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作 者:赵玮 ZHAO Wei(Mechanical Department of Chengde Petroleum College,Hebei Chengde 067000,China;School of Mechanical Engineering,Beijing University of Science and Technology,Beijing 100083,China)
机构地区:[1]承德石油高等专科学校机械系,河北承德067000 [2]北京科技大学机械工程学院,北京100083
出 处:《机械设计与制造》2023年第8期139-147,共9页Machinery Design & Manufacture
基 金:承德科技创新创业苗圃项目—轮履串联式变径管道机器人设计与应用研究(CGX2017KMP0008)。
摘 要:为了解决传统归零神经网络(ZNN)存在的两个缺点:收敛时间趋于无限大且外部干扰的抑制停留在渐近收敛阶段,提出了一种超螺旋归零神经网络(Super-Twisting Zeroing Neural Network,ST-ZNN)多机械臂协调运动控制模型。首先建立了考虑外部干扰多机械臂的运动控制模型,然后提出了ST-ZNN设计框架,并证明了ST-ZNN在带有外部干扰的多机械臂协调运动控制中具备全局稳定性、有限时间收敛性和鲁棒性。最后,通过两个带有不同干扰的多机械臂协调运动控制仿真实例的对比分析可知两个实例中的收敛时间均为0.2s,该结果与理论证明推导结果一致,且在不同外部干扰条件下,收敛时间相同,另外,ST-ZNN模型产生的残留误差明显小于传统ZNN模型。验证了所提出的ST-ZNN模型具有有限时间收敛性和对外部干扰的鲁棒性。In order to solve the two shortcomings of traditional zeroing neural network(ZNN):the convergence time tends to be infinite and the suppression of external interference stays at the asymptotic convergence stage,a super twisting zeroing neural network(ST-ZNN)multi manipulator coordinated motion control model was proposed.Firstly,the motion control model of multi manipulators with external disturbances was established,and then the design framework of ST-ZNN was proposed.It was proved that ST-ZNN had global stability,finite time convergence and robustness in coordinated motion control of multi manipulators with external disturbances.Finally,through the comparative analysis of two simulation examples of coordinated motion control of multiple manipulators with different disturbances,it could be seen that the convergence time of the two examples was O.2s,which was consistent with the theoretical results,and the convergence time was the same under different external interference conditions.In addition,the residual error of ST-ZNN model was significantly smaller than that of traditional ZNN model.It is verified that the proposed ST-ZNN model has finite time convergence and robustness to external disturbances.
关 键 词:协调运动控制 归零神经网络 有限时间收敛 超螺旋 多机械臂
分 类 号:TH16[机械工程—机械制造及自动化] TP241[自动化与计算机技术—检测技术与自动化装置] TP183[自动化与计算机技术—控制科学与工程]
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