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Input–Output Constrained Adaptive Cruise Control
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AI
DOI：10.1109/TCST.2025.3573671.png)
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Abstract
En 中文
In this article, we introduce an adaptive cruise control (ACC) scheme that ensures collision avoidance by enforcing amplitude-and-rate constraints on the control input, while dynamically adapting output performance specifications. The proposed control strategy leverages the adaptive performance control (APC) methodology to handle asymmetrical input constraints, providing robust performance under diverse driving conditions. By incorporating multiple constraints into a unified funnel-based approach, the controller eliminates the need for complex optimization techniques, thereby maintaining low computational complexity. A rigorous theoretical analysis is provided, demonstrating the boundedness of all closed-loop signals and the overall feasibility of the control design. Comprehensive and comparative simulations highlight the ability of the proposed scheme to maintain a safe intervehicular distance, achieve accurate velocity tracking when state constraints (SCs) allow, and strict adherence to input limitations, resulting in improved passenger comfort. Finally, experimental validation using mobile robots confirms the effectiveness of the proposed ACC scheme in real-world conditions.
Keywords:
Adaptive performance control (APC)
autonomous driving
cruise control
input constraints
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