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

Abstract

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 parameters and structure; the other is the position error accumulation (PEA) phenomenon caused by the open-loop nature. To overcome such two limitations, this paper proposes a novel Jacobianmatrix- adaption (JMA) method for the tracking control of robot manipulators via the zeroing dynamics. Unlike existing works requiring information of the known robot model, the proposed JMA method uses only the input-output measurements 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 and comparisons substantiate the efficacy and superiority of the proposed JMA method for the tracking control of robot manipulators subject to unknown models.