Robust Event-triggered Distributed Average Tracking for Double-integrator Agents without Velocity Measurements

This paper investigates the distributed average tracking problem for a group of double-integrator agents. In some practical applications, velocity measurements may be unavailable due to technology and space limitations, and it is also usually less accurate and more expensive to implement. To this end, a distributed average tracking algorithm without using velocity measurements and correct initialization is established. It is worth noting that no global information is needed for parameter design. Then, in order to remove the requirement on continuous interaction, and reduce the communication cost and improve the energy efficiency, an event-triggered distributed average tracking algorithm is designed by incorporating an event-triggered communication strategy without using velocity measurements. To be exact, the idea of a dynamic event-triggered strategy is used to construct a triggering condition for each agent to guarantee the exclusion of Zeno behavior. Finally, simulations are provided to illustrate the obtained results.