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Article

Longitudinal Finite-Time Control of Intelligent Vehicle Fleet Considering Time-Delay and Interference

1
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
2
Zhenjiang City Jiangsu University Engineering Technology Research Institute, Zhenjiang 212013, China
3
Jiangsu Province Engineering Research Center of Electric Drive System and Intelligent Control for Alternative Vehicles, Zhenjiang 212013, China
4
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
*
Author to whom correspondence should be addressed.
Machines 2026, 14(5), 570; https://doi.org/10.3390/machines14050570
Submission received: 16 April 2026 / Revised: 8 May 2026 / Accepted: 14 May 2026 / Published: 20 May 2026
(This article belongs to the Special Issue New Journeys in Vehicle System Dynamics and Control)

Abstract

To address the robustness degradation of intelligent vehicle fleet longitudinal control systems caused by the coexistence of disturbances and time-delay, a longitudinal finite-time control strategy based on a predictive finite-time extended state observer (PFTESO) is proposed. First, a finite-time extended state observer (FTESO) is designed to estimate system disturbances. To address the observer input asynchrony induced by time-delay, an improved Smith predictor is integrated into the FTESO to construct the PFTESO, thereby improving disturbance observation accuracy under delayed conditions. Meanwhile, a proportional–integral (PI) compensation controller is introduced based on the estimation error to further enhance control accuracy. Subsequently, a global fast integral terminal sliding mode controller (GFITSMC) is developed based on the PFTESO to improve the robustness and finite-time convergence performance of the intelligent vehicle fleet system under disturbances and time-delay. Finally, comparative simulation studies under different operating conditions are conducted to evaluate the effectiveness of the proposed strategy. Simulation results demonstrate that the proposed PFTESO effectively improves state observation accuracy under delayed conditions, where the RMSE values of z1 and z2 are reduced from 0.082 and 0.214 to 0.021 and 0.067, respectively. In addition, compared with conventional sliding mode control strategies, the proposed FTESO-GFITSMC reduces the peak acceleration chattering from ±0.23 m/s2 to 0.03 m/s2 while achieving a finite-time convergence time of 13 s. The proposed method exhibits superior robustness, faster convergence performance, and smoother acceleration response for an intelligent vehicle fleet under disturbances and delayed conditions.
Keywords: intelligent vehicle fleet; finite-time control; finite-time extended state observer; time-delay prediction; global fast terminal sliding mode control intelligent vehicle fleet; finite-time control; finite-time extended state observer; time-delay prediction; global fast terminal sliding mode control

Share and Cite

MDPI and ACS Style

Wang, S.; Shi, D.; Wang, S.; Xie, Y.; Chen, Y. Longitudinal Finite-Time Control of Intelligent Vehicle Fleet Considering Time-Delay and Interference. Machines 2026, 14, 570. https://doi.org/10.3390/machines14050570

AMA Style

Wang S, Shi D, Wang S, Xie Y, Chen Y. Longitudinal Finite-Time Control of Intelligent Vehicle Fleet Considering Time-Delay and Interference. Machines. 2026; 14(5):570. https://doi.org/10.3390/machines14050570

Chicago/Turabian Style

Wang, Songbo, Dehua Shi, Shaohua Wang, Yongquan Xie, and Yan Chen. 2026. "Longitudinal Finite-Time Control of Intelligent Vehicle Fleet Considering Time-Delay and Interference" Machines 14, no. 5: 570. https://doi.org/10.3390/machines14050570

APA Style

Wang, S., Shi, D., Wang, S., Xie, Y., & Chen, Y. (2026). Longitudinal Finite-Time Control of Intelligent Vehicle Fleet Considering Time-Delay and Interference. Machines, 14(5), 570. https://doi.org/10.3390/machines14050570

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