Flatness-based Model Predictive Control for Autonomous Vehicle Trajectory Tracking

Abstract

Model predictive control (MPC) has become more relevant to vehicle dynamics control due to its inherent capacity of treating system constraints. However, online optimization from MPC introduces an extensive computational burden for today's onboard microprocessors. To alleviate MPC computational load, several methods have been proposed. Among them, online successive system linearization and the resulting linear time-varying model predictive controller (LTVMPC) is one of the most popular options. Nevertheless, such online successive linearization commonly approximates the original (nonlinear) system by a linear one, which inevitably introduces extra modeling errors and therefore reduces MPC performance. Actually, if the controlled system possesses the "differential flatness" property, then it can be exactly linearized and an equivalent linear model will appear. This linear model maintains all the nonlinear features of the original system and can be utilized to design a flatness-based model predictive controller (FMPC). CarSim-Simulink joint simulations demonstrate that the proposed FMPC substantially outperforms a classical LTVMPC in terms of the path-tracking performance for autonomous vehicles.