InvSim algorithm for pre-computing airplane flight controls in limited-range autonomous missions, and demonstration via double-roll maneuver of Mirage III fighters

Abstract

In this work, we start with a generic mathematical framework for the equations of motion (EOM) in flight mechanics with six degrees of freedom (6-DOF) for a general (not necessarily symmetric) fixed-wing aircraft. This mathematical framework incorporates (1) body axes (fixed in the airplane at its center of gravity), (2) inertial axes (fixed in the earth/ground at the take-off point), wind axes (aligned with the flight path/course), (3) spherical flight path angles (azimuth angle measured clockwise from the geographic north, and elevation angle measured above the horizon plane), and (4) spherical flight angles (angle of attack and sideslip angle). We then manipulate these equations of motion to derive a customized version suitable for inverse simulation flight mechanics, where a target flight trajectory is specified while a set of corresponding necessary flight controls to achieve that maneuver are predicted. We then present a numerical procedure for integrating the developed inverse simulation (InvSim) system in time; utilizing (1) symbolic mathematics, (2) explicit fourth-order Runge-Kutta (RK4) numerical integration technique, and (3) expressions based on the finite difference method (FDM); such that the four necessary control variables (engine thrust force, ailerons' deflection angle, elevators' deflection angle, and rudder's deflection angle) are computed as discrete values over the entire maneuver time, and these calculated control values enable the airplane to achieve the desired flight trajectory, which is specified by three inertial Cartesian coordinates of the airplane, in addition to the Euler's roll angle. We finally demonstrate the proposed numerical procedure of flight mechanics inverse simulation (InvSim).

47 pages, 20 figures, 10 tables, published journal article, peer-reviewed, open access

References in corpus (18)

• AMFlow: a Mathematica package for Feynman integrals computation via Auxiliary Mass Flow
• Assessment of global warming in Al Buraimi, sultanate of Oman based on statistical analysis of NASA POWER data over 39 years, and testing the reliability of NASA POWER against meteorological measurements
• Adiabatic Flame Temperatures for Oxy-Methane, Oxy-Hydrogen, Air-Methane, and Air-Hydrogen Stoichiometric Combustion using the NASA CEARUN Tool, GRI-Mech 3.0 Reaction Mechanism, and Cantera Python Package
• MINN: Learning the dynamics of differential-algebraic equations and application to battery modeling
• The Sod gasdynamics problem as a tool for benchmarking face flux construction in the finite volume method
• Detailed and simplified plasma models in combined-cycle magnetohydrodynamic power systems
• Temperature Dependent Functions of the Electron Neutral Momentum Transfer Collision Cross Sections of Selected Combustion Plasma Species
• Estimated electric conductivities of thermal plasma for air-fuel combustion and oxy-fuel combustion with potassium or cesium seeding
• Hydrogen Utilization as a Plasma Source for Magnetohydrodynamic Direct Power Extraction (MHD-DPE)
• Coupled differential-algebraic equations framework for modeling six-degree-of-freedom flight dynamics of asymmetric fixed-wing aircraft
• Wind Speed Weibull Model Identification in Oman, and Computed Normalized Annual Energy Production (NAEP) From Wind Turbines Based on Data From Weather Stations
• State-space aerodynamic model reveals high force control authority and predictability in flapping flight
• Condenser Pressure Influence on Ideal Steam Rankine Power Vapor Cycle using the Python Extension Package Cantera for Thermodynamics
• Computed emissivity of carbon dioxide, water vapor, and their mixtures for a wide range of temperatures and pressure-pathlengths
• Profiles of the Power Density and Other Properties of Hydrogen Magnetohydrodynamic Generators at Conditions
• Effects of Turbulence Modeling and Parcel Approach on Dispersed Two-Phase Swirling Flow
• A Machine Learning-based Non-precipitating Clouds Estimation for THz Dual-Frequency Radar
• Changes in Fluctuation Waves in Coherent Airflow Structures with Input Perturbation

Cited by in corpus (2)

• OpenFOAM computational fluid dynamics (CFD) solver for magnetohydrodynamic open cycles, applied to the Sakhalin pulsed magnetohydrodynamic generator (PMHDG)
• Extended five-term nonlinear drag model for a wide range of cylinder wakes