Decentralized control of collaborative redundant manipulators with partial command coverage via locally connected recurrent neural networks

This paper studies the decentralized control of multiple redundant manipulators for the cooperative task execution problem. Different from existing work with assumptions that all manipulators are accessible to the command signal, we propose in this paper a novel strategy capable of solving the problem even though there exists some manipulators unable to access the command signal directly. The cooperative task execution problem can be formulated as a constrained quadratic programming problem. We start analysis by re-designing the control law proposed in (Li et al. Neurocomputing, 2012), which solves the optimization problem recursively. By replacing the command signal with estimations with neighbor information, the control law becomes to work in the partial command coverage situation. However, the stability and optimality of the new system are not necessarily the same as the original system. We then prove in theory that the system indeed also globally stabilizes to the optimal solution of the constrained quadratic optimization problem. Simulations demonstrate the effectiveness of the proposed method.