Advanced Transportation Equipment

An Adaptive Nonsingular Fast Terminal Sliding Mode Control for Yaw Stability Control of Bus Based on STI Tire Model

  • Xiaoqiang Sun ,
  • Yujun Wang ,
  • Yingfeng Cai ,
  • Pak Kin Wong ,
  • Long Chen
展开
  • 1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, 212013, China;
    2. Department of Electromechanical Engineering, University of Macau, Taipa, Macau, China;
    3. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China

收稿日期: 2020-06-18

  修回日期: 2021-01-31

  网络出版日期: 2021-12-21

基金资助

Supported by National Natural Science Foundation of China (Grant Nos. 52072161, U20A20331), China Postdoctoral Science Foundation (Grant No. 2019T120398), State Key Laboratory of Automotive Safety and Energy of China (Grant No. KF2016), Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province (Grant No. QCCK2019-002), and Young Elite Scientists Sponsorship Program by CAST (Grant No. 2018QNRC 001).

An Adaptive Nonsingular Fast Terminal Sliding Mode Control for Yaw Stability Control of Bus Based on STI Tire Model

  • Xiaoqiang Sun ,
  • Yujun Wang ,
  • Yingfeng Cai ,
  • Pak Kin Wong ,
  • Long Chen
Expand
  • 1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, 212013, China;
    2. Department of Electromechanical Engineering, University of Macau, Taipa, Macau, China;
    3. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China

Received date: 2020-06-18

  Revised date: 2021-01-31

  Online published: 2021-12-21

Supported by

Supported by National Natural Science Foundation of China (Grant Nos. 52072161, U20A20331), China Postdoctoral Science Foundation (Grant No. 2019T120398), State Key Laboratory of Automotive Safety and Energy of China (Grant No. KF2016), Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province (Grant No. QCCK2019-002), and Young Elite Scientists Sponsorship Program by CAST (Grant No. 2018QNRC 001).

摘要

Due to the bus characteristics of large quality, high center of gravity and narrow wheelbase, the research of its yaw stability control (YSC) system has become the focus in the field of vehicle system dynamics. However, the tire nonlinear mechanical properties and the effectiveness of the YSC control system are not considered carefully in the current research. In this paper, a novel adaptive nonsingular fast terminal sliding mode (ANFTSM) control scheme for YSC is proposed to improve the bus curve driving stability and safety on slippery roads. Firstly, the STI (Systems Technologies Inc.) tire model, which can effectively reflect the nonlinear coupling relationship between the tire longitudinal force and lateral force, is established based on experimental data and firstly adopted in the bus YSC system design. On this basis, a more accurate bus lateral dynamics model is built and a novel YSC strategy based on ANFTSM, which has the merits of fast transient response, finite time convergence and high robustness against uncertainties and external disturbances, is designed. Thirdly, to solve the optimal allocation problem of the tire forces, whose objective is to achieve the desired direct yaw moment through the effective distribution of the brake force of each tire, the robust least-squares allocation method is adopted. To verify the feasibility, effectiveness and practicality of the proposed bus YSC approach, the TruckSim-Simulink co-simulation results are finally provided. The co-simulation results show that the lateral stability of bus under special driving conditions has been significantly improved. This research proposes a more effective design method for bus YSC system based on a more accurate tire model.

本文引用格式

Xiaoqiang Sun , Yujun Wang , Yingfeng Cai , Pak Kin Wong , Long Chen . An Adaptive Nonsingular Fast Terminal Sliding Mode Control for Yaw Stability Control of Bus Based on STI Tire Model[J]. Chinese Journal of Mechanical Engineering, 2021 , 34(4) : 79 -79 . DOI: 10.1186/s10033-021-00600-4

