Composite-Learning-Based Adaptive Neural Control for Dual-Arm Robots With Relative Motion

This article presents an adaptive control method for dual-arm robot systems to perform bimanual tasks under modeling uncertainties. Different from the traditional symmetric bimanual robot control, we study the dual-arm robot control with relative motions between robotic arms and a grasped object. The robot system is first divided into two subsystems: a settled manipulator system and a tool-used manipulator system. Then, a command filtered control technique is developed for trajectory tracking and contact force control. In addition, to deal with the inevitable dynamic uncertainties, a radial basis function neural network (RBFNN) is employed for the robot, with a novel composite learning law to update the NN weights. The composite learning is mainly based on an integration of the historic data of NN regression such that information of the estimate error can be utilized to improve the convergence. Moreover, a partial persistent excitation condition is employed to ensure estimation convergence. The stability analysis is performed by using the Lyapunov theorem. Numerical simulation results demonstrate the validity of the proposed control and learning algorithm.

Automated summary

This article presents an adaptive control method for dual-arm robot systems to perform bimanual tasks under modeling uncertainties. The control method incorporates trajectory tracking and contact force control by considering the relative motions between robotic arms and a grasped object. The proposed control also utilizes a radial basis function neural network (RBFNN) and a composite learning law to update the network weights and improve convergence. The stability analysis confirms the validity of the control and learning algorithm.