Learning-Based Robust Bipartite Consensus Control for a Class of Multiagent Systems

Abstract

This paper studies the robust bipartite consensus problems for heterogeneous nonlinear nonaffine discrete-time multiagent systems (MASs) with fixed and switching topologies against data dropout and unknown disturbances. At first, the controlled system's virtual linear data model is developed by employing the pseudo partial derivative technique, and a distributed combined measurement error function is established utilizing a signed graph theory. Then, an input gain compensation scheme is formulated to mitigate the effects of data dropout in both feedback and forward channels. Moreover, a data-driven learning-based robust bipartite consensus control (LRBCC) scheme based on a radial basis function neural network observer is proposed to estimate the unknown disturbance, using the online input/output data without requiring any information on the mathematical dynamics. The stability analysis of the proposed LRBCC approach is given. Simulation and hardware testing also illustrate the correctness and effectiveness of the designed method.

Data-based bipartite formation control for multi-agent systems with communication constraints

“…Since MFAC was studied by Hou et al, 17 many related approaches have been developed, including single system and multi-systems. Especially, for single system, numerous issues have been investigated, such as measurement disturbance, 26 data dropout, 35 cyber attack, 36 and data quantization. 37 As MASs, an effective consensus tracking control scheme was formulated by Bu et al 38 The switching topologies and time-varying delays of MASs were considered by Li et al 15 The disturbance and heterogeneous problems of MASs to achieve formation control were studied by Xiong et al 39 In the aforementioned related DDC formation approaches, saturation nonlinearity and data quantization have rarely been considered while designing the controller.…”