In this study, a robust adaptive control strategy using a \({{\mathcal {L}}}\) 1 theory is applied to fulfil the motion control of a four-wheel electric vehicle. The dynamic model includes longitudinal velocity, lateral velocity, and yaw rate. To develop the proposed control strategy, the vehicle dynamics are decomposed into linear and nonlinear components. The linear part is controlled by fine-tuned state feedback, with its steady-state error addressed by a feedforward control block. The nonlinear part is addressed based on nonlinear adaptive laws and \({{\mathcal {L}}}\) 1 theory to mitigate adverse effects on the linear dynamics, unwanted parameter changes, and external disturbances. An essential component of the proposed approach is the incorporation of a reference model that dictates the desired system responses. A projection algorithm is used to instantly estimate the nonlinear part of the vehicle dynamics. The performance of the closed-loop system is thoroughly evaluated over a range of vehicle manoeuvres, assessing factors such as steady-state accuracy, transient response, and power consumption. In addition, the effectiveness of the proposed method is compared with conventional model reference adaptive control and a recent robust control approach, particularly in terms of robustness to uncertainties inherent in the nonlinear aspects of vehicle dynamics.
