A Real-Time Model Predictive Control Approach for Autonomous Active Steering
Paper i proceeding, 2006

The problem of controlling the front steering to stabilize a vehicle along a desired path is tackled in this paper. Although a Non-LinearModel Predictive Control (NLMPC) approach can achieve good performance and constraints fulfillment, its computational burden does not allow a real-time implementation. In order to decrease the complexity of the controller, in this paper we propose a suboptimal MPC scheme based on successive on-line linearizations of the non-linear vehicle model. The method stems from an accurate analysis of the vehicle nonlinearities, the constraints and the performance index in the optimal control problem. The simulation results show a significant reduction of the controller complexity, with a small loss of performances respect to a NLMPC controller. The suboptimal MPC control policy is compared to the control policy of a robot driver from Ford Motor Company.We show that better performance can be achieved with a smaller control effort, without violating vehicle physical constraints, by using a systematic control design procedure.


Paolo Falcone

Chalmers, Signaler och system, System- och reglerteknik

Francesco Borrelli

Jahan Asgari

H. Eric Tseng

Davor Hrovat

First IFAC International workshop on NMPC for Fast Systems, Grenoble, France, October 2006



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