Integral MRAC With Bounded Switching Gain for Vehicle Lateral Tracking
Shilp Dixit, Umberto Montanaro, Mehrdad Dianati, Alexandros Mouzakitis, Saber Fallah
Abstract
In this article, an enhanced model reference adaptive control (EMRAC) algorithm is used to design a generic lateral-tracking controller for a vehicle. This EMRAC is different from the EMRAC in the literature as it adopts a σ-modification approach to bind the adaptive gain of the switching action. Moreover, an extended Lyapunov theory for discontinuous systems is used to analytically prove the ultimate boundedness of the closed-loop control system when the adaptive gain of the switching action is bounded with a σ-modification strategy. The control algorithm is applied to a vehicle path-tracking problem and its tracking performance is investigated under conditions of: 1) external disturbances such as crosswind; 2) road surface changes; 3) modeling errors; and 4) parameter missmatches in a co-simulation environment based on IPG Carmaker/MATLAB. The simulation studies show that the controller is effective at tracking a given reference path for performing different autonomous highway driving maneuvers while ensuring the boundedness of all closed-loop signals even when the system is subjected to the conditions mentioned above.