glossy-3d-blue-plane-iconN&R Engineering worked with engineers at NASA Glenn Research Center to develop a preliminary automated system aimed at preventing accidents for a transport aircraft during approaches and landings. This phase is particularly sensitive to flight crew errors because the aircraft is flying in a low energy state: engine power is low and both altitude and speed are decreasing. The system we developed—the Model-Predictive Automatic Recovery System (MPARS)—was designed to detect an impending accident and wait until the last moment for pilot intervention before automatically recovering the aircraft.

Figure 1 illustrates how MPARS works. The system contains a simplified airframe/engine model of the aircraft it’s flying on. During an approach to landing, MPARS queries this model every second to predict the future flight trajectory of the plane if it were to execute an aggressive emergency recovery maneuver (i.e., pull up and full engine power). If this predicted flight path dips below some predefined safe altitude threshold (e.g., 50 feet above the ground), MPARS overrides the normal flight and propulsion controls and executes the aforementioned recovery maneuver. The idea is that if pulling this aggressive maneuver results in just skirting the minimum altitude threshold, then the system can no longer wait for the pilot to intervene. If the airplane gets sufficiently close to the designated runway without triggering this override mode, then the landing is deemed safe and MPARS automatically shuts down, allowing the plane to land.

We have conducted many piloted evaluations of MPARS in a flight simulator at NASA Glenn (Figure 2). The tests confirmed the effectiveness of the system both in preventing ground collisions during unsafe approaches and not interfering with safe landings.

Services Provided:

  • Control System Design and Analysis
  • Mathematical Modeling of Aerospace Components and System Performance


Litt, J.S., Liu, Y., Sowers, T.S., Owen, A.K., and Guo, T., “Piloted Simulation Evaluation of a Model-Predictive Automatic Recovery System to Prevent Vehicle Loss of Control on Approach,” AIAA SciTech 2014, National Harbor, Maryland, 13-17 January 2014

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