无模型的自适应模型预测控制用于自动采矿卡车的轨迹跟踪
Feixiang Xu1,2, Qiuyang Zhang3, Junkang Feng3
1State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou 221116, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
本研究引入了自动采矿卡车的新控制框架,将模型预测控制 (MPC) 和无模型自适应控制 (MFAC) 结合起来. 这种方法显著提高了在具有挑战性的矿山地形上轨迹跟踪的准确性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 汽车工程 汽车工程
- 地质技术工程 地质技术工程
背景情况:
- 自动采矿卡车需要精确的轨迹跟踪,以实现高效运行.
- 现有的车辆动力学模型无法准确地代表在崎的露天矿山环境中复杂的轮胎-地面相互作用.
- 在传统控制方法中使用的线性模型导致跟踪性能降低.
研究的目的:
- 为自动采矿卡车开发一个先进的轨迹跟踪控制框架.
- 解决当前模型在捕捉复杂地形相互作用方面的局限性.
- 提高自动采矿车辆的整体跟踪性能和可靠性.
主要方法:
- 整合模型预测控制 (MPC) 与计算效率的热启动策略.
- 整合无模型自适应控制 (MFAC) 以实时补偿控制偏差.
- 在不同条件下通过 CarSim 和 MATLAB/Simulink 平台上的共同模拟进行验证.
主要成果:
- 拟议的MPC-MFAC框架显著提高了轨迹跟踪性能.
- 与LQR对照相比,显示了平均误差 (90.83%),最大误差 (15.05%) 和RMSE (71.93%) 的显著降低.
- 实现了MPC的仅2ms的计算时间,提高了控制器的效率.
结论:
- 新的综合控制框架有效地提高了自动采矿卡车的轨迹跟踪能力.
- 该方法在各种道路条件和驾驶场景中提供了强大的性能.
- 这种方法为提高自主采矿操作的效率和安全提供了一个有希望的解决方案.
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