Adaptive neural network control for maglev vehicle systems with time-varying mass and external disturbance

Unexpected disturbance and ever-changing passengers are unfavorable factors that always accompany maglev trains. If not considered or handled properly, they would deteriorate the control system performance significantly and even cause instability. This paper proposes a neural network-based adaptive...

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Bibliographic Details
Published in:Neural computing & applications 2023-06, Vol.35 (17), p.12361-12372
Main Authors: Sun, Yougang, Xu, Junqi, Lin, Guobin, Sun, Ning
Format: Article
Language:English
Subjects:
Adaptive control
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Control theory
Data Mining and Knowledge Discovery
Electromagnets
Image Processing and Computer Vision
Levitation
Magnetic levitation vehicles
Network control
Neural networks
Nonlinear control
Probability and Statistics in Computer Science
Radial basis function
S.I. : New Trends of Neural Computing for Advanced Applications
Special Issue on New Trends of Neural Computing for Advanced Applications
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Summary:Unexpected disturbance and ever-changing passengers are unfavorable factors that always accompany maglev trains. If not considered or handled properly, they would deteriorate the control system performance significantly and even cause instability. This paper proposes a neural network-based adaptive control approach to stabilize the airgap of the nonlinear maglev vehicle. Meanwhile, the time-varying mass and external disturbance can be estimated accurately. Specifically, to ensure the asymptotic stability of the maglev system, a nonlinear basic control law is developed first. To tackle the uncertainty, a radial basis function neural network is fused into the basic controller, which can recover the unknown mass and disturbance more quickly and accurately. Lyapunov stability techniques are utilized to prove the stability of the whole maglev control system without any linear approximation. The sufficient comparative simulation results are provided to demonstrate that the established control scheme can obtain better levitation performance and achieve a precise estimation of time-varying and disturbance simultaneously. Finally, we build a dSPACE-based single electromagnet suspension test bed to examine its efficacy and practical applicability as well.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-021-05874-2