CN113093794A - Multimode accurate partitioning method for wide-area flight - Google Patents
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Abstract
The invention relates to a multimode accurate partitioning method for wide-area flight, which determines key factors influencing multimode partitioning of an aircraft in a wide-area climbing process through demand analysis and takes the key factors as modal indicating variables; performing preliminary determination of stable modes according to demand analysis and priori knowledge, and ensuring that the modes are arranged according to actual time sequence; performing modal division and identification on the aircraft offline data, and combining a stable modal preliminary division result and similarity analysis to realize accurate division of a stable modal and a transition modal; designing a mode switching strategy based on a multi-mode accurate division result, and finally providing a multi-mode switching system facing to flight; according to the invention, the multimode accurate division of the wide-area climbing process of the aircraft is realized in an experience/data dual-drive mode, and the multimode process switching strategy is designed according to stable mode class attribution and transition mode starting and stopping moments, so that the smooth switching of the wide-area flight multimode is facilitated, the flight safety is improved, and the method is suitable for engineering application.
Description
Technical Field
The invention belongs to the technical field of aerospace, and particularly relates to a multimode accurate partitioning method for wide-area flight.
Background
With the rapid development of aerospace technology, the envelope of the aircraft is wider and wider, so that the aircraft can take off horizontally from the ground and fly in a wide area. In the wide-area climbing process, the speed of the aircraft is continuously increased to a hypersonic speed, and the aircraft plays an important role in the aspects of remote rapid transportation, space travel, global rapid attack and the like.
During wide-area flight, an aircraft may face different power modes, aerodynamic configurations, and flight missions, resulting in multiple flight modes. The multiple modes are general characteristics of the wide-area climbing process of the aircraft, different models need to be established and different controllers need to be designed for different modes, therefore, the wide-area climbing process of the aircraft is the switching process of the multi-mode flight controllers, and the accurate division and identification of the multiple modes are important for designing an aircraft switching control system. The wide-range flight process comprises a plurality of stable flight modes, different transition modes exist among different stable modes, a plurality of working modes are divided according to experience, the transition modes are ignored, multi-mode accurate division identification and stable conversion among adjacent stable modes cannot be achieved, safety is poor, and engineering implementation is not facilitated. Therefore, the research of the multimode accurate division method for wide-area flight has great significance and urgent need for the research of the multi-mode switching control of flight.
Disclosure of Invention
Technical problem to be solved
In order to overcome the defect that the existing multi-mode division method for flying is poor in practicability, the invention provides a multi-mode accurate division method for wide-area flying.
Technical scheme
A multimode accurate division method for wide-area flight is characterized by comprising the following steps:
step 1: analyzing the demand of the aircraft in the wide-area climbing process based on the flight task and the instruction knowledge base, and determining key factors influencing the multi-mode division of the flight as modal indicating variables;
step 2: the empirical risk of modal identification is fully considered, the stable modal is preliminarily determined according to the priori knowledge, and the number N of the stable modal of the wide-area climbing of the aircraft is given0And the operating time S of each stable moden,n＝1,2,…,N0Further analyzing the logic relation among the multiple modes to ensure that the modes are arranged according to the actual time sequence;
and step 3: carrying out modal division and identification on the offline data of the aircraft based on an improved K-means clustering algorithm to realize accurate division of a stable mode and a transition mode;
and 4, step 4: establishing a multi-modal model set based on a plurality of divided stable modes, designing a modal switching strategy based on a transition mode, and constructing an aircraft switching system facing wide-area climbing by combining the multi-modal model set and the modal switching strategy;
writing a wide-area-climb-oriented aircraft system as a multiple-input multiple-output switching system as shown below
Wherein, XiI is 1,2, …, n is a state variable, fi,σ(t)And gi,σ(t)As a non-linear function, uσ(t)For control input, σ (t) [ [0, ∞) → M ═ 1,2, …, M } is the switching signal;
for the switching signal, the number m is equal to the number N of stable modes of the division0The switching strategy is specifically described as follows: switching signals are switched according to the multi-modal process time sequence, and in a stable mode, calling a corresponding mode model and designing a controller; in the transition mode, because the transition mode time is short, if the starting time of the transition mode is in the last window of the previous stable mode, the model corresponding to the previous stable mode is called, if the starting time of the transition mode is in the first window of the transition mode, the model corresponding to the next stable mode is called, and meanwhile, soft switching control is designed.
