Abstract
Concentrating on various types of grasped objects, this work presents a distributed coordinated control scheme for a dual-arm reconfigurable manipulator (DRM) via adaptive dynamic programming (ADP). Kinematic and force analyses are performed to formulate the dynamics of a single-arm manipulator and the object using joint torque feedback technology and the Newton–Euler formulation. To address uncertainties associated with the grasped object, a gradient model-based adaptive algorithm is developed for online estimation of the grasp matrix relating the object to the DRM. By employing an adaptive observer to identify unknown dynamic terms, an enhanced optimal performance index is constructed that incorporates both object tracking performance and internal force effects within the manipulator. The performance index function is approximated by only a critic neural network, and the optimal control policy is obtained by policy iteration. Lyapunov stability analysis demonstrates that internal force error, position tracking error, and critic network weight approximation error are all uniformly ultimately bounded. Experimental results confirm the effectiveness of the developed coordinated control approach.