Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements |
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| Institution: | 1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;2. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China;3. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, PR China;2. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, PR China |
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| Abstract: | The cooperative energy-efficient trajectory planning for multiple high-speed train movements is considered in this paper. We model all the high-speed trains as the agents that can communicate with others and propose a local trajectory planning control model using the Model Predictive Control (MPC) theory. After that we design an online distributed cooperative optimization algorithm for multiple train trajectories planning, under which each train agent can regulate the trajectory planning procedure to save energy using redundancy trip time through tuning ACO’s heuristic information parameter. Compared to the existing literature, the vital distinctions of our work lies not only on the online cooperative trajectory planning but also on the distributed mechanism for multiple high-speed trains. Experimental studies are given to illustrate the effectiveness of the proposed methods with the practical operational data of Wuhan-Guangzhou High-speed Railway in China. |
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| Keywords: | High-speed train Energy-saving Trajectory planning Model predictive control Ant colony optimization |
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