A Hierarchical Model Predictive Control Framework for Autonomous Ground Vehicles
Paper in proceeding, 2008

A hierarchical framework based on Model Predictive Control (MPC) for autonomous vehicles is presented. We formulate a predictive control problem in order to best follow a given path by controlling the front steering angle while fulfilling various physical and design constraints. We start from the low-level active steering-controller presented in [3], [9] and integrate it with a high level trajectory planner. At both levels MPC design is used. At the high-level, a trajectory is computed on-line, in a receding horizon fashion, based on a simplified point-mass vehicle model. At the low-level a MPC controller computes the vehicle inputs in order to best follow the desired trajectory based on detailed nonlinear vehicle model. This article presents the approach, the method for implementing it, and successful preliminary simulative results on slippery roads at high entry speed.

point-mass vehicle model

high level trajectory planner

low-level active steering-controller

autonomous ground vehicles

slippery roads

nonlinear vehicle model

hierarchical model predictive control

front steering angle


Paolo Falcone

Chalmers, Signals and Systems, Systems and control

Francesco Borrelli

University of California

H. Eric Tseng

Ford Motor Company

Jahan Asgari

Ford Motor Company

Davor Hrovat

Ford Motor Company

American Control Conference

0743-1619 (ISSN)

3719 - 3724
9781424420797 (ISBN)

Subject Categories

Control Engineering





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