Mirali, FurughFurughMiraliWerner, HerbertHerbertWerner2020-08-272020-08-272020-07American Control Conference (ACC 2020)http://hdl.handle.net/11420/7165This paper presents a novel approach for handling packet loss in first-order consensus protocols based on a Taylor series expansion. We propose a dynamic memory approach which depends on a locally measured loss rate at each agent. The quasi-Taylor method assumes that each agent is storing not only the past received value of its neighbours in a memory, but the last ν received states of all neighbours in order to predict the future trajectory with the quasi-Taylor estimation. In addition, we use the so-called importance measure to label the most important information received at each time step. Then, depending on the measured loss rate the past data points of a neighbour are used to predict the future trajectory. Hereby, the trajectory is determined as a convex combination of different orders of the quasi-Taylor estimation. In order to minimise the distance of the consensus value to the actual average, we propose to use an adaptive step size for predicting the future trajectory of the neighbours. We give an upper bound on the convergence rate when uniform packet loss is assumed and show that the proposed approach outperforms existing methods from the literature with the help of simulation studies.en0743-16192020701706Conference Paper10.23919/ACC45564.2020.9147682Other