High Velocity Lane Keeping Control Method Based on the Non-Smooth Finite-Time Control for Electric Vehicle Driven by Four Wheels Independently

Meng, Qinghua; Zhao, Xinhui; Hu, Chuan; Sun, Zong‐Yao

doi:10.3390/electronics10060760

Cited by 6 publications

(3 citation statements)

References 42 publications

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“…The simulation results and from the comparison to the slide mode controller technique showed that the proposed technique has a good control result for the active FWS of an electrical vehicle. The article (Meng et al, 2021) recommended a lane-tracking controller using a non-smooth finite-time control technique. A lane-tracking control prototype was built and a tracking scheme and error weight superposition technique to track error computing for the lane-tracking control were suggested by depending on the given information of the used lane line.…”

Section: Finite-time Control: Approaches and Reviewmentioning

confidence: 99%

A Finite-Time Speed and Direction Control for Four-Wheel Drive System

M.Shareef

Aula

2021

UKH J SCI ENG

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With the rapid use of the Four-Wheel Drive System (FWDS) worldwide, the necessity of having an adequate control system to control speed and direction in FWDS is extremely required. For this purpose, several control schemes are available in the literature to control the speed and direction in FWDS which should be fast convergence of the control, continuous control performance, and solving external disturbances. In latest years, finite-time controllers (FTC) have gained more consideration from many researchers in the control area, who have expressed applications in several procedures and systems. This research provides a major review of the FTC approaches via both input and output feedbacks for controlling FWDS.

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Section: Finite-time Control: Approaches and Reviewmentioning

confidence: 99%

A Finite-Time Speed and Direction Control for Four-Wheel Drive System

M.Shareef

Aula

2021

UKH J SCI ENG

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“…Lane detection and following is a fundamental task that can be accomplished with classical methods or can benefit from combining methods based on deep learning and further pre-or post-processing [4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition

et al. 2021

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There are various techniques to approach learning in autonomous driving; however, all of them suffer from some problems. In the case of imitation learning based on artificial neural networks, the system must learn to correctly identify the elements of the environment. In some cases, it takes a lot of effort to tag the images with the proper semantics. This is also relevant given the need to have very varied scenarios to train and to thus obtain an acceptable generalization capacity. In the present work, we propose a technique for automated semantic labeling. It is based on various learning phases using image superposition combining both scenarios with chromas and real indoor scenarios. This allows the generation of augmented datasets that facilitate the learning process. Further improvements by applying noise techniques are also studied. To carry out the validation, a small-scale car model is used that learns to automatically drive on a reduced circuit. A comparison with models that do not rely on semantic segmentation is also performed. The main contribution of our proposal is the possibility of generating datasets for real indoor scenarios with automatic semantic segmentation, without the need for endless human labeling tasks.

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“…Meng et al presented 'High Velocity Lane Keeping Control Method Based on the Non-Smooth Finite-Time Control for Electric Vehicle Driven by Four Wheels Independently' [6], in which two kinds of tracking error computing methods of lane keeping control for electric vehicles were proposed to tackle different conditions, and a Non-FT lane keeping controller was designed to keep the EV-DFWI running in the desired lane suffering external disturbances.…”

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confidence: 99%