Fault Tolerant Tracking Control for Near Space Hypersonic Vehicle Via Neural Network

摘要：

In this paper, a fault tolerant control (FTC) strategy is investigated for Near Space Hypersonic Vehicle (NSHV) based on neural network and adaptive backstepping design. Firstly, a radial basis function (RBF) neural network (NN) is used to approximate the nonlinear dynamics, a neural network observer is constructed to estimate the unknown system fault, the adaptive on-line parameter-updating laws are derived, and the stability of the state error dynamic is guaranteed. Then an adaptive backstepping based fault tolerant controller is designed for the faulty system. The asymptotical stability of the closed-loop system and uniform boundedness of the state tracking error are proved according to Lyapunov theorem. Finally, simulation results on the NSHV attitude dynamics demonstrate the effectiveness of the proposed scheme.

作者: Yufei Xu Bin Jiang Zhifeng Gao

作者单位: Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing,210016,china

会议类型: 国际会议

会议名称: The 3rd International Symposium on Systems and Control in Aeronautics and Astronautics(第三届航空航天系统与控制国际会议 ISSCAA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 614-619

在线出版日期: 2010-01-08（万方平台首次上网日期，不代表论文的发表时间）