Fully-actuated hybrid UAVs offer significant operational flexibility compared to conventional under-actuated platforms; however mid, their highly nonlinear, coupled dynamics and vulnerability to external disturbances pose major challenges for precise trajectory tracking. While conventional Sliding Mode Control (SMC) provides robustness, it suffers from the chattering phenomenon, which degrades actuator performance and stability. To address these limitations, this paper develops a robust adaptive control scheme integrating an Adaptive Neuro-Fuzzy Inference System with SMC (ANFIS-SMC) specifically tailored for a fully-actuated hybrid UAV. Instead of a standard discontinuous switching function, an ANFIS approximator is designed to generate a smooth control law, effectively mitigating chattering while preserving robustness against uncertainties. The ANFIS parameters are adapted online to compensate for nonlinear system dynamics and environmental disturbances. Furthermore, a control allocation strategy is employed to map the control efforts to the six-degree-of-freedom actuators. The stability of the closed-loop system and the boundedness of the tracking errors are rigorously analyzed using the Lyapunov stability theorem. Comprehensive simulations verify the controller’s performance under nominal conditions and sudden external wind gusts. The results demonstrate that the proposed framework significantly outperforms conventional SMC and Fuzzy-SMC methods. Based on the Integral of Time-weighted Absolute Error (ITAE) criterion, particularly under external disturbance conditions, the proposed approach achieves a tracking error reduction of approximately 93% compared to Fuzzy-SMC and over 98% compared to standard SMC, highlighting its superior tracking accuracy and chattering suppression capabilities for complex autonomous missions.
Kabiri et al. (Thu,) studied this question.