Stability of feedback linearization under intermittent information: A target-pursuit case

Managing networks of Autonomous Vehicles (AVs) for accomplishing a common goal, such as target pursuit, is very challenging due to the limited processing, sensing and communication capabilities of the agents. The effects of these limitations on stability of control systems are investigated in this paper. Having the performance of a target-pursuit controller provided with limited information about the target as an incentive, we develop a complete methodology for analyzing robustness of nonlinear controllers under intermittent information. As long as new information arrive within Maximum Allowable Transfer Intervals (MATIs), stability of the closed-loop system is guaranteed. Considering networks of AVs as spatially distributed systems, we adopt a Network Control Systems (NCSs) approach. Using Lyapunov techniques and the small-gain theorem, we are able to analyze stability of internal dynamics in feedback linearized systems within the same framework, and not as a separate problem. Finally, based on the target's maneuver, we provide MATIs leading to different types of stability for the investigated target-pursuit policy, and provide corroborating numerical simulations.