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Particle swarm optimization method for airplane trim

A particle swarm optimization and particle swarm technology, applied in the field of aircraft flight control, can solve problems such as model error, cumbersome process of obtaining trim state, and affecting the accuracy of trim state, so as to achieve the effect of improving work efficiency and accuracy

Inactive Publication Date: 2012-11-21
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The traditional method to obtain the stable state of aircraft trim is to perform linear approximation, that is, expand it into a Taylor series, and take the first-order items, omit the second-order items, and then solve the linear equation. Such a linear approximation will inevitably cause model errors. , which affects the accuracy of the trim state, and the process of obtaining the trim state is also very cumbersome

Method used

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  • Particle swarm optimization method for airplane trim
  • Particle swarm optimization method for airplane trim
  • Particle swarm optimization method for airplane trim

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0101] Example 1: Steady-state level flight

[0102] The number of particles is set to 20, and the maximum number of iterations is set to 150. When the value of the objective function is less than 10 -9 When the algorithm stops.

[0103] v t =43.0m / s, α=0.5°, θ=1.4°, p=0° / s, q=0° / s, r=0° / s;

[0104] The corresponding input values ​​are:

[0105] δ z =4.1°,δ x =0°,δ y =0°,δ p =65.3%, at this time the objective function value is 10 -9 , and the number of iterations is 116.

[0106] Among them, the throttle opening is expressed as a percentage, and the rated state opening is 80%.

Embodiment 2

[0107] Example 2: Steady State Climb

[0108] The number of particles is set to 25, and the maximum number of iterations is set to 200. When the value of the objective function is less than 10 -9 When the algorithm stops.

[0109] v t =43.0m / s, α=0.01°, β=0°, θ=2.51°, p=0° / s, q=0° / s, r=0° / s;

[0110] The corresponding input values ​​are:

[0111] δ z =3.6°, δ x =0°, δ y =0°, δ p =80.32%, at this time the objective function value is 10 -9 , and the number of iterations is 126.

Embodiment 3

[0112] Example 3: Steady-state decline

[0113] The number of particles is set to 30, and the maximum number of iterations is set to 150. When the value of the objective function is less than 10 -9 When the algorithm stops.

[0114] v t =43.3m / s, α=-3.72°, β=0°, θ=1.22°, p=0° / s, q=0° / s, r=0° / s;

[0115] The corresponding input values ​​are:

[0116] δ z =4.22°, δ x =0°, δ y =0°, δ p =31.73%, at this time the objective function value is 10 -9 , and the number of iterations is 113.

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Abstract

The invention discloses a particle swarm optimization method for airplane trim, which comprises the following steps of: (1) initializing particle swarm parameters, designating flight modes, a search range and constraint conditions and giving the constraint conditions of the corresponding flight mode; (2) under a given control input and a state given quantity, resolving an airplane nonlinear dynamics math model; (3) carrying out particle fitness detection; (4) updating a history optimal position of a swarm and an individual history optimal position of particles; (5) generating the same number of progeny particles, replacing parent particles with the progeny particles and carrying out hybridization on the particles; (6) continuously searching a target space until the maximum iteration of the particle swarm is reaches or a target function reaches a set value; and (7) judging end conditions and outputting a finally obtained trim state. According to the method, the automation of the trim process can be implemented and the working efficiency and the accuracy of the airplane trim process are improved.

Description

technical field [0001] The invention belongs to the field of aircraft flight control, in particular to an intelligent evolutionary global optimization method for automatic calculation of a trim stable state of an aircraft. Background technique [0002] In the process of aircraft overall design and flight performance analysis, the trim flight state of the aircraft must be obtained. The trim state is defined as the state in which all operating variables are constant or zero, that is, all linear velocity and angular velocity components are constant or zero, and all The acceleration component of is zero (assuming the weight of the aircraft remains constant, and ignoring the change of air density with altitude), such as constant direct flight, steady turning, etc. [0003] The traditional method to obtain the stable state of aircraft trim is to perform linear approximation, that is, expand it into a Taylor series, and take the first-order items, omit the second-order items, and t...

Claims

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Application Information

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IPC IPC(8): G06F17/50
Inventor 张民
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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