Decentralized adaptive consensus control for multi-manipulator system with uncertain dynamics

An adaptive control approach is proposed to deal with the multi-manipulator system consensus problem based on the multi-agent theory. In the current multi-agent literature, agents are assumed to have determined models. However, the real manipulator's dynamics contains uncertain parameters. According to the “linearity-in-parameters” property, the adaptive updating law for uncertain dynamics parameters is derived by the projection method. Then, a decentralized controller is designed based on the backstepping scheme, which only utilizes the information of connected manipulators. By the proposed controller, all the manipulators' joints move towards the same configuration to achieve certain coordination tasks. In addition, performance of the control system is analyzed by the Lyapunov method, and the consensus error is proved to approach zero. Finally, the effectiveness of the proposed scheme is illustrated by simulations on a multiple two-link manipulators system.