Data-Driven Koopman Model Predictive Control for Optimal Operation of High-Speed Trains

Chen, Bin; Huang, Zhiwu; Zhang, Rui; Liu, Weirong; Li, Heng; Wang, Jing; Fan, Yunsheng; Peng, Jun

doi:10.1109/access.2021.3086111

Cited by 15 publications

(5 citation statements)

References 39 publications

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“…Only one single study was found pertaining to this vehicle category, which was for an MPC application of a high-speed train whose linearized model was obtained via EDMD [49]. Remark 9 (Identified Gap in the Literature): For this vehicle class, we have not been able to find from the surveyed literature studies pertaining to trams, cable cars and roller coasters.…”

Section: G Railmentioning

confidence: 99%

Vehicular Applications of Koopman Operator Theory—A Survey

2023

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Koopman operator theory has proven to be a promising approach to nonlinear system identification and global linearization. For nearly a century, there had been no efficient means of calculating the Koopman operator for applied engineering purposes. The introduction of a recent computationally efficient method in the context of fluid dynamics, which is based on the system dynamics decomposition to a set of normal modes in descending order, has overcome this long-lasting computational obstacle. The purely data-driven nature of Koopman operators holds the promise of capturing unknown and complex dynamics for reduced-order model generation and system identification, through which the rich machinery of linear control techniques can be utilized. Given the ongoing development of this research area and the many existing open problems in the fields of smart mobility and vehicle engineering, a survey of techniques and open challenges of applying Koopman operator theory to this vibrant area is warranted. This review focuses on the various solutions of the Koopman operator which have emerged in recent years, particularly those focusing on mobility applications, ranging from characterization and component-level control operations to vehicle performance and fleet management. Moreover, this comprehensive review of over 100 research papers highlights the breadth of ways Koopman operator theory has been applied to various vehicular applications with a detailed categorization of the applied Koopman operator-based algorithm type. Furthermore, this review paper discusses theoretical aspects of Koopman operator theory that have been largely neglected by the smart mobility and vehicle engineering community and yet have large potential for contributing to solving open problems in these areas.

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Section: G Railmentioning

confidence: 99%

Vehicular Applications of Koopman Operator Theory—A Survey

2023

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“…In doing so, the ILC algorithm designed in [17] becomes feasible and it is able to ensure the convergence of the tracking process in the presence of time-varying adhesion dynamics between wheel and track via the definition of a new Composite Energy Function (CEF), without requiring the common assumption in ILC theory of globally Lipschitz property for the dynamic system. To face both uncertain dynamics and actuator saturation, an adaptive data-driven Koopman MPC strategy is introduced in [21] to solve the automatic train tracking control problem. Herein, firstly the Koopman operator theory is used to obtain an explicit linear dynamical train model that reflects train nonlinearities, thus resulting in a Koopman model involving an online adaptive mechanism able to cope with the changing dynamic characteristics; then MPC is designed under comfort and actuator constraints.…”

Section: A Related Workmentioning

confidence: 99%

A GoA4 Control Architecture for the Autonomous Driving of High-Speed Trains Over ETCS: Design and Experimental Validation

Barruffo,

Caiazzo,

Petrillo

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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“…Koopman operator is an infinite dimension data-driven linear operator, which can fully capture the nonlinear dynamics of the system [22], and provide a good practical value for the modelling of complex nonlinear systems. The vehicle operation data and Koopman operator is used to identify the linear dynamics model of urban rail vehicle [23]. For the dynamic system of urban rail vehicle, the observation function corresponding to each carriage is defined as…”

Section: Koopman Model Of Urban Rail Vehiclementioning

confidence: 99%

Cooperative braking of urban rail vehicles with Koopman model predictive control

Gao

Peng

et al. 2022

IET Control Theory & Appl

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Braking control of urban rail vehicles with multiple carriages is critical to ensure the safe operation of urban rails. However, existing decentralized braking control methods lead to the inconsistent speed and excessive coupler force among carriages, which compromises the operation safety of urban rails. To address this issue, a cooperative braking strategy is proposed for urban rail vehicles based on Koopman model predictive control method. First, a cyber‐physical model is established, where the physical layer characterizes the dynamic model of multiple carriages using the Koopman operator. The cyber layer represents the communication topology of carriages with graph theory. Second, a cooperative braking controller is designed with the distributed model predictive control method. An optimisation problem is formulated to minimize the speed inconsistency and relative displacement difference among carriages. Third, extensive simulations under various scenarios, including normal and extreme operating conditions, are conducted to verify the effectiveness of the proposed method. Simulation results show that, compared with the classical decentralized method, the proposed braking method reduces the average relative displacement by 78% while reducing the speed error by 29%.

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Data-Driven Koopman Model Predictive Control for Optimal Operation of High-Speed Trains

Cited by 15 publications

References 39 publications

Vehicular Applications of Koopman Operator Theory—A Survey

Vehicular Applications of Koopman Operator Theory—A Survey

A GoA4 Control Architecture for the Autonomous Driving of High-Speed Trains Over ETCS: Design and Experimental Validation

Cooperative braking of urban rail vehicles with Koopman model predictive control

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