Data-Driven Bipartite Consensus Tracking for Nonlinear Multiagent Systems With Prescribed Performance
针对未知非线性多智能体系统，提出一种仅使用输入输出数据的数据驱动控制策略，在固定或切换拓扑下实现二分一致性跟踪，并保证跟踪误差收敛到预设区域。
In this article, a data-driven control strategy with prescribed performance is proposed to address the distributed bipartite consensus problem for unknown nonlinear multiagent systems (MASs) undersigned directed graphs. The dynamics of the agents in each topology are completely unknown and the desired trajectory information is only communicated to a subset of agents. First, the unknown nonlinear dynamic of each agent is transformed into an equivalent time-varying linearized model by utilizing the dynamic linearization method. Then, by considering the prescribed performance, a strictly increasing function is given to transform the constrained tracking error condition of the nonlinear MASs into an equivalent unconstrained error condition. Meanwhile, a new control protocol that only uses the input and output data of the agents is designed in the case of fixed or switched topologies. The proposed method can not only guarantee the bipartite consensus but also ensure the bipartite tracking error always converges to the prescribed region. Moreover, the designed control parameters can be adjusted appropriately to obtain better tracking performance. Finally, the convergence of bipartite consensus tracking error is guaranteed through rigorous theoretical analysis. The control scheme is further verified by two examples.