Rendering bounded error in adaptive robust path tracking control for autonomous vehicles

For the sake of safety, the vehicle path tracking control should not only ensure the stability of the path tracking error containing the lateral offset and the orientation error but also guarantee that both the transient and steady states of the lateral offset are within a specified safe boundary. However, the time-varying uncertainties of a vehicle system make the control design a tough task. This paper develops an adaptive robust control (ARC) which guarantees both the tracking stability and the bounded error property for autonomous vehicles. First, to handle the bounded error requirement, a barrier function based state transformation which converts the constrained lateral offset into an unconstrained state is proposed. Then, the path tracking control task is cast into an equality constraint of the system state. On this basis, a novel adaptive robust constraint-following controller is developed to make the transformed system follow the proposed equality constraint. Through Lyapunov minimax analysis, it is proved that the resulting control guarantees the approximate constraint-following performance and the bounded error property despite the presence of system uncertainties. Finally, the main theoretical results are verified through CarSim-Simulink co-simulation results.