A hyperplane aircraft climbing trajectory swarm intelligence optimization method

By improving the chicken flock algorithm to optimize the climb trajectory of hypersonic aircraft, the dynamics and flight safety issues in the climb trajectory optimization were solved, and the climb time and fuel consumption were significantly reduced, while the selection of the power switching point was optimized.

CN117421856BActive Publication Date: 2026-07-21BEIHANG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
BEIHANG UNIV
Filing Date
2023-08-09
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Optimizing the climb trajectory of hypersonic vehicles is difficult to achieve optimal solutions in terms of dynamic characteristics, flight safety characteristics, and selection of dynamic mode switching points. In particular, there are obvious coupling and constraint limitations in the combined propulsion system, which makes trajectory optimization difficult.

Method used

An improved chicken flock algorithm is used for intelligent optimization of the climb trajectory of hypersonic aircraft. By constructing dynamic equations, initializing the chicken flock algorithm, updating the hierarchy and parent-child relationship in the flock, and combining penalty terms to handle constraints, the dynamic parameters of the climb segment are optimized.

Benefits of technology

It significantly shortens the climb time and total fuel consumption of hypersonic aircraft, improves the optimization efficiency and accuracy of climb trajectory, and solves the problem of trajectory optimization during the climb phase.

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Abstract

The application discloses a hyperplane climbing track group intelligent optimization method, which comprises the following steps: constructing a hyperplane climbing section dynamics equation, and obtaining to-be-optimized parameters in the hyperplane climbing section dynamics equation; designing a hierarchical cost function considering multiple constraints and turbojet, subsonic ramjet and supersonic ramjet three power mode switching Mach numbers; and optimizing the cost function through a chicken swarm algorithm, thereby completing the hyperplane climbing track group optimization. The hyperplane climbing track parameter optimization is performed through the improved chicken swarm algorithm, the suitable power mode switching Mach number is selected while the complex constraints of the hyperplane climbing section are met, the climbing scheme capable of significantly shortening the climbing time and total fuel consumption is obtained, and the problem that the track is difficult to optimize under the hyperplane cross-power mode and long-time window climbing condition is solved.
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