Deep Learning Based Semi-Supervised Control for Vertical Security of Maglev Vehicle With Guaranteed Bounded Airgap
نویسندگان
چکیده
The vertical security problem of maglev train is challenging for nonlinearity, external disturbances, unmeasurable airgap velocity and constrained output. To solve this problem, a semi-supervised controller based on deep belief network (DBN) algorithm proposed in the presence unknown disturbances. Firstly, extended state observer (ESO) designed to ensure fast convergence observation errors with high enough estimation precision. An output-constrained by backstepping method, estimated value ESO introduced that output within bounded range. Then, stability method proved symmetric Barrier Lyapunov function. Subsequently, presented DBN controller. numerical simulation results show can effectively deal generalized guarantee Finally, experiments are implemented full-scale vehicle experimental demonstrate developed learning security.
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ژورنال
عنوان ژورنال: IEEE Transactions on Intelligent Transportation Systems
سال: 2021
ISSN: ['1558-0016', '1524-9050']
DOI: https://doi.org/10.1109/tits.2020.3045319