Model predictive multi-objective vehicular adaptive cruise control

Shengbo Li, Keqiang Li, Rajesh Rajamani, Jianqiang Wang

Research output: Contribution to journalArticlepeer-review

330 Scopus citations

Abstract

This paper presents a novel vehicular adaptive cruise control (ACC) system that can comprehensively address issues of tracking capability, fuel economy and driver desired response. A hierarchical control architecture is utilized in which a lower controller compensates for nonlinear vehicle dynamics and enables tracking of desired acceleration. The upper controller is synthesized under the framework of model predictive control (MPC) theory. A quadratic cost function is developed that considers the contradictions between minimal tracking error, low fuel consumption and accordance with driver dynamic car-following characteristics while driver longitudinal ride comfort, driver permissible tracking range and rear-end safety are formulated as linear constraints. Employing a constraint softening method to avoid computing infeasibility, an optimal control law is numerically calculated using a quadratic programming algorithm. Detailed simulations with a heavy duty truck show that the developed ACC system provides significant benefits in terms of fuel economy and tracking capability while at the same time also satisfying driver desired car following characteristics.

Original languageEnglish (US)
Article number5471064
Pages (from-to)556-566
Number of pages11
JournalIEEE Transactions on Control Systems Technology
Volume19
Issue number3
DOIs
StatePublished - May 2011

Bibliographical note

Funding Information:
Manuscript received June 06, 2009; revised March 11, 2010; accepted March 24, 2010. Manuscript received in final form April 21, 2010. First published May 24, 2010; current version published April 15, 2011. Recommended by Associate Editor S. Liu. This work was supported by the National Science Foundation of China under Grant 50975155.

Keywords

  • Adaptive cruise control (ACC)
  • driver characteristics
  • fuel economy
  • model predictive control (MPC)
  • tracking capability

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