Integral MRAC With Bounded Switching Gain for Vehicle Lateral Tracking

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.