Distributed Model-Free Bipartite Consensus Tracking for Unknown Heterogeneous Multi-Agent Systems with Switching Topology

This paper proposes a distributed model-free adaptive bipartite consensus tracking (DMFABCT) scheme. The proposed scheme is independent of a precise mathematical model, but can achieve both bipartite time-invariant and time-varying trajectory tracking for unknown dynamic discrete-time heterogeneous multi-agent systems (MASs) with switching topology and coopetition networks. The main innovation of this algorithm is to estimate an equivalent dynamic linearization data model by the pseudo partial derivative (PPD) approach, where only the input–output (I/O) data of each agent is required, and the cooperative interactions among agents are investigated. The rigorous proof of the convergent property is given for DMFABCT, which reveals that the trajectories error can be reduced. Finally, three simulations results show that the novel DMFABCT scheme is effective and robust for unknown heterogeneous discrete-time MASs with switching topologies to complete bipartite consensus tracking tasks

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

This paper studies the robust bipartite consensus problems for heterogeneous nonlinear nonaffine discrete-time multi-agent 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

Distributed Event-triggered Bipartite Consensus for Multi-agent Systems Against Injection Attacks

This paper studies fully distributed data-driven problems for nonlinear discrete-time multi-agent systems (MASs) with fixed and switching topologies preventing injection attacks. We first develop an enhanced compact form dynamic linearization model by applying the designed distributed bipartite combined measurement error function of the MASs. Then, a fully distributed event-triggered bipartite consensus (DETBC) framework is designed, where the dynamics information of MASs is no longer needed. Meanwhile, the restriction of the topology of the proposed DETBC method is further relieved. To prevent the MASs from injection attacks, neural network-based detection and compensation schemes are developed. Rigorous convergence proof is presented that the bipartite consensus error is ultimately boundedness. Finally, the effectiveness of the designed method is verified through simulations and experiment

Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems

In this study, we investigate the event-triggering time-varying trajectory bipartite formation tracking problem for a class of unknown nonaffine nonlinear discrete-time multiagent systems (MASs). We first obtain an equivalent linear data model with a dynamic parameter of each agent by employing the pseudo-partial-derivative technique. Then, we propose an event-triggered distributed model-free adaptive iterative learning bipartite formation control scheme by using the input/output data of MASs without employing either the plant structure or any knowledge of the dynamics. To improve the flexibility and network communication resource utilization, we construct an observer-based event-triggering mechanism with a dead-zone operator. Furthermore, we rigorously prove the convergence of the proposed algorithm, where each agent’s time-varying trajectory bipartite formation tracking error is reduced to a small range around zero. Finally, four simulation studies further validate the designed control approach’s effectiveness, demonstrating that the proposed scheme is also suitable for the homogeneous MASs to achieve time-varying trajectory bipartite formation tracking