当前位置: X-MOL 学术IEEE Trans. Ind. Electron. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Adaptive Parameter Estimation and Control Design for Robot Manipulators with Finite-Time Convergence
IEEE Transactions on Industrial Electronics ( IF 7.7 ) Pub Date : 2018-10-01 , DOI: 10.1109/tie.2018.2803773
Chenguang Yang , Yiming Jiang , Wei He , Jing Na , Zhijun Li , Bin Xu

For parameter identifications of robot systems, most existing works have focused on the estimation veracity, but few works of literature are concerned with the convergence speed. In this paper, we developed a robot control/identification scheme to identify the unknown robot kinematic and dynamic parameters with enhanced convergence rate. Superior to the traditional methods, the information of parameter estimation error was properly integrated into the proposed identification algorithm, such that enhanced estimation performance was achieved. Besides, the Newton–Euler (NE) method was used to build the robot dynamic model, where a singular value decomposition-based model reduction method was designed to remedy the potential singularity problems of the NE regressor. Moreover, an interval excitation condition was employed to relax the requirement of persistent excitation condition for the kinematic estimation. By using the Lyapunov synthesis, explicit analysis of the convergence rate of the tracking errors and the estimated parameters were performed. Simulation studies were conducted to show the accurate and fast convergence of the proposed finite-time (FT) identification algorithm based on a 7-DOF arm of Baxter robot.

中文翻译:

有限时间收敛机器人机械手的自适应参数估计与控制设计

对于机器人系统的参数识别,现有的大部分工作都集中在估计的准确性上,但很少有文献关注收敛速度。在本文中,我们开发了一种机器人控制/识别方案,以提高收敛速度来识别未知的机器人运动学和动力学参数。优于传统方法,参数估计误差的信息被适当地集成到所提出的识别算法中,从而实现了增强的估计性能。此外,使用牛顿-欧拉(NE)方法建立机器人动力学模型,其中设计了一种基于奇异值分解的模型约简方法来解决NE回归器潜在的奇异性问题。而且,采用区间激励条件来放宽对运动学估计的持续激励条件的要求。通过使用李雅普诺夫综合,对跟踪误差的收敛速度和估计参数进行了显式分析。进行了仿真研究,以显示所提出的基于 Baxter 机器人 7 自由度臂的有限时间 (FT) 识别算法的准确和快速收敛。
更新日期:2018-10-01
down
wechat
bug