Dynamic neural networks aided distributed cooperative control of manipulators capable of different performance indices.

This paper presents a distributed scheme for the control of multiple redundant manipulators to simultaneously achieve four objectives, i.e., the task to reach global cooperation, joint-physical limits compliance, limited communications among manipulators and optimality in terms of a specified performance index. In addition, corresponding theoretical analyses are provided, which guarantee that, with the communication network being connected, all manipulators can jointly obtain the same desired motion information. Then, the proposed scheme is converted into a quadratic program (QP) formed formulation and solved by a dynamic neural network with rigorously provable convergence. Furthermore, simulations and comparisons are provided to illustrate the effectiveness of the proposed distributed scheme as well as the presented dynamic neural network.