Abstract
The work focus on the position and velocity tracking control problem of high speed train with uncertain system model and external disturbance as well as unknown traction/braking actuation characteristics. Neuroadaptive Proportion-integral-derivative (PID)-like fault-tolerant control algorithms are developed to achieve uniformly ultimately bounded (UUB) stable position/velocity tracking control of high speed train by using a well defined smooth function. Unlike the traditional PID control, the resultant control scheme is of PID structure and able to deal with unknown system parameters and nonlinearities and actuator failures without the need for any “trial and error” process to determine the PID gains. The effectiveness of the proposed control strategy is confirmed by theoretical analysis and numerical simulations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Romero, J.G., Ortega, R., Donaire, A.: Energy shaping of mechanical systems via PID control and extension to constant speed tracking. IEEE Trans. Autom. Control 61, 3551–3556 (2016)
Moradi, M., Fekih, A.: Adaptive PID-sliding-mode fault-tolerant control approach for vehicle suspension systems subject to actuator faults. IEEE Trans. Veh. Technol. 63, 1041–1054 (2014)
Chen, X.Q., Ma, Y.J., Hou, T., Cai, R.C.: Study on the speed control of high-speed train based on predictive fuzzy PID control. J. Syst. Simul. 26, 191–196 (2014)
Wang, X.Q., Song, J.C., Wushi, H.Q.: A study of fuzzy PID control algorithm for high speed train’s eiectric hydraulic braking system. Mach. Des. Manuf. 9, 22–24 (2013)
Gao, B., Dong, H.R., Zhang, Y.X.: Speed adjustment braking of automatic train operation system based on fuzzy-PID switching control. In: 6th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 577–580. IEEE Press (2009)
Yang, H., Zhang, K.P., Liu, H.E.: Online regulation of high speed train trajectory control based on T-S fuzzy bilinear model. IEEE Trans. Intell. Transp. Syst. 17, 1496–1508 (2016)
Wang, T., Gao, H.J., Qiu, J.B.: A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control. IEEE Trans. Neural Netw. Learn. Syst. 27, 416–425 (2016)
Wai, R.J., Yao, J.X., Lee, J.D.: Backstepping fuzzy-neural-network control design for hybrid maglev transportation system. IEEE Trans. Neural Netw. Learn. Syst. 26, 302–317 (2015)
Song, Q., Song, Y.D.: Data-based fault-tolerant control of high speed trains with traction/braking notch nonlinearities and actuator failures. IEEE Trans. Neural Netw. 22, 2250–2261 (2011)
Schaefer, H.H.: The comparison of formulae on train resistance and locomotive adhesion coeffcient in various countries. Foreign Diesel Locomot. 2, 35–43 (1989)
Wang, Y.J., Song, Y.D., Krstic, M., Wen, C.Y.: Fault-tolerant finite time consensus for multiple uncertain nonlinear mechanical ssystems under single-way directed communication interactions and actuation failures. Automatica 63, 374–383 (2016)
Liu, X.Y., Song, Y.D., Song, Q.: Fault-tolerant control of dynamic systems with unknown control direction-input nonlinearities-actuator failures. In: Decision and Control and European Control Conference, pp. 4973–4978. IEEE Press, Orlando (2011)
Huang, W.Y., Sun, Z.Y.: Noticeable problems of braking calculation for high speed train. Railw. Locomot. Car 26, 24–27 (2006)
Slotine, J.J., Li, W.: Applied Nonlinear Control. Prentice-Hall, Upper Saddle River (1991)
Acknowledgments
This work is supported by National Natural Science Foundation (NNSF) of China under Grant 61503021, the Talent Fund (No. 2015RC048) and the State Key Laboratory Program (No. RCS2015ZT003).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Song, Q., Sun, T. (2017). Neuroadaptive PID-like Fault-Tolerant Control of High Speed Trains with Uncertain Model and Unknown Tracking/Braking Actuation Characteristics. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-59081-3_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59080-6
Online ISBN: 978-3-319-59081-3
eBook Packages: Computer ScienceComputer Science (R0)