Decentralized adaptive leader-follower control of multi-manipulator system with uncertain dynamics

An adaptive control approach is proposed to deal with the multi-manipulator leader-follower control based on the multi-agent theory. In the current multi-agent literature, agents are assumed to have determined linear models. However, the real manipulator’s dynamics is highly nonlinear and contains uncertain parameters. According to the “inearity-in-parameters” property, the adaptive updating law for uncertain dynamics parameters is derived. Then, the decentralized torque controller for each follower manipulator is designed using the backstepping scheme, which only utilizes the information of its neighbor (connected) manipulators. By the proposed controller, all the follower manipulators’ joints can track the leader joint’s trajectory to achieve certain coordination tasks. In addition, the performance of control system is analyzed by the Lyapunov method, and the tracking error is proved to approach zero. Finally, the effectiveness of the proposed scheme is illustrated by simulations on a multiple two-link manipulators system.