Implementing Adaptive Cruise Control -Based Model Predictive Control using Turtlebot3 Robot
DOI:
https://doi.org/10.31185/wjes.Vol14.Iss2.864Keywords:
Adaptive Cruise Control, Model Predictive Control, Robotic Operating System, Turtlebot3 RobotAbstract
Adaptive cruise control (ACC) systems have been developed to enhance the performance of traditional cruise control (CC) systems. ACC efficiently reduces congestion and traffic accidents, decreases drivers’ workloads, and improves economic efficiency. This paper first validates and verifies a dual-level controller based on model predictive control (MPC) through an experiment that results in formulating an adaptive cruise control algorithm that improves robustness and safety. Second, two control methodologies, the MPC and conventional proportional integral derivative (PID) control, are tested and compared. The tests are realized using the TurtleBot3 robot as the host vehicle and a toy car as the target vehicle. Thus, it demonstrates a clear relationship between theoretical and practical system functionality while questioning the controller’s performance with real hardware. The novelty of this work lies in the integration of a dual-level architecture with real hardware validation, including a Light Detection and Ranging (LiDAR) data-filtering method that improves real-time responsiveness. Quantitative results from experimental scenarios show that the MPC controller achieves a 32% reduction in distance tracking error and improved robustness compared with the conventional PID controller. The numerical findings and experiments have proven the advantages and effectiveness of the MPC method over the traditional PID method when adaptability to perturbations or aggressive scenarios is essential.
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Copyright (c) 2026 Farah Mahdi Ali, Nizar Hadi Abbas
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