Cooperative $H_{\infty}$  Control of Multiple High-Speed Trains With Saturation Constraints

Xiang, Peng; Wang, Xiaoli; Mo, Lipo

doi:10.1109/access.2019.2939953

Cited by 12 publications

(7 citation statements)

References 21 publications

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“…However, the mode of controlling through target distance curve protection cannot maximize the utilization of line space and improve transportation efficiency. Because in the actual driving process, it is impossible for the forward train to instantly change from a operating state to a stationary state [1].…”

Section: Introductionmentioning

confidence: 99%

Automatic Tracking Control Strategy of Autonomous Trains Considering Speed Restrictions: Using the Improved Offline Deep Reinforcement Learning Method

Liu,

Feng,

Xiao

et al. 2024

IEEE Access

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Previous research on automatic control of high-speed trains in speed limit sections is insufficient. This article proposes a new offline reinforcement learning strategy for automatic tracking of autonomous trains. Firstly, the operating speed and deceleration starting point were determined for different speed limit scenarios. Then, a tracking controller based on the improved offline conservative Q-learning (CQL) algorithm was designed to avoid frequent interaction between the train and the environment. Selected an appropriate policy to implement the CQL algorithm. The data samples were reclassified to increase sample concentration. The value and strategy network structure was redesigned. The state space and action space of tracking trains were limited, and the dimension of state space was increased. A multi-objective reward function was designed to distinguish the tracking process of trains in different sections. The simulation results show that the proposed high-speed railway tracking interval automatic control algorithm is superior to traditional online reinforcement learning methods in terms of safety, comfort, and convergence efficiency.

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Section: Introductionmentioning

confidence: 99%

Automatic Tracking Control Strategy of Autonomous Trains Considering Speed Restrictions: Using the Improved Offline Deep Reinforcement Learning Method

Liu,

Feng,

Xiao

et al. 2024

IEEE Access

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“…It has been challenging to meet the safety and efficiency criteria of fully automatic operation of urban rail transit trains under high density and lengthy periods of time, single‐train operation control. The multiple trains cooperative control (MTCC) is a solution to realize global optimization and guarantee scheme recital (Gao et al, 2018; Xiang et al, 2019). The problem of controlling a multi‐agent robotic system to achieve a common goal is addressed through cooperative control.…”

Section: Introductionmentioning

confidence: 99%

Cooperative robust adaptive control of multiple trains based on RBFNN position output constraints

2022

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The stability of each train, high control accuracy, and minimum safe separation distance are important indexes to measure the performance of the cooperative control system of multiple trains. In this article, aiming at the problem of low accuracy of multiple trains cooperative control with nonlinear running resistance and external disturbance, the distributed cooperative robust adaptive control scheme for multiple trains with RBFNN position output constraints based on train running curve tracking is proposed. Multiple different control techniques are offered for different trains, and that they are based on local knowledge of position, speed, and acceleration. The leading train's speed and position precisely match the planned operation curve, while the following train keeps the tracking interval at the minimum safe distance between two trains. In order to reduce the influence of the uncertainty of basic resistance parameters and external interference on the cooperative control of multiple trains, the parameter uncertainties are compensated by adding a robust adaptive law to the multiple trains control based on position output constraints. The lumped exogenous disturbances (additional resistance, external interference, measurement noise, etc.) are estimated using an RBFNN approximator for the unknown term of the cooperative system. The stability of the cooperative operation of multiple trains is confirmed using the Lyapunov stability theorem. The performance of the proposed scheme was evaluated by the cooperative control system of multiple trains in predecessor following (PF) and bidirectional control (BC) modes.

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“…A back-stepping adaptive controller was designed in [5] for the position tracking control of high-speed trains. H ∞ control methods were proposed in [6], [7] for the speed tracking control with consideration of uncertain train mass and resistance. The robust control method was developed in [8] for high-speed trains to inhibit the external disturbances.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Automatic train operation systems of high-speed trains are critical to guarantee operational safety, comfort, and parking accuracy. However, implementing optimal automatic operation control is challenging due to the train's uncertain dynamics and actuator saturation. To address this issue, this paper develops a data-driven Koopman model based predictive control method for automatic train operation systems. The proposed control scheme is designed within a data-driven framework. First, using operational data of trains and the Koopman operator, an explicit linear Koopman model is built to characterize the train dynamics. Then, a model predictive controller is designed based on the Koopman model under comfort and actuator constraints. Furthermore, an online update mechanism for the Koopman model is developed to cope with the changing dynamic characteristics of trains, which reduces the accumulation errors and improves control performance. Stability analysis of the closed-loop control system is provided. Comparative simulation results validate the effectiveness of the proposed control approach.

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Cooperative $H_{\infty}$ Control of Multiple High-Speed Trains With Saturation Constraints

Cited by 12 publications

References 21 publications

Automatic Tracking Control Strategy of Autonomous Trains Considering Speed Restrictions: Using the Improved Offline Deep Reinforcement Learning Method

Automatic Tracking Control Strategy of Autonomous Trains Considering Speed Restrictions: Using the Improved Offline Deep Reinforcement Learning Method

Cooperative robust adaptive control of multiple trains based on RBFNN position output constraints

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

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