Regular Papers

International Journal of Control, Automation, and Systems 2024; 22(2): 446-459

https://doi.org/10.1007/s12555-022-0389-9

© The International Journal of Control, Automation, and Systems

A Novel Stochastic Model Predictive Control Considering Predictable Disturbance With Application to Personalized Adaptive Cruise Control

Xuqiang Qiao, Ling Zheng, Yinong Li*, Ziwei Zhang, Jie Zeng, and Hao Zheng

Chongqing University

Abstract

A novel stochastic model predictive control (SMPC) scheme is proposed for automotive scenes based on high-performance and practical motion state prediction method. The significant properties of the proposed scheme are that: 1) it can accurately predict disturbances within the prediction horizon, and 2) the prediction results can be considered into the optimizing process to obtain a more efficient and accurate controller. As a result, the proposed adaptive cruise control (ACC) system can ensure driving safety and improve tracking accuracy and comfort performance while satisfying different driving styles. In detail, a large amount of naturalistic driving data is collected based on a real vehicle test platform at first. Then an adaptive optimization Gaussian process regression (AOGPR) is developed and trained with real measurements to predict the motion states of the preceding vehicle. The prediction module is embedded in SMPC to bind the collision conditions, tighten the states and finally construct a novel controller, i.e., AOGPR-SMPC controller. A bidirectional LSTM (BiLSTM) network is trained and tested for online recognizing driving styles to satisfy personalized car-following needs. The simulation and field tests verify and evaluate the proposed controller. The results demonstrate that the ACC system could realize personalized carfollowing according to the driver’s driving style, and the proposed controller can obtain better tracking accuracy and comfort performance compared with the GPR-SMPC controller and MPC controller.

Keywords Adaptive cruise control, driving style recognition, motion states prediction, stochastic model predictive control.

Article

Regular Papers

International Journal of Control, Automation, and Systems 2024; 22(2): 446-459

Published online February 1, 2024 https://doi.org/10.1007/s12555-022-0389-9

Copyright © The International Journal of Control, Automation, and Systems.

A Novel Stochastic Model Predictive Control Considering Predictable Disturbance With Application to Personalized Adaptive Cruise Control

Xuqiang Qiao, Ling Zheng, Yinong Li*, Ziwei Zhang, Jie Zeng, and Hao Zheng

Chongqing University

Abstract

A novel stochastic model predictive control (SMPC) scheme is proposed for automotive scenes based on high-performance and practical motion state prediction method. The significant properties of the proposed scheme are that: 1) it can accurately predict disturbances within the prediction horizon, and 2) the prediction results can be considered into the optimizing process to obtain a more efficient and accurate controller. As a result, the proposed adaptive cruise control (ACC) system can ensure driving safety and improve tracking accuracy and comfort performance while satisfying different driving styles. In detail, a large amount of naturalistic driving data is collected based on a real vehicle test platform at first. Then an adaptive optimization Gaussian process regression (AOGPR) is developed and trained with real measurements to predict the motion states of the preceding vehicle. The prediction module is embedded in SMPC to bind the collision conditions, tighten the states and finally construct a novel controller, i.e., AOGPR-SMPC controller. A bidirectional LSTM (BiLSTM) network is trained and tested for online recognizing driving styles to satisfy personalized car-following needs. The simulation and field tests verify and evaluate the proposed controller. The results demonstrate that the ACC system could realize personalized carfollowing according to the driver’s driving style, and the proposed controller can obtain better tracking accuracy and comfort performance compared with the GPR-SMPC controller and MPC controller.

Keywords: Adaptive cruise control, driving style recognition, motion states prediction, stochastic model predictive control.

IJCAS
June 2024

Vol. 22, No. 6, pp. 1761~2054

Stats or Metrics

Share this article on

  • line

Related articles in IJCAS

IJCAS

eISSN 2005-4092
pISSN 1598-6446