Multistep model predictive control with current and voltage constraints for linear induction machine based urban transportation

Zou, J, Xu, W, Yu, X, Liu, Y and Ye, C 2017, 'Multistep model predictive control with current and voltage constraints for linear induction machine based urban transportation', IEEE Transactions on Vehicular Technology, vol. 66, no. 12, pp. 10817-10829.


Document type: Journal Article
Collection: Journal Articles

Title Multistep model predictive control with current and voltage constraints for linear induction machine based urban transportation
Author(s) Zou, J
Xu, W
Yu, X
Liu, Y
Ye, C
Year 2017
Journal name IEEE Transactions on Vehicular Technology
Volume number 66
Issue number 12
Start page 10817
End page 10829
Total pages 13
Publisher IEEE.
Abstract In order to improve the working performance of linear metro, the multistep model predictive control (MMPC) is applied to linear induction machine (LIM) in this paper. However, due to the maximum current and voltage limitations, the optimal problem of multistep model predictive control (MMPC) becomes difficult to handle in practice. For simplifying the optimal problem of an MMPC with constraints, first, the optimal problem of MMPC is solved off-line, with the assumption that the input voltage of the LIM is continuous without limitations. And, the expression of current constraint is expressed by voltage variables through the mathematical model of the LIM to simultaneously consider both the voltage and current limitations. Then, according to the solved optimal value without constraints, one iterative algorithm is proposed to search a suboptimal value, which satisfies both the current and voltage limitations. Finally, the proposed strategy is applied to two 3-kW arc induction motors to verify its effectiveness.
Subject Control Systems, Robotics and Automation
Industrial Electronics
Keyword(s) Arc induction motor (AIM)
Linear induction machine (LIM)
Multistep model predictive control (MMPC)
Urban transportation
DOI - identifier 10.1109/TVT.2017.2736533
Copyright notice © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
ISSN 0018-9545
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