Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/93146
Title: CNN-based distributed adaptive control for vehicle-following platoon with input saturation
Authors: Guo, Xiang-Gui
Wang, Jian-Liang
Liao, Fang
Teo, Rodney Swee Huat
Keywords: Actuator Saturation
DRNTU::Engineering::Electrical and electronic engineering
String Stability
Issue Date: 2017
Source: Guo, X.-G., Wang, J.-L., Liao, F., & Teo, R. S. H. (2018). CNN-based distributed adaptive control for vehicle-following platoon with input saturation. IEEE Transactions on Intelligent Transportation Systems, 19(10), 3121-3132. doi:10.1109/TITS.2017.2772306
Series/Report no.: IEEE Transactions on Intelligent Transportation Systems
Abstract: A neural network-based distributed adaptive approach combined with sliding mode technique is proposed for vehicle-following platoons in the presence of input saturation, unknown unmodeled nonlinear dynamics, and external disturbances. A simple and straightforward strategy by adjusting only a single parameter is proposed to compensate for the effect of input saturation. Two spacing polices (i.e., traditional constant time headway policy and modified constant time headway policy) are used to guarantee string stability and maintain the desired spacing. Chebyshev neural networks (CNN) are used to approximate the unknown nonlinear functions in the followers online, and the implementation of the basic functions of CNN depends only on the leader's velocity and acceleration. Furthermore, unlike existing approaches, the nonlinearities of consecutive vehicles need not satisfy the matching condition. Finally, simulations are carried out to illustrate the effectiveness and the advantage of the proposed methods, first using a numerical example, followed by a practical example of a high speed train platoon.
URI: https://hdl.handle.net/10356/93146
http://hdl.handle.net/10220/48519
ISSN: 1524-9050
DOI: 10.1109/TITS.2017.2772306
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TITS.2017.2772306
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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