Jin Zhong, Gong Mingde, Zhao Dingxuan, Zhang Yue, Liu Wenbin, Zhu Jie, Su Bin
School of Mechanical Engineering, Yanshan University, Qinhuangdao, 066004, China.
Guangxi Liugong Machinery Co. Ltd., Liuzhou, 545027, China.
Heliyon. 2024 Apr 30;10(9):e29915. doi: 10.1016/j.heliyon.2024.e29915. eCollection 2024 May 15.
The control precision of the working device has always been a challenging aspect in unmanned excavator research due to the adoption of a triangular drive mode and a complex hydraulic system in the working mechanism. The article focuses on the research of autonomous control for the downward motion of a robotic arm in an unmanned excavator equipped with a regeneration valve. The study aims to achieve precise tracking of fast movement trajectories during operator manipulation, utilizing Model Predictive Control (MPC). Furthermore, the exceptional disturbance rejection capability of the MPC algorithm is demonstrated through interference application. A comprehensive model considering mechanical, hydraulic, and electrical factors is established for the excavator boom. The MPC algorithm is applied to achieve precise control over the boom descent process, providing a foundation for motion control in unmanned excavators. This article presents a theoretical analysis to elucidate the robustness principle of MPC in the descent control of uncertain dynamic arms. By incorporating real parameters, we successfully track predetermined planned paths at different speeds and validate them on a 20-ton hydraulic excavator. The results demonstrate that the MPC control algorithm accurately manipulates the boom descent motion while exhibiting excellent disturbance rejection performance. Compared to PID control algorithms, MPC offers wider target adaptability range and better disturbance rejection performance, making it suitable for rapid application in controlling working devices of unmanned excavators.
由于工作机构采用三角驱动模式和复杂的液压系统,工作装置的控制精度一直是无人挖掘机研究中的一个具有挑战性的方面。本文重点研究了配备再生阀的无人挖掘机中机械臂向下运动的自主控制。该研究旨在利用模型预测控制(MPC)在操作员操作期间实现快速运动轨迹的精确跟踪。此外,通过施加干扰展示了MPC算法卓越的抗干扰能力。针对挖掘机动臂建立了一个考虑机械、液压和电气因素的综合模型。应用MPC算法实现对动臂下降过程的精确控制,为无人挖掘机的运动控制提供了基础。本文进行了理论分析,以阐明MPC在不确定动态臂下降控制中的鲁棒性原理。通过纳入实际参数,我们成功地在不同速度下跟踪预定的规划路径,并在一台20吨液压挖掘机上进行了验证。结果表明,MPC控制算法能够精确地操纵动臂下降运动,同时表现出优异的抗干扰性能。与PID控制算法相比,MPC具有更宽的目标适应范围和更好的抗干扰性能,使其适合在无人挖掘机工作装置控制中快速应用。