The objective of the NASA Aviation Safety Program is to improve the safety of current and future aircraft operating in the National Airspace System. Research under this program has focused on vehicle design, construction, operation and maintenance. Reducing aircraft loss of control accidents is critical to increasing aviation safety as it is the largest and most fatal aircraft accident category. Loss of control accidents result in aircraft operation outside the normal flight envelope in regions where aerodynamic data is either poorly characterized or unavailable. Hence it is important to monitor, in real-time, aircraft states and environmental conditions to assess the current state of the aircraft flight envelope. This paper describes the development of algorithms for dynamic flight envelope assessment using reachable set and nonlinear region of attraction techniques and their application to the NASA Generic Transport Model (GTM). The ability to estimate a safe envelope around various operating trim points is demonstrated. Nomenclature I zz Principal moment of inertia about Z-axis [lb-f t 2 ] b Wing span [ft] c Mean chord [ft] S ref Wing reference area [f t 2 ] C x , C z , C m X,Z body axes force and pitching moment coefficients [nondimensional] q Dynamic pressure [lb/f t 2 ] ∆X, ∆Y, ∆Z Displacement between cartesian coordinates of two points [ft] m Mass [slugs] g Acceleration due to gravity [ft/s 2 ] ω b 2U Normalized steady state component of body angular velocity [rad/s] q Pitch rate, body axis [rad/s] q Normalized Pitch rate, body axis [rad/s] EAS, U Equivalent Air Speed [ft/s] θ Pitch attitude [rad] ε radian-to-degree,180.0/π α Angle of attack [rad] LU T Look up table δ e Elevator control surface deflection [rad] δ th Throttle position, normalized [0 − 1]
A model-based approach is presented for predicting future state (position and velocity) of the preceding vehicle in response to velocity disturbance from lead vehicle in a platoon. Online parameter estimation is used to adapt model parameters based on characteristics of individual drivers in the platoon. A car-following model is used to describe platoon longitudinal dynamics. Examples are presented using simulated as well as real-traffic data.
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