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Online Adaptive Error Compensation SVM-Based Sliding Mode Control of an Unmanned Aerial Vehicle

机译:基于SVM的在线自适应误差补偿滑模控制

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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.
机译:无人机(UAV)是具有不确定性和噪声的非线性动力学系统。因此,适当的控制系统有义务确保无人机的稳定和导航。本文主要讨论受未知参数和噪声影响的四旋翼无人机系统的控制问题。此外,提出了一种基于在线自适应误差补偿支持向量机(SVM)的滑模控制,以稳定四旋翼无人机系统。通过分析四转子动力学模型建立滑模控制器,其中离线SVM计算未知参数。在此过程中,采用了在线自适应误差补偿支持向量机方法。由于在飞行过程中都存在建模误差和噪声,因此离线SVM一次性模式无法准确预测不确定性和噪声。通过在控制过程中向在线自适应SVM中引入新的训练样本数据来实时调整控制律,从而确保飞行的稳定性和鲁棒性。通过仿真实验可以证明,加入在线自适应SVM的无人机可以根据其动态模型更快地跟踪变化路径。因此,所提出的方法在无人机系统中具有较好的控制效果。

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