Novel Data-Driven Discrete Neurodynamics Schemes for Redundant Manipulator Control
针对未知模型冗余机械臂的末端跟踪控制难题，提出了两种数据驱动离散神经动力学方案，通过自适应雅可比矩阵和避免矩阵求逆，经UR5实验验证了其有效性和优越性。
It is very challenging to precisely control a redundant manipulator with an unknown model during the end-effector tracking task. Adata-driven approach offers a promising solution for manipulator control under such conditions. In this article, two data-driven discrete neurodynamics (DDDN) schemes are proposed for redundant manipulator tracking control. First, utilizing discrete neurodynamics (DN) principles, the DDDN-1 scheme with an adaptive Jacobian matrix is developed. Subsequently, the DDDN-2 scheme is further presented, which eliminates the need for the Jacobian matrix inversion operation. Detailed theoretical analyses verify the effectiveness of DDDN-1 and DDDN-2 schemes. Additionally, detailed comparisons with existing schemes have been provided. Finally, simulative and physical experiments conducted using the UR5 manipulator validate the theoretical analyses, demonstrating the effectiveness and superiority of DDDN-1 and DDDN-2 schemes.