Neural Network-Based Adaptive Backstepping Control for Hypersonic Flight Vehicles with Prescribed Tracking Performance

Zhu Guoqiang and Liu Jinkun

Mathematical Problems in Engineering, 2015, vol. 2015, 1-10

Abstract:

An adaptive neural control scheme is proposed for a class of generic hypersonic flight vehicles. The main advantages of the proposed scheme include the following: (1) a new constraint variable is defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries; (2) RBF NNs are employed to compensate for complex and uncertain terms to solve the problem of controller complexity; (3) only one parameter needs to be updated online at each design step, which significantly reduces the computational burden. It is proved that all signals of the closed-loop system are uniformly ultimately bounded. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:591789

DOI: 10.1155/2015/591789

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