Hybrid Aperiodic Control of Distributed Networked Nonlinear Systems

This paper proposes a hybrid aperiodic sampled-data control strategy for the stability of interconnected nonlinear systems. These interconnected nonlinear systems are modelled using Takagi-Sugeno fuzzy modelling technique. In the proposed methodology, first a self-triggering strategy is developed for computing the next sampling instant for each node in a distributed networked control system. Further, an event-triggering condition is checked at these sampling instants and the control signal for each system is updated only if the event-triggering condition is satisfied. This reduces the number of sampling instants and number of times the control law is computed as compared to time-triggered mechanism and the aperiodic sampled-data strategy when applied alone. The results presented in this paper are validated through simulations and a comparison with other triggering mechanisms is also shown.