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.

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.

In this paper, a fault-tolerant control method is proposed for quadcopter unmanned aerial vehicles (UAV) to account for system uncertainties and actuator faults. A mathematical model of the quadcopter UAV is first introduced when faults occur in actuators. A normal adaptive sliding mode control (NASMC) approach is proposed as a baseline controller to handle the chattering problem and system uncertainties, which does not require information of the upper bound. To improve the performance of the NASMC scheme, radial basis function neural networks are combined with an adaptive scheme to make a quick compensation in presence of system uncertainties and actuator faults. The Lyapunov theory is applied to verify the stability of the proposed methods. The effectiveness of modified ASMC algorithm is compared with that of NASMC using numerical examples under different faulty conditions.

In this paper, fault detection and fault-tolerant control strategies are proposed to handle the issues of both actuator faults and disturbances in a hexacopter. A dynamic model of a hexacopter is first derived to develop a model-based fault detection system. Secondly, the altitude control based on a sliding mode and disturbance observer is presented to tackle the disturbance issue. Then, a nonlinear Thau observer is applied to estimate the states of a hexacopter and to generate the residuals. Using a fault detection unit, the motor failure is isolated to address the one or two actuator faults. Finally, experimental results are tested on a DJI F550 hexacopter platform and Pixhawk2 flight controller to verify the effectiveness of the proposed approach. Unlike previous studies, this work can integrate fault detection and fault-tolerant control design as a single unit. Moreover, the developed fault detection and fault-tolerant control method can handle up to two actuator failures in presence of disturbances.

Fault-tolerant control has drawn attention in recent years owning to its reliability and safe flight during missions. In this article, an active fault-tolerant control method is proposed to control a quadcopter in the presence of actuator faults and disturbances. Firstly, the dynamics of the quadcopter are presented. Secondly, a robust adaptive sliding mode Thau observer is presented to estimate the time-varying magnitudes of actuator faults. Thirdly, a fault-tolerant control scheme based on sliding mode control and reconfiguration technique is designed to maintain the quadcopter at the desired position despite the presence of faults. Unlike previous studies, the proposed method aims to integrate the fault diagnosis and a fault-tolerant control scheme into a single unit with total loss of actuator. Simulation results illustrate the efficiency of the suggested algorithm.

Fault diagnosis (FD) is one of the main roles of fault-tolerant control (FTC) systems. An FD should not only identify the presence of a fault, but also quantify its magnitude and location. In this work, we present a robust fault diagnosis method for quadcopter unmanned aerial vehicle (UAV) actuator faults. The state equation of the quadcopter UAV is examined as a nonlinear system. An adaptive sliding mode Thau observer (ASMTO) method is proposed to estimate the fault magnitude through an adaptive algorithm. We then obtain the design matrices and parameters using the linear matrix inequalities (LMI) technique. Finally, experimental results are presented to show the advantages of the proposed algorithm. Unlike previous research on quadcopter UAV FD systems, our study is based on ASMTO and can, therefore, determine the time variability of a fault in the presence of external disturbances.