Composite Learning Enhanced Robot Impedance Control

• Published in: IEEE transaction on neural networks and learning systems Vol. 31; no. 3; pp. 1052 - 1059
• Main Authors: Sun, Tairen, Peng, Liang, Cheng, Long, Hou, Zeng-Guang, Pan, Yongping
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; composite adaptation; Computer simulation; Controllers; Convergence; Impedance; impedance control; Learning; learning control; parameter convergence; Parameter estimation; robot; Robot control; Robots; Stability criteria; Uncertainty
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2019.2912212

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.