摘要
以挖掘机为研究对象,建立挖掘机工作装置运动学和动力学数学模型,通过五次多项式插值方法将运动轨迹离散,采用MATLAB编写离散运动轨迹程序并将其计算结果与ADAMS仿真结果进行对比,最终确定动力学模型参数;其次,建立基于动力学模型的Simulink和ADAMS联合仿真模型,在动态控制中采用PD控制方法,进行轨迹规划联合仿真并试验。结果表明:基于动力学模型的运动轨迹控制方法可以有效避免始末位置和液压缸输出力变化的突变,采用动态控制中的PD控制方法,得到的收敛趋势相较于未优化的要收敛近50%;液压缸输出力变化稳定,其误差在5%附近,提升了自动挖掘运动轨迹控制精度。
Taking the excavator as the research object,the mathematical models of kinematics and dynamics of the excavator working device were established.The motion trajectory was discretized by using the 5-degree polynomial interpolation method.The discrete motion trajectory program was compiled using MATLAB and its calculation results were compared with the ADAMS simulation results,the dynamics model parameters were determined.The Simulink and ADAMS joint simulation model based on the dynamics model was established.In the dynamic control,the PD control method was used,and the trajectory planning joint simulation was conducted and tested.The results show that the motion trajectory control method based on the dynamic model can effectively avoid the sudden change of the starting and ending positions and the output force of the hydraulic cylinder.With the PD control method in dynamic control,the obtained convergence trend is nearly 50%higher than that of the unoptimized one.The output force of the hydraulic cylinder changes stably with an error of about 5%,which improves the control accuracy of the automatic excavation motion trajectory.
作者
周有明
刘凯磊
殷鹏龙
康绍鹏
强红宾
ZHOU Youming;LIU Kailei;YIN Penglong;KANG Shaopeng;QIANG Hongbin(School of Mechanical Engineering,Jiangsu University of Technology,Changzhou Jiangsu 213001,China;Sinomach Changlin Co.,Ltd.,Changzhou Jiangsu 213136,China;Research Center of Fluid Machinery Engineering and Technology,Jiangsu University,Zhenjiang Jiangsu 212013,China)
出处
《机床与液压》
北大核心
2024年第6期145-152,共8页
Machine Tool & Hydraulics
基金
国家自然科学基金青年科学基金项目(51805228)
江苏省高等学校自然科学基金项目(22KJB460021)
常州市科技支撑计划(社会发展)(CE20209002)
常州市领军型创新人才引进培育项目资助(CQ20210093)
江苏省研究生科研与实践创新计划项目(SJCX21_1323)。
关键词
挖掘机
运动学模型
轨迹规划
动力学模型
动态控制
excavator
kinematics model
trajectory planning
dynamics model
dynamic control