Steering control using model predictive control and multiple internal models

Progress in understanding the driver/vehicle interface requires an improved model of driver skill in the steering control task. A possible explanation for driver steering skill involves driver usage of multiple internal models of the vehicle dynamics, where each internal model represents the actual vehicle dynamics within a limited range of the performance envelope of the vehicle. This paper presents the derivation of a nonlinear driver steering controller using multiple linearized models of the vehicle dynamics and Model Predictive Control (MPC) theory. The resulting Multiple Linear Internal Model (MLIM) driver steering controller was implemented in a simulation environment with a nonlinear vehicle model, and tested performing a closed-loop steering task. The resulting path-following abilities of the MLIM controller are reviewed and compared to a MPC driver model using a single internal model.