In this article, a sliding mode control based on neural networks is proposed for attitude and altitude system of quadcopter under external disturbances. First, the dynamic model of the quadcopter is considered under external disturbances. Sliding mode controllers are then integrated with neural network algorithm to achieve the time-varying sliding surface; their coefficients in sliding surface are adjusted through backpropagation law. The disturbance observer is also combined with sliding mode controllers to estimate and handle the external disturbances. Finally, the Lyapunov theory is applied to validate the stability of suggested control method. The performance of proposed sliding mode control has been evaluated using a numerical simulation. The results show that the attitude and altitude controller based on suggested algorithm has a better tracking performance and disturbance rejection.

Quadcopter unmanned aerial vehicles continue to play important roles in several applications and the improvement of their control performance has been explored in a great number of studies. In this paper, we present an altitude control algorithm for quadcopters that consists of a combination of nonlinear and linear controllers. The smooth transition between the nonlinear and linear modes are guaranteed through controller gains that are obtained based on mathematical analysis. The proposed controller takes advantage and addresses some known shortcomings of the conventional proportional–integral–derivative control method. The algorithm is simple to implement, and we prove its stability through the Lyapunov theory. By prescribing certain flight conditions, we use numerical simulations to compare the control performance of our control method to that of a conventional proportional–derivative–integral approach. Furthermore, we use a DJI-F450 drone equipped with a laser ranging sensor as the experimental quadcopter platform to evaluate the performance of our new controller in real flight conditions. Numerical simulation and experimental results demonstrate the effectiveness of the proposed algorithm.

This paper presents a lumped perturbation observer-based robust control method using an extended multiple sliding surface for a system with matched and unmatched uncertainties. The fundamental methodology is to apply the multiple surfaces to approximate the unknown lumped perturbations simultaneously influencing on a nonlinear single input–single output (SISO) system. Subsequently, a robust controller, based on the proposed multi-surface and the approximated values, is designed to highly improve the control performance of the system. A general stability of the lumped perturbation observer and closed-loop control system is obtained through the Lyapunov theory. Results of a numerical simulation of an illustrative example demonstrate the soundness of the proposed algorithm.

Nowadays, quadcopter unmanned aerial vehicles play important roles in several real-world applications and the improvement of their control performance has become an increasingly attractive topic of a great number of studies. In this paper, we present a new approach for the design and stability analysis of a quadcopter adaptive trajectory tracking control. Based on the quadcopter nonlinear dynamics model which is obtained by using the Euler–Lagrange approach, the tracking controller is devised via the backstepping control technique. Besides, an adaptive law is proposed to deal with the system parameterized uncertainties and to guarantee that the control input is finite. In addition, the vehicle’s vertical descending acceleration is ensured to not exceed the gravitational acceleration by making use of a barrier Lyapunov function. It is shown that the suitable parameter estimator is stable and the tracking errors are guaranteed to be asymptotically stable simultaneously. By prescribing certain flight conditions, we use numerical simulations to compare the control performance of our method to that of existing approaches. The simulation results demonstrate the effectiveness of the proposed algorithm.

Nowadays, with the increasing popularity of quadcopter unmanned aerial vehicles in several real-world applications, achieving a fully autonomous quadcopter flight has become an imperative topic investigated in many studies. One of the most pressing issues in such a topic is the precision landing task, which always is devastatingly influenced by the ground effect and external disturbances. In this paper, we present an autonomous quadcopter landing algorithm allowing the vehicle to land robustly and precisely onto a heaving platform. Firstly, a robust control algorithm addressing the altitude flight under the ground effect and external disturbances is derived. We strictly prove the closed-loop system stability by using the Lyapunov theory. Secondly, a landing target state estimator is proposed to provide state estimations of the moving landing target. In addition, we propose a landing procedure to ensure the landing task is achieved safely and reliably. Finally, we use a DJI-F450 drone equipped with an infrared sensor and a laser ranging sensor as the experimental quadcopter platform and conduct experiments to evaluate the performance of our new algorithm in real flight conditions. The experimental results demonstrate the effectiveness of the proposed method.