Abstract

Due to the bus characteristics of large quality, high center of gravity and narrow wheelbase, the research of its yaw stability control (YSC) system has become the focus in the field of vehicle system dynamics. However, the tire nonlinear mechanical properties and the effectiveness of the YSC control system are not considered carefully in the current research. In this paper, a novel adaptive nonsingular fast terminal sliding mode (ANFTSM) control scheme for YSC is proposed to improve the bus curve driving stability and safety on slippery roads. Firstly, the STI (Systems Technologies Inc.) tire model, which can effectively reflect the nonlinear coupling relationship between the tire longitudinal force and lateral force, is established based on experimental data and firstly adopted in the bus YSC system design. On this basis, a more accurate bus lateral dynamics model is built and a novel YSC strategy based on ANFTSM, which has the merits of fast transient response, finite time convergence and high robustness against uncertainties and external disturbances, is designed. Thirdly, to solve the optimal allocation problem of the tire forces, whose objective is to achieve the desired direct yaw moment through the effective distribution of the brake force of each tire, the robust least-squares allocation method is adopted. To verify the feasibility, effectiveness and practicality of the proposed bus YSC approach, the TruckSim-Simulink co-simulation results are finally provided. The co-simulation results show that the lateral stability of bus under special driving conditions has been significantly improved. This research proposes a more effective design method for bus YSC system based on a more accurate tire model.

