This paper discusses the combination of model-based feedforward and feedback controller for the hybrid wheel-legged robot. Uncertainties in the modeling parameters such as friction coefficients, inertia matrices, and position of CoM can bring negative effects in the performance of model-based motion controllers. The ability of a control system to compensate for unforeseen circumstances is not only utilitarian but essential if one wishes to bring the robotic systems into the real world. To develop such a robust motion controller for the wheeled biped robot, we present the design and implementation of a nonlinear feedforward controller together with linearized feedback LQR to control the robot on a flat surface. This study investigates the controller performance: feedback, feedforward, and the combined in simulations and also with the real wheel-legged robot. Furthermore, the Extended Kalman Filter (EKF) is implemented to estimate the system states and reduce the sensor noise of the real robot.