Sun Tairen, Peng Liang, Cheng Long, Hou Zeng-Guang, Pan Yongping
IEEE Trans Neural Netw Learn Syst. 2020 Mar;31(3):1052-1059. doi: 10.1109/TNNLS.2019.2912212. Epub 2019 May 20.
The desired impedance dynamics can be achieved for a robot if and only if an impedance error converges to zero or a small neighborhood of zero. Although the convergence of impedance errors is important, it is seldom obtained in the existing impedance controllers due to robots modeling uncertainties and external disturbances. This brief proposes two composite learning impedance controllers (CLICs) for robots with parameter uncertainties based on whether a factorization assumption is satisfied or not. In the proposed control designs, the convergence of impedance errors, reflected by the convergence of parameter estimation errors and some auxiliary errors, is achieved by using composite learning laws under a relaxed excitation condition. The theoretical results are proven based on the Lyapunov theory. The effectiveness and advantages of the proposed CLICs are validated by simulations on a parallel robot in three cases.
当且仅当阻抗误差收敛到零或零的一个小邻域时，才能为机器人实现期望的阻抗动态特性。尽管阻抗误差的收敛很重要，但由于机器人建模的不确定性和外部干扰，在现有的阻抗控制器中很少能实现。本文基于是否满足因式分解假设，为具有参数不确定性的机器人提出了两种复合学习阻抗控制器（CLIC）。在所提出的控制设计中，通过在宽松激励条件下使用复合学习律，由参数估计误差和一些辅助误差的收敛所反映的阻抗误差收敛得以实现。基于李雅普诺夫理论证明了理论结果。通过在三种情况下对并联机器人进行仿真，验证了所提出的CLIC的有效性和优势。
