Tracking Control of Robot Manipulators with Unknown Models: A Jacobian-Matrix-Adaption Method

• Published in: IEEE transactions on industrial informatics Vol. 14; no. 7; pp. 3044 - 3053
• Main Authors: Chen, Dechao, Zhang, Yunong, Li, Shuai
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Complexity theory; Computer simulation; Input-output information; Jacobian-matrix-adaption (JMA); Manipulators; Path planning; Power system dynamics; Protocols; Robot arms; robot manipulators; Robots; Shortest path problem; Tracking control; unknown models; Vehicle dynamics; zeroing dynamics
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2017.2766455

Tracking control of robot manipulators is a fundamental and significant problem in robotic industry. As a conventional solution, the Jacobian-matrix-pseudo-inverse (JMPI) method suffers from two major limitations: one is the requirement on known information of the robot model such as parameter and structure; the other is the position error accumulation phenomenon caused by the open-loop nature. To overcome such two limitations, this paper proposes a novel Jacobian-matrix-adaption (JMA) method for the tracking control of robot manipulators via the zeroing dynamics. Unlike existing works requiring the information of the known robot model, the proposed JMA method uses only the input-output information to control the robot with unknown model. The solution based on the JMA method transforms the internal, implicit, and unmeasurable model information to the external, explicit, and measurable input-output information. Moreover, simulation studies including comparisons and tests substantiate the efficacy and superiority of the proposed JMA method for the tracking control of robot manipulators subject to unknown models.