Moving Horizon Optimization of Dynamic Trajectory Planning for High-Speed Train Operation

Xiao, Yan; Cai, Baigen; Ning, Bin; Shangguan, Wei

doi:10.1109/tits.2015.2499254

Cited by 50 publications

(11 citation statements)

References 39 publications

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“…In order to acquire better performance in the presence of disturbances, scholars have been paying more attention to the online optimization problem of the driving strategy. Considering the uncertain disturbances on resistance coefficients and the possible delay time, Yan et al [19] proposed a moving horizon train optimization approach for dynamic train trajectory planning problem, which can be solved by immune differential evolution algorithm. Yan et al [20] presented a cooperative energy-efficient trajectory planning scheme for multiple high-speed train movements using distributed model predictive control, under which each train can get the optimal speed trajectory online.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this section, we present an algorithm to solve the optimization problem (19). Concerning the switching time optimization problem, a number of algorithms have been developed in [27,30].…”

Section: Solution Algorithm Developmentmentioning

confidence: 99%

“…Concerning the switching time optimization problem, a number of algorithms have been developed in [27,30]. However, these algorithms cannot be directly applied to solving the optimization problem (19) due to the existence of a terminal state constraint.…”

Section: Solution Algorithm Developmentmentioning

confidence: 99%

“…A switched-mode dynamical system is introduced to describe the dynamics of a metro train. By using the switching times as the optimization variables, the defect of high dimension of the optimization variable in [3,[19][20][21][22]28] can be avoided such that the computation cost can be reduced.…”

mentioning

confidence: 99%

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An Online Energy-Saving Driving Strategy for Metro Train Operation Based on the Model Predictive Control of Switched-Mode Dynamical Systems

2020

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With the rapid development of urban rail transit systems and the consequent sharp increase of energy consumption, the energy-saving train operation problem has been attracting much attention. Extensive studies have been devoted to optimal control of a single metro train in an inter-station run to minimize the energy consumption. However, most of the existing work focuses on offline optimization of the energy-saving driving strategy, which still needs to be tracked in real train operation. In order to attain better performance in the presence of disturbances, this paper studies the online optimization problem of the energy-saving driving strategy for a single metro train, by employing the model predictive control (MPC) approach. Firstly, a switched-mode dynamical system model is introduced to describe the dynamics of a metro train. Based on this model, an MPC-based online optimization problem is formulated for obtaining the optimal mode switching times with minimal energy consumption for a single train in an inter-station run. Then we propose an algorithm to solve the constrained optimization problem at each time step by utilizing the exterior point penalty function method. The proposed online optimal train control algorithm which determines the mode switching times can not only improve the computational efficiency but also enhances the robustness to disturbances in real scenarios. Finally, the effectiveness and advantages of this online optimal train control algorithm are illustrated through case studies of a single train in an inter-station run.

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Section: Literature Reviewmentioning

confidence: 99%

“…In this section, we present an algorithm to solve the optimization problem (19). Concerning the switching time optimization problem, a number of algorithms have been developed in [27,30].…”

Section: Solution Algorithm Developmentmentioning

confidence: 99%

Section: Solution Algorithm Developmentmentioning

confidence: 99%

mentioning

confidence: 99%

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An Online Energy-Saving Driving Strategy for Metro Train Operation Based on the Model Predictive Control of Switched-Mode Dynamical Systems

2020

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“…Although static multi-objective optimization evolutionary algorithms (MOEAs) [5], [6] have the superior capability on addressing the static multi-objective optimization problems, it still is a challenging task as static MOEAs lack the capability of dynamic optimization [7]- [9] on dealing with DMOPs. Nevertheless, there has been an increasing amount of research interest in the field of dynamic multi-objective optimization (DMO) for better solving DMOPs since many real-world applications, like dynamic scheduling [10]- [13], path planning [14]- [16], resource allocation [17], [18], and machine learning [19]. Moreover, the effective EAs must be created to attain the goals [20]…”

Section: Introductionmentioning

confidence: 99%

A Novel Prediction Strategy Based on Change Degree of Decision Variables for Dynamic Multi-Objective Optimization

Xing

Liu

et al. 2020

IEEE Access

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Effectively balancing the convergence and diversity in dynamic environments is a challenging task. In order to handle the issue, this paper proposes a novel prediction strategy based on change degree of decision variables for dynamic multi-objective optimization (CDDV), which has the ability to detect the change degree in the decision space and design the different prediction strategy to make the population adapt to the new environment. The proposed method can adaptively increase population diversity according to the analysis of change degree, when an environmental change is detected. In order to study the efficacy and usefulness of the novel change degree on evolutionary algorithms, a range of dynamic multi-objective benchmark problems are selected to evaluate the performance of the proposed algorithm. The results demonstrate the effectiveness of proposed algorithm in compared with four other state-of-the-art methods. INDEX TERMS Dynamic multi-objective optimization, evolutionary algorithms, change degree, diversity.

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Dynamic Multi-objective Optimization via Sliding Time Window and Parallel Computing

Fan

Wang

Ersoy

et al. 2021

Lecture Notes in Computer Science

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Moving Horizon Optimization of Dynamic Trajectory Planning for High-Speed Train Operation

Cited by 50 publications

References 39 publications

An Online Energy-Saving Driving Strategy for Metro Train Operation Based on the Model Predictive Control of Switched-Mode Dynamical Systems

An Online Energy-Saving Driving Strategy for Metro Train Operation Based on the Model Predictive Control of Switched-Mode Dynamical Systems

A Novel Prediction Strategy Based on Change Degree of Decision Variables for Dynamic Multi-Objective Optimization

Dynamic Multi-objective Optimization via Sliding Time Window and Parallel Computing

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