The further technical scheme of the invention is as follows: the specific process of step 3 is as follows:
(a) window cutting
Selecting a cutting window with the length of H to cut the two-dimensional flight data matrix X along the sampling direction, wherein the length of the cutting window is H
N＝K×H+d (1)
Wherein N is the number of the sampling data, K is the number of the cutting windows, d is the sampling data which are not cut, and d is more than or equal to 0 and less than or equal to H;
recording the two-dimensional matrix of the window data obtained by cutting as
Calculating the mean vector of these two-dimensional matrices
As a basic unit of modal division;
(b) clustering process
Clustering window mean vectors by using an improved K-means clustering algorithm, wherein the input of the algorithm is a window mean vector set
And a minimum distance threshold theta of two subclass cluster centers, wherein the output of the algorithm is the membership of each window belonging to different subclasses
Number of sum subclasses Cst；
Therefore, the whole multi-modal process can be clustered into C by the algorithmstA subclass;
(c) time interval division
Dividing window units which are continuous in time and belong to the same subclass into the same time period according to the time sequence of window mean vector arrangement;
the divided sub-period is recorded as
Wherein M is0Is the number of divided time periods. Each sub-period belongs to a membership of a different subclass of
Wherein C is a clustered subclass;
the time length of the sub-period is recorded as
For aircraftDetermining a stable mode;
(d) stable modality determination
Determining the shortest operation time of the stable mode based on the operation time of each mode in the step 2
Smin＝min{S1,S2,…,Sn} (3)
By comparing sub-period time lengths
And stable mode minimum operation time SminTo determine a stable mode
Wherein the stable modes belonging to the same subclass are defined as the same stable mode;
further introducing the stable mode time interval in the step 2 to analyze the identified stable mode, and if the identified stable mode is in the corresponding time interval, attributing to the specific stable mode;
(e) modal precision partitioning
Deeply analyzing windows of a stable mode and a transition mode, and judging that the starting time of the transition mode occurs at the rear half section of the last window of a previous stable mode or the front half section of the first window of the transition mode, and the ending time of the transition mode occurs at the rear half section of the last window of the transition mode or the front half section of the first window of the next stable mode;
for confirmation of transition modality start time, assume that the modality starts from kth1Starting the transition from one window, the k-th window, which is the last window of the stable mode in the front sequence1-1 window starts to be reanalyzed and judged with a shorter sliding window L, the sliding step length being h;
further, the average value of the small sliding windows obtained by analyzing the two windows is calculated, and the average value of the small sliding windows is recorded as
Defining the similarity between the sliding window and the preamble stable mode as
Wherein,
the average value of variables of the pre-order stable modes, and J is the number of multi-mode process variables;
introducing a similarity threshold value alpha as a boundary parameter, analyzing the relation between each similarity and the threshold value, and determining the initial moment of the transition mode as
The above rule is expressed as when from the t-th1The small windows start to continuously have r small windows all meeting gammat<α, then consider the multimodal process from the t1The small window enters a transition mode;
thus will t1The starting position of the small window is used as the start of the transition mode, and the starting time of the transition mode is (k)1-2)×H+(t1-1)×h+1；
For confirmation of the transition modality end time, assume that the modality is from kth2The transition is started from the last window of the transition mode, i.e. the kth window2-1 window starts to be reanalyzed and judged with a shorter sliding window L, the sliding step length being h;
further, the average value of the small sliding windows obtained by analyzing the two windows is calculated, and the average value of the small sliding windows is recorded as
Defining the similarity between the sliding window and the subsequent stable mode as
Wherein,
is the mean value of the variables of the subsequent stable mode;
analyzing the relation between each similarity and the threshold value, and determining the ending moment of the transition mode as
The above rule is expressed as when from the t-th2The small windows start to continuously have r small windows all meeting gammatIf not less than alpha, the multi-modal process is considered to be from the tth2The small window enters a subsequent stable mode;
thus will t2The starting position of the small window is used as the ending of the transition mode, and the ending time of the transition mode is (k)2-2)×H+(t2-1)×h+1。
Advantageous effects
The invention provides a multimode accurate division method for wide-area flight. The method analyzes the requirement of the aircraft in the wide-area climbing process, preliminarily determines the stable mode according to priori knowledge, further realizes accurate division of the stable mode and the transition mode based on an improved K-means clustering algorithm, and finally constructs a wide-area flight-oriented multi-mode switching system by combining a multi-mode model set and a mode switching strategy, thereby facilitating the engineering realization. The beneficial effects are as follows:
(1) a clustering algorithm based on flight data is introduced on the basis of empirically determining the stable mode of the aircraft, so that the experience risk of mode division is reduced;
(2) comparing the clustered stable modes by using the stable mode time intervals which are divided by experience, so that the category attribution of the stable modes is realized;
(3) the wide-range climbing aircraft is converted into a switching system, a switching strategy of switching signals is given through multi-mode accurate division, and smooth switching among the multi-modes is guaranteed.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention will now be further described with reference to the following examples and drawings:
