Speed Tracking Based Energy-Efficient Freight Train Control Through Multi-Algorithms Combination

Yang, Jinyue; Jia, Limin; Fu, Yunxiao; Lu, Shu-Shen

doi:10.1109/mits.2017.2666580

Cited by 29 publications

(10 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this article, we employed the four strategies in (8) to form a strategy pool α = {α 1 , α 2 , α 3 , α 4 }, and we employed an adaptive operator selection method to decided which operator could be used at the next generation [21]. This method contains two parts.…”

Section: Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Train operation optimization with adaptive differential evolution algorithm based on decomposition

Liu

Zhu

et al. 2019

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

The train operation is a multi‐objective optimization process including several operating indexes, such as safety, punctuality, precision stop, comfort, and low‐energy consumption. The speed profile and timetable for trains should be adjusted correspondingly to satisfy the passengers' demands while minimizing energy consumption. To solve this problem, we propose a multi‐objective optimization approach for train operation. First, we develop a single‐particle model of the train and a multi‐objective optimization model for the train operation, which is subject to the constraints such as safety requirement, passenger comfort, and low energy consumption. Second, to obtain the Pareto frontier of train operation, a uniform design multi‐objective adaptive differential evolution algorithm based on decomposition (UMADE/D) is studied and applied to solve the multi‐objective optimization model for the train operation. Finally, we present numerical examples based on the real‐life operation data from the Nanjing Metro Line 1 in Nanjing, China. Three searching methods, namely, the proposed UMADE/D, Non‐Dominated Sorting Genetic Algorithm, and Multi‐Objective Evolutionary Algorithm based on Decomposition, are implemented to find the Pareto frontier of train operation quickly and efficiently. The simulation results show the efficiency of the proposed UMADE/D algorithm, which can provide corresponding operation strategies and satisfy the multi‐objective demands when the train is in different operation state. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

show abstract

Section: Selectionmentioning

confidence: 99%

“…Ref. carried out the controller design for freight train speed tracking based on the target speed profile. The classical proportion integration differentiation (PID) controller, fuzzy controller, multimodal fuzzy PID controller, and the integrated controller are used to simulate and make comparison of train speed tracking.…”

Section: Introductionmentioning

confidence: 99%

Train operation optimization with adaptive differential evolution algorithm based on decomposition

Liu

Zhu

et al. 2019

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…To overcome the disadvantage of not being able to adjust the parameters of the PID control algorithm in real-time, [12] and [13] proposed a train speed-tracking control algorithm that combines PID and a fuzzy control algorithm, which can adjust the parameters of PID controller in real-time according to external disturbances. To improve the robustness of the fuzzy PID control algorithm, Yang, Jia et al [14] proposed a multimodal fuzzy PID control algorithm. With the development of intelligent control algorithms, a series of intelligent control algorithms has been proposed [15]- [23].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Traction Control Method Based on Model Predictive Fuzzy PID Control and Online Optimization for Permanent Magnetic Maglev Trains

2021

View full text Add to dashboard Cite

Considering that the speed control system of the suspended permanent magnetic maglev train is more complicated and the parameters are more unstable than those of other trains, the traditional speedtracking algorithm has large tracking errors, frequent controller output changes, high energy consumption, and decreasing the passengers' riding comfort. To improve the shortcomings of the traditional automatic train operation (ATO) control algorithm, this paper proposes a predictive fuzzy proportional-integralderivative control algorithm with weights (WM-F-PID). The main contribution of this work is to propose a cascaded predictive fuzzy PID (F-PID) control algorithm architecture with weights and use an improved steepest descent method to calculate online the weight of the F-PID controller input occupied by the predictive controller output. Compared with the proportional-integral-derivative (PID), F-PID, model predictive control (MPC), and simple cascade predictive fuzzy PID (M-F-PID) control algorithms, this control algorithm effectively improves train tracking accuracy and comfort and reduces train energy consumption and stopping errors.INDEX TERMS Suspended permanent magnetic maglev train, WM-F-PID control algorithm, Online optimization algorithm, Speed-tracking.

show abstract

“…Yang et al [38] proposed a controller based on the combination of neural network and fuzzy logic, the control effect of this method was superior to the single controller that validated by the computer simulation. Yang et al [39] separated different train running commands under different situations and defined them as control modes, and various control algorithms were introduced to different modes to optimize the speed tracking effect.…”

Section: Introductionmentioning

confidence: 99%

Integrated Optimal Design of Speed Profile and Fuzzy PID Controller for Train With Multifactor Consideration

Zhu

Liu³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Speed profile design and tracking control are two important issues to ensure the performance of the automatic train operation system. Aiming to realize the maximum optimization probability of train running, this paper constructs an integrated optimal method through speed profile design and fuzzy PID controller design. In the recommended speed profile design, three typical running strategies are included in the proposed comprehensive scheme to extend solution space, and the constructed train performance simulation is combined with NSGA-II algorithm to solve such multiobjective problem. As for recommended speed profile tracking, in order to improve the tracking performance of the train, which is a nonlinear system, a fuzzy PID controller is designed to adaptively adjust PID gains. Note that multiple indicators of punctually, energy consumption, stopping accuracy and comfort are all considered in the integrated design. Taking Beijing Subway Line 8 as the numerical example, the results show that the design of speed profile and fuzzy PID controller are both effective, and energy consumption saves about 10.40% with other better indicators compared with the original data.

show abstract

Speed Tracking Based Energy-Efficient Freight Train Control Through Multi-Algorithms Combination

Cited by 29 publications

References 17 publications

Train operation optimization with adaptive differential evolution algorithm based on decomposition

Train operation optimization with adaptive differential evolution algorithm based on decomposition

Intelligent Traction Control Method Based on Model Predictive Fuzzy PID Control and Online Optimization for Permanent Magnetic Maglev Trains

Integrated Optimal Design of Speed Profile and Fuzzy PID Controller for Train With Multifactor Consideration

Contact Info

Product

Resources

About