New Finite Memory-Based Sliding Mode Control for Quadcopter

In this article, we propose a new finite memory-based sliding mode control (FM-SMC) for robust quadcopter trajectory tracking. The new FM-SMC was developed by designing a finite memory-based disturbance observer (FM-DOB) for accurate and robust disturbance compensation and a finite memory-based neural network learning algorithm (FM-NNLA) for approximating unknown nonlinearities. Unlike conventional infinite-memory approaches that suffer from error accumulation and sensitivity to initial conditions, the proposed FM-DOB estimates and compensates for disturbances within a finite time horizon, while the FM-NNLA updates neural network weights using only recent state information to approximate nonlinear dynamics without long-term error accumulation. By leveraging these FM structures, the new FM-SMC enhances robustness against disturbances and system uncertainties while ensuring stable performance. Rigorous stability analysis is carried out using Lyapunov theory, and real-time quadcopter experiments on boustrophedon and ascending helical trajectories verify the robustness and superior effectiveness of the new FM-SMC.