Abstract
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.
Highlights
Quadcopter unmanned aerial vehicles (UAVs) are used in a wide range of applications and research, oweing to their various benefits include agility, economical cost, small size, mechanical simplicity, and ability to operate in dangerous environments, which has led them to be more popular than other UAV systems
One problem with the attitude control of quadcopter UAVs is the uncertainties and unknown disturbances that the quadcopter is subjected to during operation. This issue has been investigated previously based on various control methods, such as adaptive control [9], sliding mode control [10,11], and backstepping control [12,13]
The results show good tracking performance in presence of actuator faults but the fault identification unit is required in this approach
Summary
Quadcopter unmanned aerial vehicles (UAVs) are used in a wide range of applications and research, oweing to their various benefits include agility, economical cost, small size, mechanical simplicity, and ability to operate in dangerous environments, which has led them to be more popular than other UAV systems. In references [24,25,26], the ASMC shows good results for the tracking performance with an actuator fault; these control approaches may not have sufficient robustness because the accurate model needs to be achieved in state space representation Another complex method based on a fuzzy system is proposed in reference [27]. The proposed method has some advantages such as using structure of RBF neural networks for fault identification and reconstruction, and using a simple adaptive scheme to avoid system uncertainties, and the chattering phenomenon This approach does not require the bound of uncertainty and fault detection unit which is different than the method in reference [32]. The stability of the system is verified using the Lyapunov theory
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