A Hybrid Neurodynamic Scheme for Bimanual Synchronized Tracking Control of Robotic Manipulators With Uncertain Kinematics

The reconfigurability and structural complexity of robotic manipulators introduce significant challenges to their kinematic modeling, making traditional model-based methods less feasible. This article studies the synchronized tracking control problem of a dual-arm system with completely unknown kinematic model for the first time. A gradient neurodynamic method is presented to learn the unknown Jacobian matrices of dual manipulators. Then, we innovatively employ the coupled error in dual quaternion form as the state function of the zeroing neurodynamic model to control position and orientation simultaneously, leading to a more streamlined and compact inverse kinematics solution. Furthermore, by transforming task-space coupled errors into joint-space errors, we improve the cerebellar neural network to a mode compatible with kinematic control and, for the first time, utilize it to optimize synchronized kinematic control. Finally, the effectiveness of the proposed method is validated by simulations and experiments on various robotic manipulators.