Model-Free Tracking Control for Non-Affine Lipschitz Unmanned Aerial Vehicle Systems

This study addresses the challenges of controlling nonlinear low-altitude aerial vehicles in complex environments by proposing a model-free adaptive control (MFAC) framework based on improved dynamic linearization techniques. By constructing an equivalent linear data model and incorporating an online parameter estimation algorithm, the proposed method enables real-time control of nonlinear low-altitude aerial vehicles and enhances resilience against denial-of-service (DoS) attacks. Simulation results demonstrate that the proposed approach significantly outperforms traditional methods in terms of accuracy, response speed, and robustness, particularly under data packet loss scenarios, exhibiting stable control precision and high resource utilization efficiency.