This article addresses the problem of trajectory tracking control of a quadrotor with unknown mass and external disturbances. Adaptive sliding mode control (ASMC) approach integrated with a radial basis function neural network (RBFNN) is proposed to enhance the tracking performance of the quadrotor. To cope with model uncertainties arising from unknown mass and external disturbances, adaptive laws based on RBFNN are employed to estimate and compensate for these unknowns in real time. The stability of the closed-loop system is rigorously established for each control stage using the Lyapunov theory. The effectiveness of the proposed control methodology is validated through numerical simulations under various scenarios, including unknown mass and external disturbances, and its performance is benchmarked against both sliding mode control (SMC) and ASMC approaches. The results illustrate the enhanced performance of the suggested RBFNN-ASMC technique under unknown mass and external disturbances, generating considerable reductions in root mean square error (RMSE) and improved on-track percentage (OTP).
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