###### Abstract  
This paper addresses the problem of controlling quadcopters operating in an environment characterized by unpredictable disturbances such as wind gusts. From a control point of view, this is a nonstandard, highly challenging problem. Fundamentally, these quadcopters are high-order dynamical systems characterized by an under-actuated and highly nonlinear model with coupling between several state variables. The main objective of this work is to achieve a trajectory by tracking desired altitude and attitude. The problem was tackled using a robust control approach with a multi-loop nonlinear controller combined with extended Kalman filtering (EKF). Specifically, the flight control system consists of two regulation loops. The first one is an outer loop based on the backstepping approach and allows for control of the elevation as well as the yaw of the quadcopter, while the second one is the inner loop, which allows the maintenance of the desired attitude by adjusting the roll and pitch, whose references are generated by the outer loop through a standard PID, to limit the 2D trajectory to a desired set path. The investigation integrates EKF technique for sensor signal processing to increase measurements accuracy, hence improving robustness of the flight. The proposed control system was formally developed and experimentally validated through indoor tests using the well-known Parrot Mambo unmanned aerial vehicle (UAV). The obtained results show that the proposed flight control system is efficient and robust, making it suitable for advanced UAV navigation in dynamic scenarios with disturbances. a _Applied system_  
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Acedemic Editor: [PERSON]  
122 May 2025  
128 June 2025  
122 May 2025  
their limited consideration of disturbances and uncertainties. The study presented in [3] proposes an approach aimed at enhancing the robustness of trajectory tracking control in systems facing uncertainties by employing discrete switching. However, this approach can induce instability problems. To tackle this issue, the authors introduced an adaptive law to estimate the switching gain, but that comes with challenges, such as overestimating the gain and potentially causing instability as the estimation increases. To improve flight performance and reject disturbances, several other control methods have been proposed in the literature, such as the disturbance observer-based control (DOBC) and active disturbance rejection control (ADRC) methods, which have attracted the interest of researchers because of their promising results when dealing with intense perturbation rejection. The study by [4] introduced ADRC, a multi-loop methodology incorporating an extended state observer (ESO), nonlinear state error feedback, and a tracking differentiator (TD) to deal with the classical derivative block. While simulations conducted in Matlab/Simulink yielded promising results, experimental validation has not been provided. In the context of the DOBC method with model predictive controllers, [PERSON]. [5] addresses motion control for a four-degrees-of-freedom unmanned underwater vehicle where disturbances affecting the marine vehicle are precisely estimated and rejected. Similarly, in [6] a nonlinear disturbance observer based on predictive control of planar motors was proposed. The effectiveness of the ADRC approach was validated experimentally on a planar motor system. To develop trajectory tracking, [PERSON]. [7] highlighted the importance of incorporating signal processing, focusing on the combined use of unscented Kalman and cubature Kalman filters. Integrating Kalman filtering and sensor fusion has been addressed in various studies, including those by [8; 9] presenting solutions for managing sensor data. This filtering and estimation strategy is especially useful for handling disturbances in dynamic environments. In this work, it is used to improve measurement accuracy and address the challenges of quadcopter control.