参考文献

[1] S Tian, L Wei, C Schwarz, et al. An earlier predictive rollover index designed for bus rollover detection and prevention. Journal of Advanced Transportation, 2018: 2713868.
[2] M Ghazali, M Durali, H Salarieh. Vehicle trajectory challenge in predictive active steering rollover prevention. International Journal of Automotive Technology, 2017, 18(3): 511-521.
[3] H Termous, H Shraim, R Talj, et al. Coordinated control strategies for active steering, differential braking and active suspension for vehicle stability, handling and safety improvement. Vehicle System Dynamics, 2019, 57(11): 1494-1529.
[4] X Xu, J Mi, F Wang, et al. Design of differential braking control system of travel trailer based on multi-objective PID. Journal of Jiangsu University (Natural Science Edition), 2020, 41(2): 172-180. (in Chinese)
[5] J T Bai, G W Meng, W J Zuo. Rollover crashworthiness analysis and optimization of bus frame for conceptual design. Journal of Mechanical Science and Technology, 2019, 33(7): 3363-3373.
[6] T Chen, X Xu, L Chen, et al. Estimation of longitudinal force, lateral vehicle speed and yaw rate for four-wheel independent driven electric vehicles. Mechanical Systems and Signal Processing, 2018, 101: 377-388.
[7] T Chen, L Chen, X Xu, et al. Reliable sideslip angle estimation of four-wheel independent drive electric vehicle by information iteration and fusion. Mathematical Problems in Engineering, 2018, 2018: 9075372.
[8] Y Z Ma, Z F Zhang, Z Y Niu, et al. Design and verification of integrated control strategy for tractor-semitrailer AFS/DYC. Journal of Jiangsu University (Natural Science Edition), 2018, 39(5): 530-536. (in Chinese)
[9] H Z Zhang, J S Liang, H B Jiang, et al. Stability research of distributed drive electric vehicle by adaptive direct yaw moment control. IEEE Access, 2019, 7: 106225-106237.
[10] L D Novellis, A Sorniotti, P Gruber, et al. Direct yaw moment control actuated through electric drivetrains and friction brakes: Theoretical design and experimental assessment. Mechatronics, 2015, 26: 1-15.
[11] Y H Chen, J K Hedrick, K H Guo. A novel direct yaw moment controller for in-wheel motor electric vehicles. Vehicle System Dynamics, 2013, 51(6): 925-942.
[12] A Goodarzi, F Diba, E Esmailzadeh. Innovative active vehicle safety using integrated stabilizer pendulum and direct yaw moment control. Journal of Dynamic Systems, Measurement, and Control, 2014, 136(5): 051026.
[13] S H Ding, J L Sun, Direct yaw-moment control for 4WID electric vehicle via finite-time control technique. Nonlinear Dynamics, 2017, 88(1): 239-254.
[14] S H Ding, L Liu, W X Zheng. Sliding mode direct yaw-moment control design for in-wheel electric vehicles. IEEE Transactions on Industrial Electronics, 2017, 64(8): 6752-6762.
[15] W Huang, P K Wong, K I Wong, et al. Adaptive neural control of vehicle yaw stability with active front steering using an improved random projection neural network. Vehicle System Dynamics, 2019, DOI: https://doi.org/10.1080/00423114.2019.1690152.
[16] X Q Sun, W W Hu, Y F Cai, et al. Identification of a piecewise affine model for the tire cornering characteristics based on experimental data. Nonlinear Dynamics, 2020, 101(2): 857-874.
[17] X Q Sun, Y F Cai, S H Wang, et al. Piecewise affine identification of tire longitudinal properties for autonomous is driving control based on data-driven. IEEE Access, 2018, 6: 47424-47432.
[18] Y Shi, Q W Liu, F Yu. Design of an adaptive FO-PID controller for an in-wheel-motor driven electric vehicle. SAE International Journal of Commercial Vehicles, 2017, 10: 265-274.
[19] H Y Guo, F Liu, F Xu, et al. Nonlinear model predictive lateral stability control of active chassis for intelligent vehicles and its FPGA implementation. IEEE Transactions on Systems Man Cybernetics-Systems, 2019, 49(1): 2-13.
[20] Q H Meng, T T Zhao, C J Qian, et al. Integrated stability control of AFS and DYC for electric vehicle based on non-smooth control. International Journal of Systems Science, 2018, 49(7): 1518-1528.
[21] J Song. Development and comparison of integrated dynamics control systems with fuzzy logic control and sliding mode control. Journal of Mechanical Science and Technology, 2013, 27(6): 1853-1861.
[22] J C Wang, R He. Hydraulic anti-lock braking control strategy of a vehicle based on a modified optimal sliding mode control method, P. I. Mech. Eng. D-Journal of Automobile Engineering, 2019, 233(12): 3185-3198.
[23] X Q Sun, Y F Cai, C C Yuan, et al. Fuzzy sliding mode control for the vehicle height and leveling adjustment system of an electronic air suspension. Chinese Journal of Mechanical Engineering, 2018, 31(1): 25.
[24] S A Chen, J C Wang, M Yao, et al. Improved optimal sliding mode control for a non-linear vehicle active suspension system. Journal of Sound and Vibration, 2017, 395: 1-25.
[25] Z B Yang, D Zhang, X D Sun, et al. Nonsingular fast terminal sliding mode control for a bearingless induction motor. IEEE Access, 2017, 5: 16656-16664.
[26] E Mousavinejad, Q L Han, F W Yang, et al. Integrated control of ground vehicles dynamics via advanced terminal sliding mode control. Vehicle System Dynamics, 2017, 55(2): 268-294.
[27] A N Asiabar, R Kazemi. A direct yaw moment controller for a four in-wheel motor drive electric vehicle using adaptive sliding mode control. P. I. Mech. Eng. K-Journal of Multi-body Dynamics, 2019, 233(3): 549-567.
