Direct Dynamic-Simulation Approach to Trajectory Optimization for Rotorcraft Category-A Maneuver Procedures
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ORIGINAL PAPER
Direct Dynamic-Simulation Approach to Trajectory Optimization for Rotorcraft Category-A Maneuver Procedures Yong Hyeon Nam1 · Chang-Joo Kim1
· Seong Han Lee1 · Sung Wook Hur1 · Yi Young Kwak1
Received: 25 February 2020 / Revised: 3 September 2020 / Accepted: 14 September 2020 © The Korean Society for Aeronautical & Space Sciences 2020
Abstract This paper treats numerical methods for an efficient prediction of the rotorcraft emergency procedures after engine failures. The analytical means of compliance for the Category-A requirements can be used for the type certification of the transportcategory rotorcraft when their fidelities are approved by the civil airworthiness authority. However, the most promising trajectory optimization approaches to the Category-A maneuver analyses typically suffer from a dimensionality problem when a high-fidelity math model is adopted. To cope with such difficulties, the paper proposes new techniques, where the system states except the initial ones and all dynamic constraints are removed from the resultant nonlinear programming problem. For these proposes, the controls are parameterized using the Hermit splines with the local support and efficient recursive formulas to predict the constraint-function Jacobians are derived. The efficiency of the proposed techniques is compared with that using the pseudo-spectral collocation method. In addition to an autorotational descent maneuver, four Category-A procedures for the continued takeoff, rejected takeoff, continued landing, and balked landing maneuvers are analyzed with varying the engine-failure conditions and with a suitable consideration on the pilot-delay time to validate the usefulness of the proposed methods. Keywords Trajectory optimization · Direct method · Point-mass model · Rotorcraft emergency procedures
Abbreviations Cat-A FAA AC NOCP NLP NAE SNOPT OEI KKT DDSA SQP LGL CTO RTO CL
B 1
Category-A Federal aviation administration Advisory circular Nonlinear optimal control problem Nonlinear programming problem Nonlinear algebraic equation Sparse nonlinear optimizer One-engine-inoperative Karush–Kuhn–Tucker Direct dynamic-simulation approach Sequential quadratic programming Legendre–Gauss–Lobatto Continued takeoff Rejected takeoff Continued landing
Chang-Joo Kim [email protected] Department of Aerospace and Information Engineering, Konkuk University, Seoul, Korea
BL TDP LDP
Balked landing Takeoff decision point Landing decision point
Nomenclature J φ(∗) f obj f ψ(∗) g(∗) x u m n Le Li t (t0 , t f ) c(∗) J N ,M
Objective function Boundary objective function Integral objective function Forcing function in the system dynamics Equality constraints Inequality constraints State vector of system dynamics Control vector of system dynamics Number of system controls Number of system states Number of equality constraints Number of inequality constraints Time variables (initial and final) Continuity condition Integral part of objective function
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International Journal of Aeronautical and Space Sciences
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