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2010•Unpublished venueRequires access

Model Predictive Adaptive Cruise Control

Emre Kural, Bilin Aksun Güvenç

Open publisher page 41 citations

Abstract

In this paper, the Model Predictive Control (MPC) structure is used to solve the ACC problem and the performance of the design is tested using a realistic nonlinear vehicle model. A hierarchical control structure is used where MPC is placed on top of the hierarchy while the actuators are controlled by simpler linear controllers. The real-time optimization needed for MPC is solved using Quadratic Programming (QP). The performance of the MPC is tested under different traffic scenarios. The control system is able to determine the ongoing scenarios autonomously, using available measurements.

About this research paper

What this paper is about

In this paper, the Model Predictive Control (MPC) structure is used to solve the ACC problem and the performance of the design is tested using a realistic nonlinear vehicle model. A hierarchical control structure is used where MPC is placed on top of the hierarchy while the actuators are controlled by simpler linear controllers. The real-time optimization needed for MPC is solved using Quadratic Programming (QP). The performance of the MPC is tested under different traffic scenarios. The control system is able to determine the ongoing scenarios autonomously, using available measurements.

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OpenAlex reports 41 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Available abstract

In this paper, the Model Predictive Control (MPC) structure is used to solve the ACC problem and the performance of the design is tested using a realistic nonlinear vehicle model. A hierarchical control structure is used where MPC is placed on top of the hierarchy while the actuators are controlled by simpler linear controllers. The real-time optimization needed for MPC is solved using Quadratic Programming (QP). The performance of the MPC is tested under different traffic scenarios. The control system is able to determine the ongoing scenarios autonomously, using available measurements.

Key concepts: Model predictive control, Cruise control, Quadratic programming, Control theory (sociology), Hierarchy, Computer science, Actuator, Linear programming

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