Nonlinear robust sliding mode control of a quadrotor unmanned aerial vehicle based on immersion and invariance method

2014 ◽  
Vol 25 (18) ◽  
pp. 3714-3731 ◽  
Author(s):  
Bo Zhao ◽  
Bin Xian ◽  
Yao Zhang ◽  
Xu Zhang
Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 54
Author(s):  
Minh-Thien Tran ◽  
Dong-Hun Lee ◽  
Soumayya Chakir ◽  
Young-Bok Kim

This article proposes a novel adaptive super-twisting sliding mode control scheme with a time-delay estimation technique (ASTSMC-TDE) to control the yaw angle of a single ducted-fan unmanned aerial vehicle system. Such systems are highly nonlinear; hence, the proposed control scheme is a combination of several control schemes; super-twisting sliding mode, TDE technique to estimate the nonlinear factors of the system, and an adaptive sliding mode. The tracking error of the ASTSMC-TDE is guaranteed to be uniformly ultimately bounded using Lyapunov stability theory. Moreover, to enhance the versatility and the practical feasibility of the proposed control scheme, a comparison study between the proposed controller and a proportional-integral-derivative controller (PID) is conducted. The comparison is achieved through two different scenarios: a normal mode and an abnormal mode. Simulation and experimental tests are carried out to provide an in-depth investigation of the performance of the proposed ASTSMC-TDE control system.


2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Kaijia Xue ◽  
Congqing Wang ◽  
Zhiyu Li ◽  
Hanxin Chen

Unmanned Aerial Vehicle (UAV) is a nonlinear dynamic system with uncertainties and noises. Therefore, an appropriate control system has an obligation to ensure the stabilization and navigation of UAV. This paper mainly discusses the control problem of quad-rotor UAV system, which is influenced by unknown parameters and noises. Besides, a sliding mode control based on online adaptive error compensation support vector machine (SVM) is proposed for stabilizing quad-rotor UAV system. Sliding mode controller is established through analyzing quad-rotor dynamics model in which the unknown parameters are computed by offline SVM. During this process, the online adaptive error compensation SVM method is applied in this paper. As modeling errors and noises both exist in the process of flight, the offline SVM one-time mode cannot predict the uncertainties and noises accurately. The control law is adjusted in real-time by introducing new training sample data to online adaptive SVM in the control process, so that the stability and robustness of flight are ensured. It can be demonstrated through the simulation experiments that the UAV that joined online adaptive SVM can track the changing path faster according to its dynamic model. Consequently, the proposed method that is proved has the better control effect in the UAV system.


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