Adaptive boosting algorithm-based turbofan engine direct data-driven control method

Patent metadata

Field	Value
Publication number	US-11788473-B2
Application number	US-202017052756-A
Country	US
Kind code	B2
Filing date	Mar 19, 2020
Priority date	Jan 7, 2020
Publication date	Oct 17, 2023
Grant date	Oct 17, 2023

Abstract

The present invention belongs to the technical field of control of aero-engines, and proposes an adaptive boosting algorithm-based turbofan engine direct data-driven control method. First, a turbofan engine controller is designed based on the Least Squares Support Vector Machine (LSSVM) algorithm, and further, the weight of a training sample is changed by an adaptive boosting algorithm so as to construct a turbofan engine direct data-driven controller combining a plurality of basic learners into strong learners. Compared with the previous solution only adopting LS SVM, the present invention enhances the control precision, improves the generalization ability of the algorithm, and effectively solves the problem of sparsity of samples by the adaptive boosting method. By the adaptive boosting algorithm-based turbofan engine direct data-driven control method designed by the present invention.

First claim

The invention claimed is:

1. An adaptive boosting algorithm-based turbofan engine direct data-driven control method, wherein the method comprises the following steps:
step 1: establishing a data set for the design of a turbofan engine direct data-driven controller
step 1.1: collecting control signals during the operation of the turbofan engine, including input fuel flow w f (n) of turbofan engine, corrected relative rotary speed n 1cor (n) of low-pressure rotor, and corrected relative rotary speed n 2cor (n) of high-pressure rotor, wherein n=1, 2, . . . , indicating the n th sampling period;
step 1.2: Δu represents the input of turbofan engine, Δy represents the output of turbofan engine, Δn 1cor (n) and Δn 2cor (n) respectively represent the variations of corrected relative rotary speeds of low-pressure rotor and high-pressure rotor of the turbofan engine, and Δw f (n) represents the variation of the input fuel flow of turbofan engine, defining
Δ u=[Δw f (1),Δ w f (2), . . . ,Δ w f ( n )] T

Δ y=[Δn 2cor (1),Δ n 2cor (2), . . . ,Δ n 2cor ( n )] T

[Δu,Δy] is the original data set for the design of the turbofan engine direct data-driven controller;
step 1.3: using the corrected relative rotary speed n 2cor of high-pressure rotor as a scheduling parameter p (with the dimension equal to 1), and converting the scheduling parameter p to be within [−1,1], as shown in the following formula:

p
=

2
⁢

n

2
⁢
cor

-

(

n

2
⁢
cor
⁢

_
⁢
max

+

n

2
⁢
cor
⁢

_
⁢
min

)

(

n

2
⁢
cor
⁢

_
⁢
max

-

n

2
⁢
cor
⁢

_
⁢
min

)

wherein n 2cor_max and n 2cor_min are respectively the upper limit and lower limit of the relative rotary speed n 2cor of high-pressure rotor of the turbofan engine;
step 2: adopting the methods of mean substitution and analysis of the Box-plot to perform data cleaning on the data in the data set [Δu,Δy], and filling missing data and eliminating outlier data in the data set;
step 3: adopting the LSSVM algorithm to design the turbofan engine controller
step 3.1: adopting the random sampling method to use 80% of the data set as a training data set and 20% as a testing data set;
step 3.2: adopting the Gauss kernel function Ω=K(p,t,k) to map the training data set to a high-dimensional feature space with the dimension of z from the original space so as to realize the linear regression of the training data set in the z-dimensional feature space, wherein the kernel function is expressed as follows:

Ω
=

K
⁡
(

p
,
t
,
k

)

=

exp
⁡
(

-



p
⁡
(
t
)

-

p
⁡
(
k
)



2
2

2
⁢

σ
2

)

.

wherein t and k respectively represent the time t and the time k, p(t) and p(k) represent the scheduling parameters of the time t and the time k, σ is the initial hyper-parameter radial basis width of the Gauss kernel function, and σ>0 is required;
step 3.3: establishing the optimization problem of LSSVM:

min

ω
,
b
,
e

J
⁡
(

ω
,
e

)

=

1
2

⁢

Assignees

• Univ Dalian Tech

Inventors

• Ma Yanhua
• Ding Nan
• Sun Ximing
• Zhao Xudong

Classifications

• F02C9/00 Primary

  Controlling gas-turbine plants; Controlling fuel supply in air- breathing jet-propulsion plants (controlling air intakes F02C7/057; controlling turbines F01D; controlling compressors F04D27/00; controlling in general G05) · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06F18/2411

  based on the proximity to a decision surface, e.g. support vector machines · CPC title

• G06N20/10

  using kernel methods, e.g. support vector machines [SVM] · CPC title

• F05D2200/11

  Sum · CPC title