Formation Control for an UAV Team With Environment-Aware Dynamic Constraints
Zhongjun Hu, Xu Jin
Abstract
State-of-the-art literature on constrained multiagent system operations can only deal with constant or at best time-varying constraint requirements. Such constraint formulations cannot respond well to the dynamic environment and presence of external agents outside of the multiagent system. In this work, we consider a formation tracking problem for a group of unmanned aerial vehicles (UAVs) in the presence of a physical attacker. The safety/performance constraint functions are environment-aware and dynamic in nature, whose formulation depends on certain path parameters and presence of the attacker. The dependence on path ensures adaptation to the dynamic operation environment. The dependence on the attacker ensures swift adjustment based on the relative distances between the attacker and agents. UAV desired paths and desired path speeds can also be both path- and attacker-dependent. Composite barrier functions have been proposed to address the constraint requirements. Neural network is used to approximate unknown attacker velocity, where the ideal weight matrix is learned by adaptive laws. Besides, unknown system parameters and external disturbances are estimated by adaptive laws. The proposed formation architecture can ensure formation tracking errors converge exponentially to small neighborhoods near the equilibrium, with all constraint requirements met. At the end a simulation study further illustrates the proposed scheme and demonstrates its efficacy.