A distributed time-varying neurodynamic algorithm for multi-UAV collaborative target tracking problem in maritime search and rescue.

Abstract

This work investigates the problem of collaborative target tracking by multiple unmanned aerial vehicles (UAVs) in maritime search and rescue. A class of time-varying (TV) convex optimization problems with inequality constraints is presented. In contrast to existing studies that address UAV-based maritime search and rescue under fixed wind speed conditions, this study also explores collaborative target tracking by UAVs under varying wind speed conditions. A distributed TV neurodynamic algorithm is designed using the prediction-correction method and sliding mode control technique. By constructing suitable Lyapunov functions, the proposed algorithm, whose fixed-time convergence property is theoretically proven, exhibits a convergence time independent of the initial state. In the context of multi-UAV collaborative target tracking experiments, the target's three-dimensional trajectory equations were established for three distinct cases, each incorporating sine and cosine functions along the x and y axes. The experiments demonstrate that the tracking efficiency of the multi-UAV system is unaffected by the TV target trajectory.