An Enhanced Model‐Free Adaptive Control Algorithm for Heavy‐Duty Truck Formations With Time Delay and Constraint Handling

ABSTRACT To address the challenges of longitudinal cooperative platoon control for multiple heavy‐duty trucks with time‐delay compensation under constraints, this paper presents a novel model‐free adaptive control algorithm termed TD‐cMFAC. The algorithm employs a pseudo partial derivative (PPD), a time‐varying parameter, to linearize the nonlinear dynamics of the multivehicle cooperative system using dynamic linearization techniques. Afterward, a dedicated TD‐cMFAC controller is then designed. To compensate for system time delays, a hybrid mechanism integrating a Smith predictor and a tracking differentiator (TD) is developed. The controller explicitly accounts for the input and output constraints inherent in practical control processes. The primary advantage of the TD‐cMFAC algorithm is its reliance solely on the input/output data of the multivehicle system throughout the control process while maintaining robust performance in the presence of time delays and constraints. Theoretical stability analysis confirms the robustness of the proposed method. By utilizing a MATLAB/Simulink and TruckSim co‐simulation interface, the effectiveness of the control strategy is demonstrated under complex driving is demonstrated, and its practical applicability is further validated through field tests conducted with an autonomous driving platform using heavy‐duty trucks on a test road in Tianjin, China.

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Publication Details

Journal
International Journal of Robust and Nonlinear Control
Published
2026-08-14
DOI
https://doi.org/10.1002/rnc.70694
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
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article

An Enhanced Model‐Free Adaptive Control Algorithm for Heavy‐Duty Truck Formations With Time Delay and Constraint Handling

Shida Liu, Zhongsheng Hou, Honghai Ji, Liguo Xing et al.
International Journal of Robust and Nonlinear Control
Traffic control and management
article

An Enhanced Model‐Free Adaptive Control Algorithm for Heavy‐Duty Truck Formations With Time Delay and Constraint Handling

Shida Liu, Zhongsheng Hou, Honghai Ji, Liguo Xing, Xiao Xiao
article en

Abstract

ABSTRACT To address the challenges of longitudinal cooperative platoon control for multiple heavy‐duty trucks with time‐delay compensation under constraints, this paper presents a novel model‐free adaptive control algorithm termed TD‐cMFAC. The algorithm employs a pseudo partial derivative (PPD), a time‐varying parameter, to linearize the nonlinear dynamics of the multivehicle cooperative system using dynamic linearization techniques. Afterward, a dedicated TD‐cMFAC controller is then designed. To compensate for system time delays, a hybrid mechanism integrating a Smith predictor and a tracking differentiator (TD) is developed. The controller explicitly accounts for the input and output constraints inherent in practical control processes. The primary advantage of the TD‐cMFAC algorithm is its reliance solely on the input/output data of the multivehicle system throughout the control process while maintaining robust performance in the presence of time delays and constraints. Theoretical stability analysis confirms the robustness of the proposed method. By utilizing a MATLAB/Simulink and TruckSim co‐simulation interface, the effectiveness of the control strategy is demonstrated under complex driving is demonstrated, and its practical applicability is further validated through field tests conducted with an autonomous driving platform using heavy‐duty trucks on a test road in Tianjin, China.

International Journal of Robust and Nonlinear Control
Qingdao University (CN), Qingdao University of Science and Technology (CN), North China University of Technology (CN), Vision Technology (United States) (US), Qingdao Municipal Center for Disease Control and Prevention (CN)
National Natural Science Foundation of China, Beijing Municipal Natural Science Foundation, National Key Research and Development Program of China
Openalex Percentile: Top 13%
Traffic control and management
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