Automatic Deep Stall Recovery using Optimal Trajectory Planning

Abstract

Abstract This paper presents the design of an automatic deep stall recovery algorithm for large transport aircraft using optimal trajectory planning. Deep stall is a condition where an aircraft is trapped in a nose-high stall condition and its elevators cannot produce enough nose-down pitching moment to recover the aircraft from the stall. The NASA Generic Transport Model (GTM) is used as the basis for the design and verification of the system. The aerodynamic model of the NASA GTM simulation model is modified to exhibit deep stall behaviour. Simulations are performed to show that the modified aircraft model can be pushed into deep stall, and cannot be recovered using elevator actions only. The deep stall recovery task is formulated as an optimal path planning problem and solved using an A* search algorithm to find the optimal sequence of control actions and the resulting optimal state trajectory to escape from the deep stall. The A* algorithm performs the planning using a simplified, three-degrees-of-freedom (3DOF) aircraft model that models only the fast rotational dynamics. The automatic deep stall recovery is then verified in simulation using the full six-degrees-of-freedom (6DOF) NASA GTM aircraft model. The simulation results show that the system successfully recovers the aircraft from deep stall. The optimal sequence of control actions first uses the rudder to yaw the horizontal tailplane out of the aircraft’s own wake to regain elevator effectiveness, and then uses the elevators to pitch the nose down and recover from the stall.