constrained adaptive neural control for air-breathing hypersonic vehicles without backstepping

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ID: 198152
2018
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Abstract
An adaptive neural control scheme without backstepping is proposed for the air-breathing hypersonic vehicle subject to input constraints. To estimate the unknown nonlinearity of velocity subsystem and altitude subsystem, two radial basis function neural networks (RBFNNs) are constructed. Since the complex backstepping design steps are not needed, the proposed control structure is quite concise and the problem of “explosion of terms” is avoided. Moreover, a novel nonlinear auxiliary system is constructed to solve the problem of input constraints. The advantage of the proposed auxiliary system is that its high-order form has good performance and the parameter tuning is relatively easy. Simulation results show that the designed controllers achieve stable tracking of reference commands with good performance.
Reference Key
tan2018mathematicalconstrained Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Shili Tan;Humin Lei;Pengfei Wang
Journal journal of power sources
Year 2018
DOI
10.1155/2018/6968526
URL
http://dx.doi.org/10.1155/2018/6968526
Keywords
civil engineering (general)mathematics engineering (general)

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