Dynamic Event-Triggered Constraint Control With Predefined-Time Convergence for Delayed Multiagent Systems

For multi-agent systems (MASs) subject to input delays and time-varying state constraints, this work proposes a predefined-time consensus control scheme. The state constraints, input delays, and additional delays caused by event triggering are obstacles that affect the control performance and practicality of MASs. To achieve predefined-time consensus while strictly satisfying the state constraints, the scheme first tackles two key obstacles: input delays and unknown system nonlinearities. Specifically, the Pade approximation technique is adopted to reconstruct the input delay into a new error variable, and then adaptive neural networks (NNs) are used to compensate for both the delay-induced error and the unknown nonlinearities. Meanwhile, by leveraging an asymmetric barrier Lyapunov function (ABLF) and a predefined-time stability framework, the scheme fundamentally ensures that the MASs achieve state consensus within a predefined time and strictly satisfy the time-varying state constraints. Further, to solve the inherent communication burden problem in networked MASs and improve the applicability of the scheme, a dynamic event-triggered mechanism (DETM) is designed as a complementary optimization: based on a dynamically evolving internal variable, the DETM only triggers communication when necessary, thereby significantly reducing the number of control updates and inter-agent data transmissions. At the same time, the scheme eliminates the requirement for agents to continuously monitor their states to determine triggering conditions, further enhancing the practicality of the scheme. Simulation results verify the effectiveness of the proposed method.