Path-Tracking and Lateral Stabilization for Automated Vehicles via Learning-based Robust Model Predictive Control

It is a great challenge to guarantee both path-tracking performance and vehicle stability when suffering from aggressive uncertainties and severe disturbances. We design a novel learning-based robust model predictive path-tracking controller to alleviate the influence of disturbances, avoid over-conservative steering actions, and mediate the conflict between path-tracking and vehicle stability. Specifically, we firstly utilize the friction limits of tires and define an enveloped stable zone in the phase portrait which is used as safety constraints. Secondly, two approaches under the model predictive control (MPC) framework are employed to tackle the severe uncertainties and disturbances: 1) a deep neural network (DNN) dynamics model is employed to estimate the predictive error and attenuate the mismatch between the nominal model and actual plant; and 2) a local feedback linear quadratic regulator (LQR) is used to stabilize the system matrix, calculate invariant tube, and thus guarantee all state and control constraints are satisfied. Finally, real vehicle experiments indicate that the proposed controller can achieve an over 18% improvement in path-tracking performance and guarantee vehicle stability, even for cases with severe uncertainties and disturbances.