Intelligent driving methods based on expert knowledge and online optimization for high-speed trains

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.