定向与自适应控制难题，大幅度提高掘进过程控制的自主性与智能化程度，显著扩大智能优化与控制理论和方法在实际复杂过程的应用范围，并为煤矿井下大型装备机器人化和智能控制等的有效解决提供技术基础，具有重要的理论意义和实用价值。