Optimal Operation of Heavy-Haul Trains Equipped With Electronically Controlled Pneumatic Brake Systems Using Model Predictive Control Methodology

Zhang, Lijun; Zhuan, Xiangtao

doi:10.1109/tcst.2013.2238235

Cited by 95 publications

(57 citation statements)

References 18 publications

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“…This is due to its facility of handling constraints, its ability of using simple models, and its closed-loop stability and inherent robustness in many applications. In the transportation field, MPC has been employed to solve the path-tracking problems of terrestrial autonomous vehicles [25] and heavy-haul trains [35]. Researchers have also applied MPC to boom cranes [1] and gauntry cranes [10] for tracking and anti-swing.…”

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confidence: 99%

Model predictive control for improving operational efficiency of overhead cranes

2014

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Model predictive control (MPC) has been successfully applied to many transportation systems. For the control of overhead cranes, existing MPC approaches mainly focus on improving the regulation performance, such as tracking error or steady-state error. In this paper, energy efficiency as well as safety is newly considered in our proposed MPC approach. Based on the system model designed, the MPC approach is applied to minimize an objective function that is formulated as the integration of energy consumption and swing angle. In our approach, promising results in terms of low energy consumption and small swing angle can be found, whilst the solutions obtained can satisfy all practical constraints. Our test results indicate that the MPC approach can ensure stability and robustness of improving energy efficiency and safety.

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confidence: 99%

Model predictive control for improving operational efficiency of overhead cranes

2014

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“…It is desirable to optimize the train's operation during a long travel rather than at a specific position such that the future states of the train and the incoming track information are accounted for. In view of this, model predictive control (MPC) approaches were introduced in [23] and [24] to optimize the trains behavior over a chosen period taking into account all physical and operational constraints. The MPC approach, with its intrinsic advantages of tackling constrained operation control problem and optimizing system behavior by looking ahead, has been successfully applied to many industrial systems [25], [26], [27], [28].…”

Section: X(t) and V(t) Are The Position And Velocity Of The Train At mentioning

confidence: 99%

“…The MPC approach, with its intrinsic advantages of tackling constrained operation control problem and optimizing system behavior by looking ahead, has been successfully applied to many industrial systems [25], [26], [27], [28]. The work presented in [23] and [24] show the resultant improvement in train performance in terms of velocity tracking, in-train force minimization and energy consumption reduction.…”

Section: X(t) and V(t) Are The Position And Velocity Of The Train At mentioning

confidence: 99%

“…Further, there are three factors not considered in previous works including [7], [9], [23] and [24]. Firstly, it is concluded in [29] that statistically, 10-20% of the energy consumed by a train is used to compensate kinetic energy loss resulted from braking.…”

Section: X(t) and V(t) Are The Position And Velocity Of The Train At mentioning

confidence: 99%

“…The train model and operation constraints are the same as detailed in the previous works [23], [24], [33]. The cascade mass point model (2) and operation constraints described by (4) are used.…”

Section: A Problem In Generalmentioning

confidence: 99%

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Development of an Optimal Operation Approach in the MPC Framework for Heavy-Haul Trains

Zhang

Zhuan

2015

IEEE Trans. Intell. Transport. Syst.

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Abstract-An operation control approach for heavy haul trains to optimize their performance, including operation safety, service quality and energy consumption, is proposed. Following a model predictive control method, the controller is capable of scheduling a train to operate optimally during a long section of the rail track. In the cost function, two penalty factors are presented, one for the braking forces and one for coupler damping effects. The penalty for braking forces is employed to reduce energy waste incurred by braking. The penalty for coupler damping is introduced to alleviate the cyclic vibration of couplers, which link adjacent cars in the train. The damping penalty is also expected to reduce energy wasted by coupler damping and corresponding maintenance/replacement cost of the dampers. In addition, the weight of the velocity tracking term in the objective function is modified to vary dynamically according to the train's velocity to improve the train's overall performance. Simulations verify the effectiveness of the proposed control approach. Discussions over the impacts of the two penalty factors and dynamic weight method are provided together with some suggestions on their applications.

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Robust nonlinear model predictive control for automatic train operation based on constraint tightening strategy

Jia

Wang

2020

Asian Journal of Control

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This paper studies the problem of automatic train operation (ATO) robust nonlinear model predictive control under considering multiple objectives and constraints. After establishing a nonlinear multipoint model with uncertain bounded disturbance, a robust nonlinear model predictive control algorithm to meet the punctuality of train operation and energy consumption for ATO is proposed based on constraint tightening strategy.Moreover, theoretical analysis of the feasibility and stability for the speed loop system are presented. Then, with the objective of reference electromagnetic torque tracking and low switching frequency, a model predictive direct torque control algorithm with one-step delay compensation is proposed.Feasibility of the proposed algorithm is ensured by using deadlock prediction method, and convergence analysis of the torque loop is given simultaneously. Lastly, the effectiveness of these two algorithms are verified by numerical simulation.

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Optimal Operation of Heavy-Haul Trains Equipped With Electronically Controlled Pneumatic Brake Systems Using Model Predictive Control Methodology

Cited by 95 publications

References 18 publications

Model predictive control for improving operational efficiency of overhead cranes

Model predictive control for improving operational efficiency of overhead cranes

Development of an Optimal Operation Approach in the MPC Framework for Heavy-Haul Trains

Robust nonlinear model predictive control for automatic train operation based on constraint tightening strategy

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