A Hybrid Aperiodic Sampled-data Strategy for Distributed Networked Control System

This paper addresses the problem of stability analysis of a linear distributed networked control system (DNCS) by employing the aperiodic sampled-data strategy. Firstly, a self-triggering strategy for each subsystem of a DNCS is developed in which the next sampling instant for control law update is computed. To further improve the efficiency of the utilization of resources of DNCS, a hybrid triggering mechanism combining event-triggered and self-triggered methodologies is proposed. The proposed hybrid approach comprises of two steps: Firstly, the next sampling instant is computed using self-triggered scheme for each subsystem of a DNCS and thereafter, the event-triggered condition is verified at these sampling instants. The control law for each subsystem is updated only if the event-triggering condition is violated. Each subsystem transmits its state information to the neighbouring subsystems only when its state error violates a predetermined event-triggering condition. This reduces the number of sampling instants as well as the control updates, thereby leading to reduction in the usage of network resources. The results proposed in the paper are validated using a simulation example.