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A Hierarchical Model Predictive Control Framework for Autonomous Ground Vehicles

Paolo Falcone (Institutionen för signaler och system, Mekatronik) ; Francesco Borrelli ; H. Eric Tseng ; Jahan Asgari ; Davor Hrovat
American Control Conference, Seattle, Washington, USA,11-13 June p. 3719 - 3724. (2008)
[Konferensbidrag, refereegranskat]

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.

Nyckelord: autonomous ground vehicles, front steering angle, hierarchical model predictive control, high level trajectory planner, low-level active steering-controller, nonlinear vehicle model, point-mass vehicle model, slippery roads

Denna post skapades 2008-04-10. Senast ändrad 2017-06-28.
CPL Pubid: 70009


Institutioner (Chalmers)

Institutionen för signaler och system, Mekatronik (2005-2017)



Chalmers infrastruktur