Adaptive model-based event-triggered consensus control for multi-agent systems with unknown system dynamics

The model-based event-triggered strategies have been developed as an alternate for Zero-Order-Hold (ZOH)based event triggered scheme in continuous-time for multi-agent systems (MAS). However, traditional event-triggered schemes need the knowledge of system dynamics whereas model-based schemes are sensitive to model uncertainties. Therefore, in this paper, a novel distributed event-triggered consensus control schemes are proposed for MAS with uncertain system dynamics. In the proposed approach, the system is considered completely unknown and the MAS model or state estimator is updated online. The update law for tuning the unknown parameters of the MAS model, and distributed event triggering condition are derived. Lyapunov theory is used to demonstrate that all the closed-loop signals and estimated MAS model parameters are asymptotically stable and consensus. Computer-aid simulation results are included to demonstrate the effectiveness of the proposed approach. Additionally preliminary work towards the implementation of the proposed strategies on a real-time MAS platform is described in order to emphasize the implications and the complexity of developing practical solutions for real applications.