Distributed optimal control design with the feed-forward compensator for high-speed train

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS ISA transactions Pub Date : 2025-01-01 DOI:10.1016/j.isatra.2024.11.042
Wenjing Xi , Jilie Zhang , Zhanhua Chang , Yingchun Wang
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Abstract

The distributed optimal design of high-speed train movement is systematically investigated in this article. A distributed optimal control law is proposed, addressing the train consist of cars coupled by spring buffers, and is affected by aerodynamic drag and rolling resistance. A new distributed controller is proposed to decouple the train model by fully removing the in-train force, which greatly simplifies the complexity of calculation. Then the pending problem is redescribed to the control of cars with different mass. Grounded on the Lyapunov stability theory and optimal control theory, distributed optimal control law is proposed in line with guaranteed cost function, which enables faster updates of the real-time status of each car and adaptive vehicle mass. It ensures consistency in the tracking process of each car of the train, and further reduces the in-train force among cars. To eliminate the speed overshoot which results from the influence of acceleration change during train operation, we weigh in with the feed-forward compensator to assure the train’s good acceleration performance. Ultimately, numerical simulations results are obtained to demonstrate convincingly the significance of our proposed control law.
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高速列车前馈补偿分布式最优控制设计。
本文系统地研究了高速列车运动的分布式优化设计。针对列车由弹簧缓冲器耦合的车厢组成,并受到空气阻力和滚动阻力的影响,提出了分布式优化控制法则。提出了一种新的分布式控制器,通过完全消除列车内力来解耦列车模型,从而大大简化了计算的复杂性。然后,将悬而未决的问题重新描述为不同质量车辆的控制问题。以李雅普诺夫稳定性理论和最优控制理论为基础,提出了符合保证代价函数的分布式最优控制法则,从而能更快地更新每节车厢的实时状态和自适应车辆质量。它确保了列车每节车厢跟踪过程的一致性,并进一步降低了车厢间的内力。为了消除列车运行过程中受加速度变化影响而产生的速度过冲,我们采用了前馈补偿器,以确保列车具有良好的加速性能。最终,数值模拟结果令人信服地证明了我们提出的控制法则的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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