[1]张家旭,杨雄,施正堂,等.基于新型跟踪微分器的车轮滑移率跟踪控制[J].东南大学学报(自然科学版),2020,50(4):767-774.[doi:10.3969/j.issn.1001-0505.2020.04.022]
　Zhang Jiaxu,Yang Xiong,Shi Zhengtang,et al.Tracking control of wheel slip based on new tracking differentiator[J].Journal of Southeast University (Natural Science Edition),2020,50(4):767-774.[doi:10.3969/j.issn.1001-0505.2020.04.022]
点击复制

基于新型跟踪微分器的车轮滑移率跟踪控制()
分享到：

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
50
期数:
2020年第4期
页码:
767-774
栏目:
交通运输工程
出版日期:
2020-07-20

文章信息/Info

Title:
Tracking control of wheel slip based on new tracking differentiator
作者:
张家旭1; 2; 杨雄3; 施正堂3; 赵健1
1吉林大学汽车仿真与控制国家重点实验室, 长春 130022; 2中国第一汽车集团有限公司智能网联开发院, 长春 130011; 3浙江亚太机电股份有限公司, 杭州 311200
Author(s):
Zhang Jiaxu1; 2;  Yang Xiong3;  Shi Zhengtang3;  Zhao Jian1
1State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
2Intelligent Network Research and Development Institute, China FAW Group Co., Ltd., Changchun 130011, China
3Zhejiang Asia-Pacific Mechanical and Electronic Co., Ltd., Hangzhou 311200, China
关键词:
车辆工程;  新型跟踪微分器;  车轮滑移率跟踪控制;  模糊推理系统
Keywords:
vehicle engineering;  new tracking differentiator;  tracking control of wheel slip;  fuzzy inference system
分类号:
U461.1
DOI:
10.3969/j.issn.1001-0505.2020.04.022
摘要:
针对汽车直接横摆力矩控制系统和自动紧急制动系统对快速、准确和稳定的车轮滑移率跟踪控制的需求,提出了一种基于新型跟踪微分器的车轮滑移率跟踪控制器.首先,分别以车轮制动力矩导数、车轮滑移率跟踪误差及其导数作为控制量和状态量,建立了包含复合加性不确定性的车轮滑移率跟踪控制模型.随后,设计了一种新型跟踪微分器来估计车轮滑移率跟踪误差及其导数,为实现车轮滑移率全状态反馈跟踪控制奠定基础.以新型跟踪微分器输出为基础,利用模糊推理系统在线估计和补偿模型的复合加性不确定性,并结合幂函数和线性函数设计了一种具有快收敛速度特征和抑制颤振能力的车轮滑移率跟踪控制律.最后,从实际应用角度仿真验证所提出的车轮滑移率跟踪控制器的可行性和有效性.结果表明,所提出的车轮滑移率跟踪控制器可以快速、准确和稳定地跟踪任意车轮目标滑移率,并且车轮滑移率跟踪残差不大于0.333%.
Abstract:
Aiming at the requirements of direct yaw moment control system and automatic emergency braking system for the fast, accurate and stable tracking control of wheel slip, a wheel slip tracking controller based on a new tracking differentiator was proposed. First, a tracking control model for the wheel slip with lumped additive uncertainty was established by using the derivative of the wheel braking moment as a control variable and using the tracking error of the wheel slip and its derivative as state variables. Then, a new tracking differentiator was designed to estimate the tracking error of the wheel slip and its derivative to lay a foundation for full-state feedback(FSFB)tracking control of the wheel slip. Based on the output of the new tracking differentiator, a fuzzy inference system was used to estimate and compensate the lumped additive uncertainty, and a tracking control law for the wheel slip with the characteristic of fast convergence speed and the flutter-suppression ability was designed by combining the power function with the linear function. Finally, the feasibility and the effectiveness of the proposed controller were verified by simulation from the practical application. The simulation results show that the controller can track any desired wheel slip quickly, accurately and stably, and the wheel slip tracking residual is not greater than 0.333%.

参考文献/References:

