Stability enhancement of a high-speed train bogie using active mass inertial actuators

ABSTRACT

In this study, a method regarding frame lateral vibration control based on the state feedback of an additional oscillator is proposed, so as to improve the bogie hunting stability. The multi-objective optimisation method (MOOP), with two objective functions of the stability index and control effort, is solved by the NSGA-II algorithm to obtain the feedback gains. The frame lateral vibration control can effectively improve the bogie hunting stability according to the linear and non-linear analysis of a high-speed train bogie, in which a fault of the yaw damper and time delay in the control system are considered. The effect of the oscillator suspension parameters and time delay on the system stability and robustness are analysed. The results show that the damped vibration frequency of the oscillator should be equal to the bogie hunting frequency, but a harder oscillator suspension can be used to improve the hunting critical speed margin of the bogie control system. However, just as how the feeding the frame states back directly, a hard oscillator suspension will lead to instability in the control system at a certain time delay. Therefore, the improvement of bogie hunting stability and reduction of control system stability must be considered when optimising the oscillator parameters. For the 350 km/h train bogie covered in this study, the optimal mass, natural frequency and damping ratio of the additional oscillator are acquired.

KEYWORDS:

• High-speed train bogie
• hunting stability
• active control
• time delay
• multi-objective optimisation
• finite dimensional state space approximation

Acknowledgments

The first author would like to thank the China Scholarship Council (CSC) for sponsoring his study in the USA.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The material in this paper is based on work supported by grants [51675443, 51735012, 11172197 and 11332008] from the National Natural Science Foundation of China, Sichuan Science and Technology Program [grant 2018JY0209], Research Innovation Team grant [17TD0040] of Sichuan Provincial University, and Traction Power State Key Laboratory [grant no. 2018TPL_T05, no. 2017TPL_T02] of the Independent Research and Development Projects.