A Safe and Reliable Anti-Lock Wheel Control with Enhanced Forgotten Factor for Brake Operation of Heavy Train

With the development of science and technologies, the locomotive is getting faster and faster and the load becomes heavier. The inertia and vertical force generated by the heavy train are increasing, and there are higher requirements of the braking and anti-slip control of the heavy train. In order to address the issues of safe and reliable anti-lock wheel control for heavy train operation, a sliding mode control based on recursive least square algorithm with enhanced forgotten factor is proposed. In this algorithm, the recursive least square method is exerted to seek optimal slip ratio. The wheel pair model is introduced to construct PI closed-loop observer to estimate unmeasured adhesive torque. On the one hand, the value of adhesion force torque can be used to calculate the actual adhesion coefficient which is an input of the algorithm for calculating the optimal slip rate; on the other hand, it can be also used to calculate the reference speed which is a necessary parameter of controller. The simulation result demonstrates the effect of real-time adjustment for braking torque, which guarantees the braking performance.

[1]  K. Ohishi,et al.  Advanced anti-slip and anti-skid re-adhesion control considering air brake for electric train , 2005, 2005 European Conference on Power Electronics and Applications.

[2]  Liu Baina.  Design of Sliding Mode Variable Structure Controller for Anti-Lock Braking System , 2015 .

[3]  Jacob Benesty,et al.  A Robust Variable Forgetting Factor Recursive Least-Squares Algorithm for System Identification , 2008, IEEE Signal Processing Letters.

[4]  K. Ohishi,et al.  Anti-skid re-adhesion control using tangential force estimator based on disturbance observer for electric commuter train , 2004, Proceedings of the 2004 IEEE International Conference on Control Applications, 2004..

[5]  Branko D. Kovacevic,et al.  Robust adaptive filtering using recursive weighted least squares with combined scale and variable forgetting factors , 2016, EURASIP Journal on Advances in Signal Processing.

[6]  Minhao Zhu,et al.  The effect of alumina particle on improving adhesion and wear damage of wheel/rail under wet conditions , 2016 .

[7]  Li Zhong.  Analysis and Simulation of Automotive ABS Fuzzy Control Methods , 2003 .

[8]  Lei Zhang,et al.  Anti-Lock Wheel Slip Control With an Adaptive Searching Algorithm for the Optimal Slip , 2015 .

[9]  Jong Shik Kim,et al.  Reference slip ratio generation and adaptive sliding mode control for railway rolling stocks , 2009 .

[10]  Qiang Fu,et al.  Study on anti-slip regulation of quarter automobile based on PID control , 2012, 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet).

[11]  Yi-Wen Liao,et al.  Robust model predictive control with discrete-time integral sliding surface , 2015, 2015 American Control Conference (ACC).

[12]  Yongduan Song,et al.  A Novel Approach for Active Adhesion Control of High-Speed Trains Under Antiskid Constraints , 2015, IEEE Transactions on Intelligent Transportation Systems.