This paper presents a comparison between two approaches addressing vehicle lateral dynamics control. In the framework of Auto-Steering for target tracking application, Model Predictive Control (MPC) and Interpolation Based Control (IBC) are studied on a similar model-based design. Both control strategies are of interest by their ability to treat problem constraints and actuator limits in a systematic manner, as well as their capability to include changes of the behavior when the speed of the vehicle changes, yielding a linear parameter varying system. Speed variation for the MPC controller brings an uncertain model, that will be described by a polytopic class of dynamics. Thanks to the online measurement of the parameter, the system dynamics will be computed at each sample time to solve the optimization problem. Parameterdependent Lyapunov functions and positive set invariance theory are used to ensure stability and feasibility. Interpolation Based control is a different approach, whose principle is to use a control action constructed as an interpolation between two computed extreme values. Each time, two linear programming problems are solved, which makes this approach to be a suitable trade-off between performance and computation cost.

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