Intelligent driving methods based on expert knowledge and online optimization for high-speed trains. (30th November 2017)

• Record Type: Journal Article
• Authors: Cheng, Ruijun; Chen, Dewang; Cheng, Bao; Zheng, Song
• Is Part Of: Expert systems with applications. Volume 87(2017)
• Page Start: 228
• Page End: 239
• Publication Date: 2017-11-30
• Subjects: High-speed train -- Expert knowledge -- Online optimization algorithm -- Intelligent driving method (IDM); Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.06.006
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Intelligent driving methods (IDMs) are proposed to control the high-speed train. IDMs is realized by integrating the expert knowledge with online algorithms. Online optimization algorithms can greatly enhance IDMs' real-time performance. The proposed IDMs are better than traditional train driving method. Abstract: In the engineering control practice of High-Speed Train (HST), the traditional automatic driving method increases the energy consumption and impairs the intelligence of train operation. Different from previous studies, we propose the intelligent driving methods (IDMs), including expert knowledge system and online optimization algorithms, to achieve the multi-objective (safety, punctuality, energy efficient, passengers' riding comfort, and so on) control of HST. First, we establish the expert knowledge system based on the driving data and control rules of excellent drivers. Then, in order to enhance the adaptability and real-time performance of proposed IDMs, two online optimization algorithms, including exact online programming driving (EOPD) and inexact online programming driving (IOPD), are developed by improved gradient descent and stochastic meta-decent method to update the controller's output online. Finally, using the field data collected from Beijing-Shanghai High-Speed Railway, the proposed IDMs are verified under the real speed-limit conditions. The simulation results show that EOPD and IOPD can achieve better performances than automatic driving method based on ATO, Fuzzy PID controller and traditional multi-objective optimization method, especially in passengers' riding comfort and energy-consumption. Furthermore, as the step size is selected with wide randomness in the updating process, IOPD has more operating mode switching times than EOPD but its punctuality is better.