Yaw moment control systems improve vehicle stability and handling in severe driving manoeuvres. Nevertheless, the control\ud system performance is limited by the unmodelled dynamics and parameter uncertainties. To guarantee robustness of the control system\ud against system uncertainties, this paper proposes a gain scheduling Robust Linear Quadratic Regulator (RLQR), in which an extra\ud control term is added to the feedback of a conventional LQR to limit the closed-loop tracking error in a neighbourhood of the origin of\ud its state-space, despite of the uncertainties and persistent disturbances acting on the plant. In addition, the intrinsic parameter-varying\ud nature of the vehicle dynamics model with respect to the longitudinal vehicle velocity can jeopardize the closed-loop performance of\ud fixed-gain control algorithms in different driving conditions. Therefore, the control gains optimally vary based on the actual\ud longitudinal vehicle velocity to adapt the closed-loop system to the variations of this parameter. The effectiveness of the proposed RLQR\ud in improving the robustness of classical LQR against model uncertainties and parameter variations is proven analytically, numerically\ud and experimentally. The numerical and experimental results are consistent with the analytical analysis proving that the proposed RLQR\ud reduces the ultimate bound of error dynamics.