Nonlinear Model Predictive Control for Longitudinal Tracking of Maglev Cars
Journal Title: Journal of Intelligent Systems and Control - Year 2024, Vol 3, Issue 1
Abstract
In the era of low-carbon travel, maglev cars emerge as a high-speed, environmentally sustainable solution, leveraging their frictionless, smooth operation. This study introduces a nonlinear dynamic model for the longitudinal dynamics of maglev cars, constructed via a data-driven approach. A nonlinear model predictive control (NMPC) strategy, incorporating rotational speed constraints, is developed to address the inherent instability of the open-loop system. The dynamic relationship between the driving force and the rotational speeds of magnetic wheels was quantified using the least squares method (LSM) based on tests conducted across varied rotational speeds. A single-degree-of-freedom model, integrating stiffness and damping characteristics, was subsequently formulated to describe the longitudinal motion of the maglev car. The model’s validity was confirmed through comparison with experimental outputs under varying conditions. Further, the stiffness and damping coefficients were derived from experimental data, enhancing the model’s precision. Control simulations and real-world experiments under diverse operational conditions demonstrated the efficacy of the NMPC in ensuring robust longitudinal tracking. This investigation substantiates the NMPC approach as an effective control strategy for enhancing the stability and performance of maglev transportation systems.
Authors and Affiliations
Huiyang Yi, Zhihao Ke, Jinbin Zou, Jiaheng Shi, Zigang Deng
Keywords
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- EP ID EP735208
- DOI https://doi.org/10.56578/jisc030104
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