A Hierarchical Model Predictive Control Framework for Autonomous Ground Vehicles
Paper i 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 ,  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
nonlinear vehicle model
hierarchical model predictive control
front steering angle