| Summary: | Unmanned aerial vehicles (UAVs) control faces major challenges such as dynamic complexity, unknown external disturbances, parametric uncertainties, time-varying states and delays. The literature proposes different techniques to address these challenges, but little attention has been paid to the design of a hybrid controller combining the advantages of these techniques to improve system performance. This research therefore aims to investigate the design of such a hybrid controller. In this paper, we present a novel intelligent controller based on Integral Sliding Mode Control (ISMC) and Extended State Observer (ESO) for a nonlinear Multiple Input Multiple Output (MIMO) drone quadrotor. First, the kinematic and dynamic models of our quadrotor drone are presented. Second, the ESO is used to estimate external disturbances and model uncertainties. Third, to overcome the problem of the reaching phase and the steady-state error, a new nonlinear ISMC is designed. The additive term of the ISMC structure has also overcome the problem of external disturbances and modelling errors, as well as observational errors. Fourth, an Adaptive Neural Network (ANN) switching control law is developed to surmount the chattering phenomenon. In addition, the stability of the control system is verified using Lyapunov stability theory. Finally, the effectiveness and superiority of the proposed control method are proved by simulation results. The results show that the proposed approach can handle external disturbances and eliminate chatter, leading to smooth control laws and lower power consumption, which is excellent from an energy efficiency perspective.
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