Data-Driven Bipartite Consensus Control for Multiagent Systems: A Low-Quantity Signal Communication Approach

This article investigates the problem of data-driven bipartite consensus control for multiagent systems under low-quantity signal communication. Dynamic linearization methods are employed to transform the unknown nonlinear model into a data-driven model. The design of a semi-independent dynamic event-triggering mechanism for the input and output channels reduces the communication frequency. Moreover, the quantized encoding and decoding techniques are used to achieve data transfer among agents, which can reduce the length of communication data. Based on the designed communication scheme, a communication energy-saving bipartite consensus control strategy is proposed. Rigorous stability analysis demonstrates that all the signals of the system are ultimately bounded in the proposed data-driven bipartite consensus control algorithm. The proposed control scheme reduces not only the communication frequency but also the transmission bytes, which greatly alleviates the communication pressure. Finally, comparative experimental results from hardware testing further demonstrate the superiority and effectiveness of the developed control scheme.