Abstract
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.
Highlights
Unmanned Aerial Vehicle (UAV) has drawn a great attention from researchers in the past few years
This paper presents a method that standard support vector machine (SVM) is combined with online adaptive error compensation SVM to design a sliding mode controller
The sliding mode controller (19) is established by using the offline SVM to predict the model when there are some unknown parameters in the UAV system
Summary
Unmanned Aerial Vehicle (UAV) has drawn a great attention from researchers in the past few years. Standard offline support vector machine (SVM) is a machine learning based on statistical theory It can solve small-sample, nonlinear, and high dimension problems by using structural risk minimization. This method can efficiently supply the details without considering the changes of modeling the quad-rotor system. This paper presents a method that standard SVM is combined with online adaptive error compensation SVM to design a sliding mode controller. Through this way, the weights of SVM can be adjusted adaptively to an optimal status. This paper proves the effectiveness of this method through the simulation results of the quad-rotor system
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