[1] Zhang R H, He Z C, Wang H W, et al. Study on self-tuning tyre friction control for developing main-servo loop integrated chassis control system[J].IEEE Access, 2017, 5: 6649-6660. DOI:10.1109/access.2017.2669263.
[2] Fildes B, Keall M, Bos N, et al. Effectiveness of low speed autonomous emergency braking in real-world rear-end crashes[J].Accident Analysis & Prevention, 2015, 81: 24-29. DOI:10.1016/j.aap.2015.03.029.
[3] Zhang J X, Li J. Robust backstepping sliding mode control with L2-gain performance for reference input wheel slip tracking of vehicle[J]. Information Technology and Control, 2019, 48(4): 660-672. DOI: 10.5755/j01.itc.48.4.24297.
[4] Johansen T A, Petersen I, Kalkkuhl J, et al. Gain-scheduled wheel slip control in automotive brake systems[J].IEEE Transactions on Control Systems Technology, 2003, 11(6): 799-811. DOI:10.1109/tcst.2003.815607.
[5] Zhang J X, Li J. Adaptive backstepping sliding mode control for wheel slip tracking of vehicle with uncertainty observer[J].Measurement and Control, 2018, 51(9/10): 396-405. DOI:10.1177/0020294018795321.
[6] 薛艳梅,杨青青,邵珂.基于模糊小波神经网络算法的汽车滑移率控制研究[J].南京信息工程大学学报,2016,8(5):415-420. DOI:10.13878/j.cnki.jnuist.2016.05.003.
Xue Y M, Yang Q Q, Shao K. Vehicle slip ratio control via fuzzy wavelet networks[J].Journal of Nanjing University of Information Science & Technology(Natural Science Edition), 2016, 8(5): 415-420. DOI:10.13878/j.cnki.jnuist.2016.05.003. (in Chinese)
[7] Yeung D S, Ng W W Y, Wang D F, et al. Localized generalization error model and its application to architecture selection for radial basis function neural network[J].IEEE Transactions on Neural Networks, 2007, 18(5): 1294-1305. DOI:10.1109/tnn.2007.894058.
[8] Subudhi B, Ge S S. Sliding-mode-observer-based adaptive slip ratio control for electric and hybrid vehicles[J].IEEE Transactions on Intelligent Transportation Systems, 2012, 13(4): 1617-1626. DOI:10.1109/tits.2012.2196796.
[9] Buckholtz K R. Reference input wheel slip tracking using sliding mode control[C]//SAE Technical Paper Series. Warrendale, PA, USA, 2002: 1-10. DOI:10.4271/2002-01-0301.
[10] Shim T, Chang S, Lee S. Investigation of sliding-surface design on the performance of sliding mode controller in antilock braking systems[J].IEEE Transactions on Vehicular Technology, 2008, 57(2): 747-759. DOI:10.1109/TVT.2007.905391.
[11] Song J. Performance evaluation of a hybrid electric brake system with a sliding mode controller[J].Mechatronics, 2005, 15(3): 339-358. DOI:10.1016/j.mechatronics.2004.09.005.
[12] Amodeo M, Ferrara A, Terzaghi R, et al. Wheel slip control via second-order sliding-mode generation[J].IEEE Transactions on Intelligent Transportation Systems, 2010, 11(1): 122-131. DOI:10.1109/TITS.2009.2035438.
[13] Savaresi S M, Tanelli M. Active braking control systems design for vehicles[M]. London: Springer, 2010: 22-24. DOI:10.1007/978-1-84996-350-3.
[14] 韩京清,王伟.非线性跟踪-微分器[J].系统科学与数学,1994,14(2):177-183.
　　Han J Q, Wang W. Nonlinear tracking-differentiator[J]. Journal of Systems Science and Mathematical Sciences, 1994, 14(2): 177-183.(in Chinese)
[15] Yao B, Tomizuka M. Adaptive robust control of MIMO nonlinear systems in semi-strict feedback forms[J].Automatica, 2001, 37(9): 1305-1321. DOI:10.1016/s0005-1098(01)00082-6.
[16] 张家旭,李静.基于混合优化方法的UniTire轮胎模型参数辨识[J].吉林大学学报(工学版),2017,47(1):15-20. DOI:10.13229/j.cnki.jdxbgxb201701003.
Zhang J X, Li J. Parameter identification for UniTire model based on hybrid optimization method[J].Journal of Jilin University(Engineering and Technology Edition), 2017, 47(1): 15-20. DOI:10.13229/j.cnki.jdxbgxb201701003. (in Chinese)

相似文献/References:

[1]王海,蔡英凤,陈龙,等.基于Haar-NMF特征和改进SOMPNN的车辆检测算法[J].东南大学学报(自然科学版),2016,46(3):499.[doi:10.3969/j.issn.1001-0505.2016.03.008]
　Wang Hai,Cai Yingfeng,Chen Long,et al.Vehicle detection algorithm based on Haar-NMF features and improved SOMPNN[J].Journal of Southeast University (Natural Science Edition),2016,46(4):499.[doi:10.3969/j.issn.1001-0505.2016.03.008]
[2]张家旭,周时莹,周洪亮,等.未知时变扰动下的车轮滑移率鲁棒非线性跟踪控制[J].东南大学学报(自然科学版),2020,50(6):1128.[doi:10.3969/j.issn.1001-0505.2020.06.020]
　Zhang Jiaxu,Zhou Shiying,Zhou Hongliang,et al.Robust nonlinear tracking control of wheel slip with unknown time-varying disturbance[J].Journal of Southeast University (Natural Science Edition),2020,50(4):1128.[doi:10.3969/j.issn.1001-0505.2020.06.020]

备注/Memo

备注/Memo:
收稿日期: 2019-12-31.
作者简介: 张家旭(1985—),男,博士;赵健(联系人),男,博士,教授,博士生导师,zhaojian@jlu.edu.cn.
基金项目: 国家自然科学基金资助项目(51575225).
引用本文: 张家旭,杨雄,施正堂,等.基于新型跟踪微分器的车轮滑移率跟踪控制[J].东南大学学报(自然科学版),2020,50(4):767-774. DOI:10.3969/j.issn.1001-0505.2020.04.022.
更新日期/Last Update: 2020-07-20