Ground maneuver for front-wheel drive aircraft via deep reinforcement learning

The maneuvering time on the ground accounts for 10%–30%of their flight time,and it always exceeds 50%for short-haul aircraft when the ground traffic is congested.Aircraft also contribute significantly to emissions,fuel burn,and noise when taxiing on the ground at airports.There is an urgent need to reduce aircraft taxiing time on the ground.However,it is too expensive for airports and aircraft carriers to build and maintain more runways,and it is space-limited to tow the aircraft fast using tractors.Autonomous drive capability is currently the best solution for aircraft,which can save the maneuver time for aircraft.An idea is proposed that the wheels are driven by APU-powered(auxiliary power unit)motors,APU is working on its efficient point;consequently,the emissions,fuel burn,and noise will be reduced significantly.For Front-wheel drive aircraft,the front wheel must provide longitudinal force to tow the plane forward and lateral force to help the aircraft make a turn.Forward traction effects the aircraft’s maximum turning ability,which is difficult to be modeled to guide the controller design.Deep reinforcement learning provides a powerful tool to help us design controllers for black-box models;however,the models of related works are always simplified,fixed,or not easily modified,but that is what we care about most.Only with complex models can the trained controller be intelligent.High-fidelity models that can easily modified are necessary for aircraft ground maneuver controller design.This paper focuses on the maneuvering problem of front-wheel drive aircraft,a high-fidelity aircraft taxiing dynamic model is established,including the 6-DOF airframe,landing gears,and nonlinear tire force model.A deep reinforcement learning based controller was designed to improve the maneuver performance of front-wheel drive aircraft.It is proved that in some conditions,the DRL based controller outperformed conventional look-ahead controllers.