TY - JOUR
T1 - Cloud-Enabled Workflow-Based Real-Time MPC for Autonomous Vehicle Dynamical Trajectory Tracking
AU - Gao, Runze
AU - Zhou, Tong
AU - Dai, Li
AU - Zou, Zhenglin
AU - Xia, Yuanqing
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2026/5/1
Y1 - 2026/5/1
N2 - Model predictive control (MPC) has commonly been used in vehicle trajectory tracking problems, as it allows for predicting the future behaviors of multiple horizons and provides a certain degree of inherent robustness. However, due to its large computational amount, the real-time performance of MPC controller has been affected when requiring more computing resources for vehicle dynamical model. The cloud workflow method has been proven effective for accelerating complex algorithms, such as deep learning, genetic calculation, etc., by adopting the distributed structure of cloud computing. That inspires us to apply the cloud workflow method to data-intensive control problems. In this paper, a novel workflow-based MPC approach is proposed to accelerate the traditional computation mode. Firstly, a dynamical trajectory tracking method using MPC based on the alternating direction method of multipliers (ADMM) algorithm is designed for online optimization. Later, a cloud workflow construction method of MPC is designed to make full use of the distributed resources in cloud computing. In the meanwhile, the convergence conditions of the constructed MPC cloud workflow is proved for the vehicle dynamical trajectory tracking. Finally, based on a containerised workflow-based cloud control platform, we verify that the computational delays and average trajectory tracking errors are reduced by at most 68.1% and 87.6%, respectively.
AB - Model predictive control (MPC) has commonly been used in vehicle trajectory tracking problems, as it allows for predicting the future behaviors of multiple horizons and provides a certain degree of inherent robustness. However, due to its large computational amount, the real-time performance of MPC controller has been affected when requiring more computing resources for vehicle dynamical model. The cloud workflow method has been proven effective for accelerating complex algorithms, such as deep learning, genetic calculation, etc., by adopting the distributed structure of cloud computing. That inspires us to apply the cloud workflow method to data-intensive control problems. In this paper, a novel workflow-based MPC approach is proposed to accelerate the traditional computation mode. Firstly, a dynamical trajectory tracking method using MPC based on the alternating direction method of multipliers (ADMM) algorithm is designed for online optimization. Later, a cloud workflow construction method of MPC is designed to make full use of the distributed resources in cloud computing. In the meanwhile, the convergence conditions of the constructed MPC cloud workflow is proved for the vehicle dynamical trajectory tracking. Finally, based on a containerised workflow-based cloud control platform, we verify that the computational delays and average trajectory tracking errors are reduced by at most 68.1% and 87.6%, respectively.
KW - Cloud control system
KW - cloud workflow processing
KW - model predictive control
KW - vehicle trajectory tracking
UR - https://www.scopus.com/pages/publications/105021107927
U2 - 10.1109/TVT.2025.3630061
DO - 10.1109/TVT.2025.3630061
M3 - Article
AN - SCOPUS:105021107927
SN - 0018-9545
VL - 75
SP - 7381
EP - 7399
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 5
ER -