Low-Computation-Based Adaptive Self-Triggered Bipartite Consensus Control for Nonlinear Multi-Agent Systems Subject to Sensor Faults

This paper presents an adaptive self-triggered tracking control scheme for nonlinear multi-agent systems with sensor faults. Firstly, this paper considers a competitive-cooperative relationship in multi-agent systems, which represents a more common situation. Then, a low-computation adaptive neural control strategy combined with constraint processing techniques is proposed, based on which the problem of complexity explosion can be avoided without introducing any filters. Furthermore, considering the limited transmission resources of the practical system, a self-triggered control mechanism is introduced to enhance the utilization of system transmission resources. The proposed control scheme ensures that all signals within the closed-loop system remain bounded and guarantees bipartite tracking performance. Finally, the effectiveness of the presented approach is verified through simulation results.