Data-Driven Bipartite Consensus Control for Large Workpieces Rotation of Nonlinear Multi-Robot Systems

Abstract

In this paper, a novel data-driven bipartite consensus control scheme is proposed for the rotation problem of large workpieces with multi-robot systems (MRSs) under a directed communication topology. The rotation of a large workpiece is described as the MRSs with cooperation and antagonism interaction. By the signed graph theory, it is further transformed into a bipartite consensus control problem, where all followers are uniformly degenerated into the general nonlinear systems based on the lateral error model. To augment the flexibility of control protocol and improve control performance, a higher-dimensional full form dynamic linearization (FFDL) technique is committed to the MRSs. The control input criterion function consists of the data model based on FFDL and the bipartite consensus error based on the signed graph theory, and the proposed control protocol is given by optimizing this criterion function. In this way, this scheme has a higher degree of freedom and better adaptive adjustment capability while not excessively increasing the control method complexity, and it can also be compatible with other forms of dynamic linearization techniques in MRSs. Further, three matrix norm lemmas are introduced to deal with the challenges of stability analysis caused by higher matrix dimensions and more robots. Finally, the effectiveness of the proposed method is verified by numerical simulations.