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Six-Degree-of-Freedom Aircraft Landing Trajectory Planning with Runway Alignment

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abstract

This paper presents a numerical optimization algorithm for generating approach and landing trajectories for a six-degree-of-freedom (6-DoF) aircraft. We improve on the existing research on aircraft landing trajectory generation by formulating the trajectory optimization problem with additional real-world operational constraints, including 6-DoF aircraft dynamics, runway alignment, constant wind field, and obstacle avoidance, to obtain a continuous-time nonconvex optimal control problem. Particularly, the runway alignment constraint enforces the trajectory of the aircraft to be aligned with the runway only during the final approach phase. This is a novel feature that is essential for preventing an approach that is either too steep or too shallow. The proposed method models the runway alignment constraint through a multi-phase trajectory planning scheme, imposing alignment conditions exclusively during the final approach phase. We compare this formulation with the existing state-triggered constraint formulation for runway alignment. To solve the formulated problem, we design a novel sequential convex programming algorithm called xPTR that extends the penalized trust-region (PTR) algorithm by incorporating an extrapolation step to expedite convergence. We validate the proposed method through extensive numerical simulations, including a Monte Carlo study, to evaluate the robustness of the algorithm to varying initial conditions.

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representative citing papers

Optimal Preconditioning for Online Quadratic Cone Programming

math.OC · 2025-01-24 · accept · novelty 5.0

For quadratic cone programs with strongly convex objectives, choosing the objective scaling factor as sqrt(sigma_min / 2) provably minimizes the KKT condition number, and packaging this with hypersphere and row-normalization steps yields a fast, factorization-free preconditioner.

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  • Optimal Preconditioning for Online Quadratic Cone Programming math.OC · 2025-01-24 · accept · none · ref 45 · internal anchor

    For quadratic cone programs with strongly convex objectives, choosing the objective scaling factor as sqrt(sigma_min / 2) provably minimizes the KKT condition number, and packaging this with hypersphere and row-normalization steps yields a fast, factorization-free preconditioner.