Wheel slip tracking control of vehicle based on Elman neural network

被引：0

作者：

Zhang J. ^{[1
,2
]}

Shi Z. ^{[3
]}

Yang X. ^{[3
]}

Zhao J. ^{[1
]}

机构：

[1] State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun

[2] Intelligent Network R&D Institute, China FAW Group Co. Ltd., Changchun

[3] Zhejiang Asia-Pacific Mechanical and Electronic Co. Ltd., Hangzhou

来源：

Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2020年 / 48卷 / 06期

关键词：

Discrete-time integral sliding mode control; Elman neural network; Particle swarm optimization algorithm; Tracking control; Wheel slip;

D O I：

10.13245/j.hust.200611

中图分类号：

学科分类号：

摘要：

In order to meet the requirement of the high-speed emergency lane-changing obstacle avoidance system for the rapid, accurate and stable tracking control of wheel slip, a wheel slip discrete-time integral sliding mode tracking controller with strong robustness against the system uncertainty was proposed based on discrete-time sliding mode variable structure control method, and one-step delay estimation method was used to on-line estimate and compensate the system uncertainty to suppress the chattering phenomenon.Meanwhile, a desired wheel slip prediction model was constructed based on Elman neural network to predict the desired wheel slip at the next sampling time, which was included in the wheel slip discrete-time integral sliding mode tracking controller, and particle swarm optimization algorithm was used to modify the unknown weight of the desired wheel slip prediction model to improve the prediction accuracy.Finally, the feasibility and effectiveness of the proposed wheel slip discrete-time integral sliding mode tracking controller were verified by simulation. © 2020, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.

引用

页码：64 / 69

页数：5

共 14 条

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