Data-Based Model-Free Predictive Control System Under the Design Philosophy of MPC and Zeroing Neurodynamics for Robotic Arm Pose Tracking

Mengrui Cao, Lin Xiao, Qiuyue Zuo, Linju Li, Xieping Gao

Abstract: Involving both position and orientation tracking, pose tracking control for the end-effector of a redundant manipulator is a critical problem in robotic motion control. However, existing methods often suffer from dependency on model parameters and lack joint constraints. To remedy these weaknesses, this article proposes a data-based predictive tracking control of position and orientation (DBPTCPO) for redundant manipulators with undetermined parameters. Specifically, in addition to minimizing tracking error, the DBPTCPO scheme can also minimize joint velocity and acceleration to optimize energy efficiency. Furthermore, it directly handles three-level joint constraints, effectively preventing a reduction in the feasible domain of decision variables. As for the uncertain parameters of redundant manipulators, a method based on zeroing neurodynamics (ZNs) is developed to estimate the Jacobian matrix, requiring only the sensory output and control signals. Ultimately, a ZN-based solver is designed to solve the quadratic programming (QP) problem with inequality constraints derived from the DBPTCPO scheme. Necessary theoretical analyses for the control process are provided, and the higher tracking accuracy of the proposed method is numerically validated when compared with other control schemes.

External IDs:dblp:journals/tnn/CaoXZLG25