Data-Driven Orthogonal Repetitive-Motion Posture Control of OMRM Under Unknown Models: A Neural Dynamics Approach

Precise position and posture control of an omnidirectional mobile redundant manipulator (OMRM) with unknown structural information is challenging. This article develops a data-driven orthogonal repetitive motion-posture control (DDORMPC) scheme that leverages online learning to regulate repetitive motions in both position and end-effector quaternion orientation. Then, a dynamic neural network with nonconvex mappings (NCMDNN) is introduced by integrating structure learning with OMRM control to solve the DDORMPC problem. It employs a velocity-compensated gradient-descent update for accurate online estimation of the system Jacobian, theoretically driving the tracking error to zero. Theoretical analysis demonstrates that both the learning and control modules exhibit favorable convergence properties under necessary noise conditions. Numerical simulations, comparative experiments, and platform validation collectively verify the innovation, effectiveness, and practical value of both the proposed DDORMPC scheme and the NCMDNN model.