An efficient and safe aircraft scheduling scheme is of great significance to the construction of smart airports. The towbarless aircraft taxiing system (TLATS) is a common dispatching method, which is composed of the towbarless towing vehicle (TLTV) and the aircraft. The system’s trajectory planning and autonomous steering control are being researched in order to improve steering accuracy, dispatching efficiency, and safety. In this article, the towbarless aircraft taxiing system is transformed into tractor-trailer system, the kinematic model and the dynamic model of the aircraft-tractor are established. Taking TLTV as a virtual subsystem of TLATS, and it is regarded as the controlled object of path planning and tracking. In response to the operational requirements of TLTV, an advanced A-star(A*) path planning algorithm is proposed to perform collision avoidance and turn radius restrictions during path planning resulting in a reference path for TLATS. Considering the estimation inaccuracy of the TLTV states and inertial parameters of the trailers, two force sensors are installed at the joint of the tractor and the aircraft wheel to obtain the force acting on the tractor, and the simplified tractor’s dynamic model is derived. With force measurements, TLTV can be a virtual subsystem, and the dynamic model is significantly simplified. The inaccurate parameters can be ignored, whereas their dynamic effects on the tractor are still precisely captured. Then, a trajectory tracking controller based on model predictive control (MPC) was designed according to the simplified dynamic model to compensate for the force of the tractor online and drive it to the desired trajectory, achieving autonomous steering of TLTV. Finally, simulation experiments are carried out to verify the autonomous steering control method. The experiments show that the optimization method has good performance of steering path tracking at a low speed of 5km/h conditions and achieves high precision tracking of standard trajectory in the presence of interference variables in the environment.