Observer-Based Event-Triggered Bipartite Consensus for Nonlinear Multi-Agent Systems: Asymmetric Full-State Constraints

This paper addresses the challenge of adaptive fuzzy event-triggered bipartite consensus control for a class of nonlinear multi-agent systems (MASs) with full-state asymmetric constraints, incorporating both cooperative and adversarial communication between agents. To ensure consensus tracking performance in the presence of asymmetric full-state constraints, the nonlinear MASs are augmented with an enhanced nonlinear function. Fuzzy logic systems (FLSs) are employed to handle unfamiliar nonlinearities effectively. To reduce the communication burden and enhance transient performance, an event-triggered control strategy is introduced, accompanied by the construction of an observer utilizing triggered output signals to assess unmeasured states. Furthermore, the control scheme is formulated by integrating dynamic surface control (DSC) and adaptive backstepping techniques. The validity of the proposed approach is verified through Lyapunov's theory, demonstrating that all signals within the closed-loop system are bounded. Simulation results further support the effectiveness of the proposed methodology. Note to Practitioners —With the rapid development and deep integration of intelligent control theory, the control design of MASs is still challenging in many engineering problems. However, in practical applications, there is not only cooperation but also competition between agents, and the system states are unmeasured and subject to various forms of constraints. To solve this problem, we introduce an improved nonlinear transformation function. When network resources are limited, continuous communication is sometimes not feasible in engineering applications. To minimize the communication burden, we propose an event-triggered control strategy, which combines dynamic surface control technology and adaptive backstepping technology to design the trigger controller. The proposed scheme can effectively save resources and deal with various constraints of system states and may be applied to other similar engineering fields.