Adaptive dynamic average consensus with dynamic event-triggered communication

Dynamic average consensus (DAC) allows networked agents to track the average of time-varying reference signals through local communication. To reduce the communication cost in DAC, event-triggered control is adopted as an efficient paradigm. While existing event-triggered DAC algorithms improve communication efficiency, they suffer from critical limitations: some require restrictive assumptions such as bounded reference signals and their derivatives, whereas others fail to guarantee the boundedness of adaptive gains in the presence of persistent disturbances. To overcome these limitations, this paper proposes a novel adaptive DAC algorithm with dynamic event-triggered communication. The proposed algorithm relaxes the requirement for known upper bounds on reference signals and their derivatives. Moreover, a σ-correction term is introduced to ensure the boundedness of adaptive gains under persistent disturbances. On this basis, the designed triggering condition, facilitated by a dynamic auxiliary variable, substantially reduces communication burden while rigorously excluding Zeno behaviour. Comparative simulation studies are provided to verify the effectiveness of the proposed algorithm.