Robust Cooperative Control of UAV Swarms for Dual-Camp Divergent Tracking of a Heterogeneous Target

Agents are used to exhibit swarm intelligence in the sense of convergence, while divergence is equivalently common nature and useful complex applications for multi-UAV systems. This paper proposes a robust target-tracking control algorithm, where UAV swarms partitioned by signed graph perform opposite movements along or against trajectory target. Uncertainties take place both fractional-order model target double-integrator dynamics UAVs. To tackle challenge induced bipartite behavior unknown components systems, article comes up with backstepping cascade controller new method uncertainty estimation-compensation via combined approach based on neural network (NN) an Uncertainty Disturbance Estimator (UDE). Steered controller, UAVs structurally balanced will display symmetry their paths, pursuing away from respect origin. Theoretical derivation numerical simulations have evidenced that tracking errors converge zero. Compared traditional NN solve such problems, this proposed first time, which can effectively improve precision cooperative reduce chattering phenomena controller.