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Online Recorded Data-Based Composite Neural Control of Strict-Feedback Systems With Application to Hypersonic Flight Dynamics.
- Source :
-
IEEE transactions on neural networks and learning systems [IEEE Trans Neural Netw Learn Syst] 2018 Aug; Vol. 29 (8), pp. 3839-3849. Date of Electronic Publication: 2017 Sep 25. - Publication Year :
- 2018
-
Abstract
- This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using the backstepping framework. In each step of the virtual control design, neural network (NN) is employed for uncertainty approximation. In previous works, most designs are directly toward system stability ignoring the fact how the NN is working as an approximator. In this paper, to enhance the learning ability, a novel prediction error signal is constructed to provide additional correction information for NN weight update using online recorded data. In this way, the neural approximation precision is highly improved, and the convergence speed can be faster. Furthermore, the sliding mode differentiator is employed to approximate the derivative of the virtual control signal, and thus, the complex analysis of the backstepping design can be avoided. The closed-loop stability is rigorously established, and the boundedness of the tracking error can be guaranteed. Through simulation of hypersonic flight dynamics, the proposed approach exhibits better tracking performance.
Details
- Language :
- English
- ISSN :
- 2162-2388
- Volume :
- 29
- Issue :
- 8
- Database :
- MEDLINE
- Journal :
- IEEE transactions on neural networks and learning systems
- Publication Type :
- Academic Journal
- Accession number :
- 28952951
- Full Text :
- https://doi.org/10.1109/TNNLS.2017.2743784