A guaranteed transient performance-based adaptive neural control scheme with low-complexity computation for flexible air-breathing hypersonic vehicles

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• 作者：Xiangwei Bu ; Xiaoyan Wu ; Jiaqi Huang ; Daozhi Wei
• 刊名：Nonlinear Dynamics
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：84
• 期：4
• 页码：2175-2194
• 全文大小：1,845 KB
• 刊物类别：Engineering
• 刊物主题：Vibration, Dynamical Systems and Control
  Mechanics
  Mechanical Engineering
  Automotive and Aerospace Engineering and Traffic
  
• 出版者：Springer Netherlands
• ISSN：1573-269X
• 卷排序：84

文摘

A robust adaptive neural control scheme is addressed for a generic flexible air-breathing hypersonic vehicle, capable of guaranteeing velocity and altitude tracking errors with desired transient performance. Different from the back-stepping design, a novel neural approximation controller is explored for the altitude subsystem based on a quite simple normal output-feedback formulation rather than a strict-feedback one, while there is no need of the complex recursive design procedure of virtual control laws. Furthermore, on the basis of the minimal learning parameter technique, the updating parameters are reduced greatly. Thus, the exploited strategy exhibits good low-complexity computation. In particular, a new finite-time-convergent differentiator is devised to estimate the newly generated states and it is also employed to provide the necessary high-order time derivatives of reference commands, based on which the proposed control methodology becomes achievable. Finally, the effectiveness of the design is confirmed by simulation results.KeywordsFlexible air-breathing hypersonic vehicle (FAHV)Neural controlTransient performanceMinimal learning parameter (MLP)Finite-time-convergent differentiator