Stall Recovery Guidance Using Fast Model Predictive Control

Abstract

Based on a detailed analysis of recent loss-of-control events, the Aircraft State Awareness Joint Safety Analysis Team has identified the need to develop algorithms and display strategies to provide control guidance for recovery from approach-to-stall or stall. In order to be effective, such guidance should enhance the pilot's ability to execute the Federal Aviation Administration's recommended stall recovery procedure. This paper explores the use of a fast model predictive control algorithm that determines near optimal recovery guidance, which quantifies the aircraft configuration and situation dependent recovery information required to maximize the effectiveness of the recovery. This information includes the magnitude of the initial pitch down maneuver, the specific amount of airspeed and thrust needed before pulling out of the recovery dive, as well as the maximum pitch-up rate that can be sustained without causing a secondary stall. The algorithm was integrated and tested with an in-house desktop simulator that implements the General Transport Aircraft model and the associated stall aircraft dynamics. Preliminary results are presented to demonstrate the use of the proposed approach as a recovery aid for pilots.

Piloted Simulator Evaluation Results of Flight Physics Based Stall Recovery Guidance

“…22 The pseudo-control hedging-based algorithm calculates a pitch angle guidance signal by inverting the aircraft pitch rate dynamics when the angle of attack exceeds the stall limit. 21 For the model-predictive control approach, an optimal recovery is computed using a fast convex optimization algorithm, 23 which is capable of running reliably from inside the Ames Vertical Motion Simulator (VMS) frame calculation loop on a 1.25 GHz DEC-Alpha, Real-Time Operating System. This paper describes how a couple of these algorithms have been evaluated by experienced commercial as well as test pilots in the high-fidelity Vertical Motion Simulator (VMS) at NASA Ames Research Center (ARC).…”

“…This algorithm has important features that help to ensure fast and reliable convergence of the optimal solutions at relatively low computational cost suitable for online implementations. The more advanced computational details covering the application of the FMPC algorithm to stall recovery guidance are documented in [23]. There are two important features to the FMPC approach.…”

Economic Model-Predictive Control Strategies for Aircraft Deep-stall Recovery with Stability Guarantees⋆

“…LOA minimizing EMPC was considered in [10] for automatic recovery from a high-bank condition. However, closed-loop stability of the proposed MPC recovery scheme was not proven in either [9] or [10].…”

“…Aircraft upset incidents remain a severe cause of fatalities in civil aviation [1] and this has motivated research into upset and loss of control recovery [2][3][4][5][6]. Upset recovery has been approached with various control techniques including adaptive control [7], machine learning [8], and model predictive control (MPC) [9,10]. MPC, in particular, is promising since it can handle nonlinearities, actuator saturation, and state constraints.…”

“…MPC has been applied to a variety of aerospace systems [16]. In [9], MPC is employed to generate guidance trajectories for recovery of a piloted aircraft from a highpitch upset. EMPC has been extensively studied in the context of process control [13]; however, comparatively few aerospace applications have been reported in the literature [16].…”