Optimizing Quadrotor Stability: RBF Neural Network Control with Performance Bound for Center of Gravity Uncertainty

The Radial Basis Function (RBF) neural network has been widely applied for approximating nonlinear systems and improving control robustness, particularly in uncertain conditions such as dynamic shifts in the quadrotor’s Center of Gravity (COG). However, initial weight estimation errors can degrade transient responses, reducing tracking performance. This study proposes a novel RBF-based control scheme integrated with a performance-bound mechanism to enhance quadrotor stability under COG uncertainty. The performance bound ensures that the quadrotor’s motion remains within a defined region around the reference trajectory, thereby minimizing steady-state and transient errors. The RBF network is trained online to estimate the system’s dynamic changes, and the controller is designed using a Lyapunov-like function to ensure stability. Simulation results show that the proposed controller achieves better tracking accuracy and significantly lower energy usage, with total force and moment values reduced compared to the standard RBF controller. Specifically, the proposed controller uses 3010.7 N of force and 2.2427 Nm of moment, while the standard controller requires 3150.2 N and 15.197 Nm. These results confirm that the proposed method provides improved performance and energy efficiency. This research highlights the potential of integrating performance bounds in neural network control for robust quadrotor navigation. Future work includes real-world experiments to validate performance under varying COG perturbations.