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The purpose of this paper is to propose a new approach for robust control of an autonomous quadrotor unmanned aerial vehicle (UAV) in automatic take-off, hovering and landing mission and also to improve the stabilizing performance of the quadrotor with inherent time-varying disturbance.

First, the dynamic model of the aerial vehicle is mathematically formulated. Then, a combination of a nonlinear backstepping scheme with the intelligent fuzzy system as a new key idea to generate a robust controller is designed for the stabilization and altitude tracking of the vehicle. For the problem of determining the backstepping control parameters, a new heuristic algorithm, namely, Gravitational Search Algorithm has been used.

The control law design utilizes the backstepping control methodology that uses Lyapunov function which can guarantee the stability of the nominal model system, whereas the intelligent system is used as a compensator to attenuate the effects caused by external disturbances. Simulation results demonstrate that the proposed control scheme can achieve favorable control performances for automatic take-off, hovering and landing mission of quadrotor UAV even in the presence of unknown perturbations.

This paper propose a new robust control design approach which incorporates the backstepping control with fuzzy system for quadrotor UAV with inherent time-varying disturbance. The originality of this work relies on the technique to compensate the disturbances acting on the quadrotor UAV. In this new approach, the fuzzy system is introduced as an auxiliary control effort to compensate the effect of disturbances. Because the proposed control technique has the capability of robustness against disturbance, thus, it is also suitable to be applied for a broad class of uncertain nonlinear systems.

This paper aims to propose a new multi-layered optimal navigation system that jointly optimizes the energy consumption, improves the robustness and raises the performance of a quadrotor unmanned aerial vehicle (UAV).

The proposed system is designed as a multi-layered system. First, the control architecture layer links the input and the output spaces via quaternion-based differential flatness equations. Then, the trajectory generation layer determines the optimal reference path and avoids obstacles to secure the UAV from collisions. Finally, the control layer allows the quadrotor to track the generated path and guarantees the stability using a double loop non-linear optimal backstepping controller (OBS).

All the obtained results are confirmed using several scenarios in different situations to prove the accuracy, energy optimization and the robustness of the designed system.

The proposed controllers are easily implementable on-board and are computationally efficient.

The originality of this research is the design of a multi-layered optimal navigation system for quadrotor UAV. The proposed control architecture presents a direct relation between the states and their derivatives, which then simplifies the trajectory generation problem. Furthermore, the derived differentially flat equations allow optimization to occur within the output space as opposed to the control space. This is beneficial because constraints such as obstacle avoidance occur in the output space; hence, the computation time for constraint handling is reduced. For the OBS, the novelty is that all controller parameters are derived using the multi-objective genetic algorithm (MO-GA) that optimizes all the quadrotor state’s cost functions jointly.