Infinite-time model predictive motion control for autonomous driving considering delay in resource-constrained computing platforms

Although optimization-based methods have become mainstream in autonomous driving motion control, their high computational complexity and significant memory usage still limit their deployment on mass-production platforms with limited resources. Currently, mainstream Model Predictive Control (MPC) solvers often fail to fully exploit the special structure of MPC and require large-scale matrix KKT decomposition, leading to high computational complexity. In contrast, the Linear Quadratic Regulator (LQR) requires less computation and maintains a high stability margin, but it cannot consider controller input delays and time-varying references. Motivated by the dynamic programming derivation of the LQR, this paper leverages the specific structure of the MPC problem to derive an infinite horizon solution method. This approach explicitly handles steering delays and time-varying references while eliminating the need for large-scale KKT matrix decomposition, thereby significantly improving real-time performance. In summary, the proposed MPC strategy maintains the computational efficiency and stability of the LQR. Furthermore, the algorithm accommodates input delays and time-varying references to significantly improve control accuracy and stability in delayed scenarios. Ultimately, the designed algorithm was successfully implemented with a maximum single-step runtime of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mn>0</mml:mn> <mml:mo>.</mml:mo> <mml:mn>4</mml:mn> <mml:mspace width="0.25em"/> <mml:mi mathvariant="normal">ms</mml:mi> </mml:mrow> </mml:math> on a controller with a main frequency of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mn>300</mml:mn> <mml:mi mathvariant="normal">MHz</mml:mi> </mml:mrow> </mml:math> and a Flash capacity of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mn>5</mml:mn> <mml:mi mathvariant="normal">MB</mml:mi> </mml:mrow> </mml:math> . Additionally, considering actuator delays effectively improved the controller’s stability and control accuracy.

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Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Published
2026-05-08
DOI
https://doi.org/10.1177/09544070261442113
Primary Topic
Advanced Control Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

Infinite-time model predictive motion control for autonomous driving considering delay in resource-constrained computing platforms

Chaojun Yu, Yin Zhao, Zixuan Yang, Zhengcai Yang et al.
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Advanced Control Systems Optimization
article

Infinite-time model predictive motion control for autonomous driving considering delay in resource-constrained computing platforms

Chaojun Yu, Yin Zhao, Zixuan Yang, Zhengcai Yang, Linhe Ge, Haitao Zhang, Tong Wu
article en

Abstract

Although optimization-based methods have become mainstream in autonomous driving motion control, their high computational complexity and significant memory usage still limit their deployment on mass-production platforms with limited resources. Currently, mainstream Model Predictive Control (MPC) solvers often fail to fully exploit the special structure of MPC and require large-scale matrix KKT decomposition, leading to high computational complexity. In contrast, the Linear Quadratic Regulator (LQR) requires less computation and maintains a high stability margin, but it cannot consider controller input delays and time-varying references. Motivated by the dynamic programming derivation of the LQR, this paper leverages the specific structure of the MPC problem to derive an infinite horizon solution method. This approach explicitly handles steering delays and time-varying references while eliminating the need for large-scale KKT matrix decomposition, thereby significantly improving real-time performance. In summary, the proposed MPC strategy maintains the computational efficiency and stability of the LQR. Furthermore, the algorithm accommodates input delays and time-varying references to significantly improve control accuracy and stability in delayed scenarios. Ultimately, the designed algorithm was successfully implemented with a maximum single-step runtime of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mn>0</mml:mn> <mml:mo>.</mml:mo> <mml:mn>4</mml:mn> <mml:mspace width="0.25em"/> <mml:mi mathvariant="normal">ms</mml:mi> </mml:mrow> </mml:math> on a controller with a main frequency of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mn>300</mml:mn> <mml:mi mathvariant="normal">MHz</mml:mi> </mml:mrow> </mml:math> and a Flash capacity of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mn>5</mml:mn> <mml:mi mathvariant="normal">MB</mml:mi> </mml:mrow> </mml:math> . Additionally, considering actuator delays effectively improved the controller’s stability and control accuracy.

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Jilin University (CN), Wuhan University of Technology (CN), Hubei University of Automotive Technology (CN)
Openalex Percentile: Top 5%
Advanced Control Systems Optimization
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