With the advent of model-based control in vehicle systems, power networks, systems biology, and traortation systems, employing the modelling of multi-agent systems (MASs) has been increased. In MASs with wireless communication networks and dynamical dependency among agents, employing the deployment of agents, inaccessibly of measurements, and communication topology. In this thesis, a parametric distributed identification of multi-agent systems based on max-consensus algorithm (MCA) is proposed. The provided method is consistent with the constraints of MASs in which agent may find the dynamic of other agents in online manner by employing local information of its neighbors through non-ideal communications networks. It is assumed that the wireless communication networks among agents have Bernoulli dropouts. It is proved that the sufficient condition for the convergence of suggested method corresponds to the dynamical characteristic of identified agent and the convergence time of MCA. It is stated that in the presence of communication networks dropouts with Bernoulli distribution, the MCA converges with probability one in the finite time. Furthermore, the upper bound for the convergence time of MCA is given by means of probabilistic expressions. By employing the suggested method, the solution is provided for the power tracing problem in microgrids as a case study. Key Words Parametric Distributed Identification, Multi-Agent Systems, Online Identification, Non-Ideal Communication Networks, Max- Consensus Algorithm, Microgrid
