Sampled-data discrete-time coordination algorithms for double-integrator dynamics under dynamic directed interaction

In this article, we study two sampled-data-based discrete-time coordination algorithms for multi-vehicle systems with double-integrator dynamics under dynamic directed interaction. For both algorithms, we derive sufficient conditions on the interaction graph, the damping gain and the sampling period to guarantee coordination by using the property of infinity products of stochastic matrices. When the conditions on the damping gain and the sampling period are satisfied, the first algorithm guarantees coordination on positions with a zero final velocity if the interaction graph has a directed spanning tree jointly while the second algorithm guarantees coordination on positions with a constant final velocity if the interaction graph has a directed spanning tree at each time interval. Simulation results are presented to show the effectiveness of the theoretical results.