Safe Learning-Based Control for Multiple UAVs Under Uncertain Disturbances

This paper presents a safe learning control strategy aimed at ensuring the accurate tracking of multiple unmanned aerial vehicles (UAVs) along their predetermined trajectories while also guaranteeing safety under uncertain environments such as trajectory conflict, airflow interference between UAVs, and external disturbances. The proposed control framework employs a high-level learning-based feedback linearization control combined with model predictive control (LB-FBL-MPC), coupled with a low-level safety barrier certificates and control Lyapunov function-based quadratic programs (SC), for nonlinear multiple-UAV systems. The high-level LB-FBL-MPC uses incremental Gaussian processes (IGPs) to learn uncertain disturbances online, and feedback linearization is applied to approximate the linear system. The MPC optimizes the reference trajectory based on the linearized dynamical model to enhance the adaptivity of the system. Furthermore, the low-level SC guarantees the safety and asymptotic stability of the multi-UAV system by using the prediction distribution of the IGPs. Ablation and benchmark comparison experiments demonstrate the efficacy of the proposed tracking control strategy.