A Novel Adaptive Formation Control Strategy for Teams of Unmanned Vehicles Under Complete Dynamic Uncertainty

Abstract

Modern unmanned systems, including aerial, terrestrial, and underwater vehicles, are increasingly utilized in dynamic and unpredictable environments, where the presence of modeling uncertainties necessitates the development of robust and adaptive control strategies. In this work, we address the formation control problem for a team of unmanned systems with completely uncertain dynamics under a virtual leader-following framework. We propose a novel cooperative adaptive formation control algorithm, designed using artificial neural networks to achieve accurate formation tracking. The effectiveness of the proposed control strategy is established through rigorous theoretical analysis, which guarantees uniform ultimate boundedness of the overall system and exponential convergence of the tracking errors to a small neighborhood of zero. Numerical simulations further validate the effectiveness of the proposed formation control algorithm, demonstrating that the followers accurately track the desired formation trajectory relative to the leader, even in the presence of complete system uncertainties. This work suggests potential application in coordinating multiple unmanned airships for tasks such as persistent aerial surveillance, atmospheric data collection, and wide-area communication support, where adaptability to time-varying and uncertain dynamics is essential.