Peaking Free HGO Based Neural Hypersonic Flight Vehicle Control

摘要：

This paper describes the design of adaptive neural controller for the longitudinal dynamics of a generic hypersonic flight vehicle (HFV). For the altitude subsystem, the dynamics are transformed into the normal feedback form and the high gain observer (HGO) is taken to estimate the unknown newly defined states. Only one Neural Network (NN) is employed to approximate the lumped uncertain system nonlinearity which is considerably simpler than the backstepping scheme with the strict-feedback form. Furthermore, the saturation design is applied on the HGO estimation error to eliminate the peaking phenomenon. For the velocity subsystem, dynamic inverse NN controller is designed. The Lyapunov stability of the NN weights and filtered tracking error are guaranteed in the semiglobal sense. The effectiveness of the proposed strategy is verified by numerical simulation study.

关键词： Hypersonic Flight Vehicle High Gain Observer Neural Network Controller Design

作者: Wang Shixing Xu Bin Sun Fuchun

作者单位: Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

会议类型: 国际会议

会议名称: 2012 International Conference on Intelligent System Design and Engineering Applications(2012年智能系统设计与工程应用国际会议 ISDEA 2012)

会议地点: 三亚

会议语种:英文

页码: 1103-1109

在线出版日期: 2012-01-06（万方平台首次上网日期，不代表论文的发表时间）