[28] J X Zhang, J Li. Integrated vehicle chassis control for active front steering and direct yaw moment control based on hierarchical structure. Transactions of the Institute of Measurement and Control, 2019, 41(9): 2428-2440.
[29] Y Shi, F Yu. Hierarchical direct yaw-moment control system design for in-wheel motor driven electric vehicle. International Journal of Automotive Technology, 2018, 19(4): 695-703.
[30] J R Wagner, J F Keane. A strategy to verify chassis controller software-dynamics, hardware, and automation. IEEE Transactions on Systems Man & Cybernetics, 1997, 27(4): 480-493.
[31] M Reiter, J Wagner. Automated automotive tire inflation system–effect of tire pressure on vehicle handling. IFAC. Proceedings, 2010, 43(7): 638-643.
[32] K Y Pan, Y J Lu. Analysis on vehicle dynamic simulating STI tire model used in driving simulator. Auto Engineer., 2009, 2: 28-30. (in Chinese)
[33] Q Xia, L Chen, X Xu, et al. Running states estimation of autonomous four-wheel independent drive electric vehicle by virtual longitudinal force sensors. Mathematical Problems in Engineering, 2019, 2019: 8302943.
[34] J Tian, J Tong, S Luo. Differential steering control of four-wheel independent-drive electric vehicles. Energies, 2018, 11(11): 2892.
[35] T Liu, X L Tang, H Wang, et al. Adaptive hierarchical energy management design for a plug-in hybrid electric vehicle. IEEE Transactions on Vehicular Technology, 2019, 68(12): 11513-11522.
[36] L Chen, T Chen, X Xu, et al. Sideslip angle estimation of in-wheel motor drive electric vehicles by cascaded multi-Kalman filters and modified tire model. Metrology and Measurement Systems, 2019, 26(1): 185-208.
[37] T Chen, X Xu, Y Li, et al. Speed-dependent coordinated control of differential and assisted steering for in-wheel motor driven electric vehicles. P. I. Mech. Eng. D-Journal of Automobile Engineering, 2018, 232(9): 1206-1220.
[38] L Chen, T Chen, X Xu, et al. Multi-objective coordination control strategy of distributed drive electric vehicle by orientated tire force distribution method. IEEE Access, 2018, 6: 69559-69574.
[39] X Zhang, H He, J Nie, et al. Performance analysis of semi-active suspension with skyhook-inertance control. Journal of Jiangsu University (Natural Science Edition), 2018, 39(5): 497-502. (in Chinese)
[40] Y C Qin, X L Tang, T Jia, et al. Noise and vibration suppression in hybrid electric vehicles: state of the art and challenges. Renewable and Sustainable Energy Reviews, 2020, 124: 109782.
[41] S B Jiang, P K Wong, R C Guan, et al. An efficient fault diagnostic method for three-phase induction motors based on incremental broad learning and non-negative matrix factorization. IEEE Access, 2019, 7: 17780-17790.
[42] H Ye, G Z Li, S H Ding, et al. Direct yaw moment control of electric vehicle based on non-smooth control technique. Journal of Jiangsu University (Natural Science Edition), 2018, 39(6): 640-646. (in Chinese)
[43] J Wang, S Chen, T Jing, et al. Remote irrigation control system for soilless culture. Journal of Drainage and Irrigation Machinery Engineering, 2020, 38(9): 959-965. (in Chinese)
[44] S H Ding, L Liu, J H Park. A novel adaptive nonsingular terminal sliding mode controller design and its application to active front steering system. International Journal of Robust and Nonlinear Control, 2019, 29(12): 4250-4269.
[45] S H Ding, W X Zheng. Nonsingular terminal sliding mode control of nonlinear second-order systems with input saturation. International Journal of Robust and Nonlinear Control, 2016, 26(9): 1857-1872.
[46] H B Jiang, F G Cao, W W Zhu. Control method of intelligent vehicles cluster motion based on SMC. Journal of Jiangsu University (Natural Science Edition), 2018, 39(4): 385-390. (in Chinese)
[47] G Q Geng, B Y Wei, C Duan, et al. A strong robust observer of distributed drive electric vehicle states based on strong tracking-iterative central difference Kalman filter algorithm. Advances in Mechanical Engineering, 2018, 10(6): 1687814018779682.
[48] J Liu, L Gao, J J Zhang, et al. Super-twisting algorithm second-order sliding mode control for collision avoidance system based on active front steering and direct yaw moment control. P. I. Mech. Eng. D- Journal of Automobile Engineering, 2020: 0954407020948298.
[49] P Sathishkumar, R C Wang, L Yang, et al. Trajectory control for tire burst vehicle using the standalone and roll interconnected active suspensions with safety-comfort control strategy. Mechanical Systems and Signal Processing, 2020, 142: 106776.
[50] B Xu, G D Shi, W Ji, et al. Design of an adaptive nonsingular terminal sliding model control method for a bearingless permanent magnet synchronous motor. Transactions of the Institute of Measurement and Control, 2017, 39(12): 1821-1828.
[51] Y Li, X Xu, W J Wang. GA-BPNN based hybrid steering control approach for unmanned driving electric vehicle with in-wheel motors. Complexity, 2018: 6132139.
[52] J Y Wang, X T Yu, Z Hui, et al. Influence of running speed and lateral distance on vehicle transient aerodynamic characteristics during curve crossing. Journal of Jiangsu University (Natural Science Edition), 2017, 38(3): 249-253. (in Chinese)
[53] J B Erway, P E Gill. A subspace minimization method for the trust-region step. SIAM Journal on Optimization, 2009, 20(3): 1439-1461.
文章导航

/