Trajectory tracking control for low-altitude vehicles based on model-free dynamic linearization

This study proposes a novel control method for nonlinear systems based on the compact dynamic linearization technique, integrating data-driven and dynamic estimation strategies to achieve fixed-time and predetermined-time stability under incomplete dynamic information. By leveraging the input-output relationship, the data-driven framework bypasses traditional physical modeling complexities, enhancing applicability and efficiency in big data environments. The dynamic estimation technique ensures real-time system state updates, significantly improving trajectory tracking accuracy. Stability is rigorously analyzed using the contraction mapping principle, while experiments examine the effects of key control parameters and demonstrate how a compensation mechanism mitigates communication interruptions. Results confirm the method's effectiveness in enhancing control accuracy, stability, and disturbance robustness in nonlinear systems.