Time-Varying Distributed Neurodynamic Algorithm to Fixed-Time Economic Dispatch with Event-Triggered Communication Over Directed Graphs

This paper proposes a distributed fixed-time neuro-dynamic algorithm with time-varying coefficients over directed communication networks, based on an event-triggered strategy. Following that, it is shown that the algorithm converges to an economic dispatch problem (EDP) optimum solution, and an upper bound on the convergence time that is independent of the initial conditions is given. Meanwhile, the proof of the exclusion of Zeno behavior is also given in detail. Finally, an EDP numerical simulation example is provided to verify the superiority of the proposed algorithm and the effectiveness of the event-triggered strategy.