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,fu...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.展开更多
侧向风对汽车行驶操纵稳定性有重要影响.通过分析侧向风干扰下车辆稳定性,提出基于主动前轮转向(active front wheel steering,AFS)的控制策略.AFS控制器采用线性二次型最优控制算法,以实现横摆角速度和质心侧偏角目标值跟踪.为了评价...侧向风对汽车行驶操纵稳定性有重要影响.通过分析侧向风干扰下车辆稳定性,提出基于主动前轮转向(active front wheel steering,AFS)的控制策略.AFS控制器采用线性二次型最优控制算法,以实现横摆角速度和质心侧偏角目标值跟踪.为了评价控制算法,基于MATLAB/Simulink和CarSim协同仿真环境建立整车动力学模型、单点预瞄驾驶员模型、控制器模型、道路和侧向风模型.仿真结果表明,AFS可有效提高车辆在侧向风干扰下的操纵稳定性,且控制算法对车速和路面附着系数具有良好的鲁棒性.展开更多
基金Funded by National Natural Science Foundation of China(No.51775014)Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems of China(No.GZKF-202010)+1 种基金National Key R&D Program of China(No.2019YFB2004503)the Science and Technology on Aircraft Control Laboratory of China。
文摘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.
文摘侧向风对汽车行驶操纵稳定性有重要影响.通过分析侧向风干扰下车辆稳定性,提出基于主动前轮转向(active front wheel steering,AFS)的控制策略.AFS控制器采用线性二次型最优控制算法,以实现横摆角速度和质心侧偏角目标值跟踪.为了评价控制算法,基于MATLAB/Simulink和CarSim协同仿真环境建立整车动力学模型、单点预瞄驾驶员模型、控制器模型、道路和侧向风模型.仿真结果表明,AFS可有效提高车辆在侧向风干扰下的操纵稳定性,且控制算法对车速和路面附着系数具有良好的鲁棒性.
