Decentralized learning control for high-speed trains with unknown time-varying speed delays

IF 4.4 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY Applied Mathematical Modelling Pub Date : 2024-09-19 DOI:10.1016/j.apm.2024.115711
Shuai Gao , Qijiang Song , Hao Jiang , Dong Shen , Yisheng Lv
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Abstract

Treating the multi-point-mass dynamic model of high-speed trains as an interconnected system, this study proposes a decentralized iterative learning control scheme for high-speed trains to achieve the trajectory tracking goal. By making reasonable estimates of the interaction term and compensating for it, the proposed control scheme utilizes only local information from each carriage and does not need any inter-carriage information exchange. The zero-error tracking of the desired trajectory is guaranteed even in a restricted communication environment. Considering unknown time-varying speed delays in the actual high-speed train operations, a modified decentralized iterative learning control scheme is also provided to address the negative impact of speed delays. The convergence of tracking errors is strictly proven by constructing appropriate composite energy functions. Numerical simulations further verify the theoretical results.
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具有未知时变速度延迟的高速列车的分散学习控制
本研究将高速列车的多点-质量动态模型视为一个互联系统,提出了一种用于高速列车的分散迭代学习控制方案,以实现轨迹跟踪目标。通过对交互项进行合理估计和补偿,所提出的控制方案只利用每节车厢的本地信息,而不需要任何车厢间的信息交换。即使在通信受限的环境下,也能保证对所需轨迹的零误差跟踪。考虑到实际高速列车运行中未知的时变速度延迟,还提供了一种改进的分散迭代学习控制方案,以解决速度延迟的负面影响。通过构建适当的复合能量函数,严格证明了跟踪误差的收敛性。数值模拟进一步验证了理论结果。
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
Applied Mathematical Modelling
Applied Mathematical Modelling 数学-工程:综合
CiteScore
9.80
自引率
8.00%
发文量
508
审稿时长
43 days
期刊介绍: Applied Mathematical Modelling focuses on research related to the mathematical modelling of engineering and environmental processes, manufacturing, and industrial systems. A significant emerging area of research activity involves multiphysics processes, and contributions in this area are particularly encouraged. This influential publication covers a wide spectrum of subjects including heat transfer, fluid mechanics, CFD, and transport phenomena; solid mechanics and mechanics of metals; electromagnets and MHD; reliability modelling and system optimization; finite volume, finite element, and boundary element procedures; modelling of inventory, industrial, manufacturing and logistics systems for viable decision making; civil engineering systems and structures; mineral and energy resources; relevant software engineering issues associated with CAD and CAE; and materials and metallurgical engineering. Applied Mathematical Modelling is primarily interested in papers developing increased insights into real-world problems through novel mathematical modelling, novel applications or a combination of these. Papers employing existing numerical techniques must demonstrate sufficient novelty in the solution of practical problems. Papers on fuzzy logic in decision-making or purely financial mathematics are normally not considered. Research on fractional differential equations, bifurcation, and numerical methods needs to include practical examples. Population dynamics must solve realistic scenarios. Papers in the area of logistics and business modelling should demonstrate meaningful managerial insight. Submissions with no real-world application will not be considered.
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