An intelligent control strategy for UAVs using adaptive Type-3 fuzzy logic with real-time uncertainty bounds and parallel auxiliary compensation

This paper presents a novel intelligent control strategy for Unmanned Aerial Vehicles (UAVs) using Type-3 fuzzy-logic systems (T3-FLSs). UAVs are widely adopted in diverse fields due to their efficiency and versatility; however, they face significant control challenges under uncertain dynamics and environmental disturbances. Addressing these issues requires control strategies with high adaptability and robustness. T3-FLSs offer superior capabilities in modeling and managing uncertainties, making them well-suited for complex UAV environments. In this study, a robust T3-FLS-based nonlinear control framework is proposed. Novel online adaptation laws are developed to optimize the T3-FLS for dynamic modeling and to identify unknown uncertainty bounds. Using these estimations, a supplementary controller is designed to operate in parallel with the T3-FLS controller, enhancing overall robustness. Theoretical analysis confirms the system’s stability under the proposed adaptation mechanisms. The effectiveness and resilience of the control scheme are validated through extensive simulations, demonstrating strong performance under structured disturbances (modeled as sinusoidal disturbances) stochastic turbulence/measurement noise (modeled as random noise) and unknown nonlinear dynamics.