Aircraft taxiing represents a critical phase in air transportation systems, where efficiency, safety, and carbon emissions converge as key challenges. Amid increasing flight volumes and operational pressures, traditional full engine taxiing and dispatch towing taxiing have revealed significant limitations, including prolonged taxi times, elevated risks of ground collisions, and substantial fuel consumption. Three contemporary ground taxiing technologies—single-engine taxiing, semi-robotic dispatch towing, and onboard electric/hydraulic taxiing systems—are thoroughly reviewed in this paper along with their practical applications. The potential and constraints of each technology in enhancing taxiing efficiency, improving safety, and reducing fuel consumption and emissions are analyzed. Additionally covered are typical roadblocks like airport compatibility and airworthiness certification. Based on this review, a phased implementation strategy is proposed: near-term efforts should focus on optimizing single-engine taxiing operations, gradually expanding the application of semi-robotic dispatch towing systems, and exploring the feasibility of zero-emission onboard taxiing technologies. This approach aims to provide actionable insights and practical directions for optimizing ground operations and reducing emissions at major airports in China.

The wheel brake system of an aircraft is the key to ensure its safe landing and rejected takeoff. A wheel’s slip state is determined by the brake torque and ground adhesion torque, both of which have a large degree of uncertainty. It is this nature that brings upon the challenge of obtaining high deceleration rate for aircraft brake control. To overcome the disturbances caused by the above uncertainties, a braking control law is designed, which consists of two parts: runway surface recognition and wheel’s slip state tracking. In runway surface recognition, the identification rules balancing safety and braking efficiency are defined, and the actual identification process is realized through recursive least square method with forgetting factors. In slip state tracking, the LuGre model with parameter adaptation and a brake torque compensation method based on RBF neural network are proposed, and their convergence are proven. The effectiveness of our control law is verified through simulation and ground experiment. Especially in the experiments on the ground inertial test bench, compared to the improved pressure-biased-modulation (PBM) anti-skid algorithm, fewer wheel slips occur, and the average deceleration rate is increased by 5.78%, which makes it a control strategy with potential for engineering applications.