referring to fig. 1, the multimode accurate division method for wide-area flight of the invention comprises the following specific steps:
step 1: considering a Rocket-based combined cycle (RBCC) aerospace vehicle, the main flight mission of the vehicle is horizontal takeoff and climbing to the height of more than 30Km, and the speed reaches about 20 Mach;
the aerospace craft passes through a very wide airspace of dense atmosphere and adjacent space in the climbing process, the environment difference of different flight sections is large, high requirements of wide-range work adaptation, low comprehensive oil consumption, high thrust-weight ratio and the like are provided for a power system, and single power cannot meet the requirements, so that the aerospace craft needs to climb in a relay manner by adopting combined power, and the aerospace craft can achieve the optimal thrust performance in each flight section; different power systems correspond to different working modes and cause different flight modes, so that the wide-area climbing process of the aerospace vehicle is a multi-mode process;
determining key influence factors of the aerospace vehicle combination power working mode as multi-mode division through the requirement analysis, wherein the key influence factors can be used as mode indicating variables;
step 2: the RBCC aerospace craft combines the rocket engine and the ramjet engine in the same flow channel, and a new thermodynamic cycle mode is formed by utilizing the rocket jet flow and the ramjet flow channel; based on the existing research, the wide-area climbing process of the RBCC aerospace vehicle is divided into 4 stable modes including an injection mode, a sub-combustion stamping mode, a super-combustion stamping mode and a rocket mode, and the running time of each stable mode is SnAnd n is 1,2,3, 4. The 4 stable modes work in sequence, wherein the injection mode is set at Mach 0-2.5, the sub-combustion stamping mode is set at Mach 2.5-6, the super-combustion stamping mode is set at Mach 6-10, and the rocket mode is set at Mach 10-20, and the method can work according to flight sample dataA speed-time curve is drawn, and then the time interval of each stable mode is determined;
and step 3: the method is characterized in that modal division and identification are carried out on RBCC aerospace vehicle offline data based on an improved K-means clustering algorithm, accurate division of a stable mode and a transition mode is realized, and the specific process is as follows
(a) Window cutting
Selecting a cutting window with the length of H to cut the two-dimensional flight data matrix X along the sampling direction, wherein the length of the cutting window is H
N＝K×H+d (1)
Wherein N is the number of the sampling data, K is the number of the cutting windows, d is the sampling data which are not cut, and d is more than or equal to 0 and less than or equal to H;
recording the two-dimensional matrix of the window data obtained by cutting as
K is 1,2, …, K, and the mean vector x of these two-dimensional matrices is foundkAs a basic unit of modal division;
(b) clustering process
Clustering window mean vectors by using an improved K-means clustering algorithm, wherein the input of the algorithm is a window mean vector set
And a minimum distance threshold theta of two subclass cluster centers, wherein the output of the algorithm is the membership of each window belonging to different subclasses
Number of sum subclasses Cst；
Therefore, the whole multi-modal process can be clustered into C by the algorithmstA subclass;
(c) time interval division
Dividing window units which are continuous in time and belong to the same subclass into the same time period according to the time sequence of window mean vector arrangement;
the divided sub-period is recorded as
Wherein M is0The number of divided time periods; each sub-period belongs to a membership of a different subclass of
Wherein C is a clustered subclass;
the time length of the sub-period is recorded as
Determining a stable mode of the aircraft;
(d) stable modality determination
Determining the shortest operation time of the stable mode based on the operation time of each mode in the step 2
Smin＝min{S1,S2,S3,S4} (3)
By comparing sub-period time lengths
And stable mode minimum operation time SminTo determine a stable mode
Wherein the stable modes belonging to the same subclass are defined as the same stable mode;
further introducing the stable mode time interval in the step 2 to analyze the identified stable mode, and if the identified stable mode is in the corresponding time interval, attributing to the specific stable mode;
(e) modal precision partitioning
Deeply analyzing windows of a stable mode and a transition mode, and judging that the starting time of the transition mode occurs at the rear half section of the last window of a previous stable mode or the front half section of the first window of the transition mode, and the ending time of the transition mode occurs at the rear half section of the last window of the transition mode or the front half section of the first window of the next stable mode;
for confirmation of transition modality start time, assume that the modality starts from kth1Starting the transition from one window, the k-th window, which is the last window of the stable mode in the front sequence1-1 window starts to be reanalyzed and judged with a shorter sliding window L, the sliding step length being h;
further, the average value of the small sliding windows obtained by analyzing the two windows is calculated, and the average value of the small sliding windows is recorded as
Defining the similarity between the sliding window and the preamble stable mode as
Wherein,
the average value of variables of the pre-order stable modes, and J is the number of multi-mode process variables;
introducing a similarity threshold value alpha as a boundary parameter, analyzing the relation between each similarity and the threshold value, and determining the initial moment of the transition mode as
The above rule is expressed as when from the t-th1The small windows start to continuously have r small windows all meeting gammat<α, then consider the multimodal process from the t1The small window enters a transition mode;
thus will t1The starting position of the small window is used as the start of the transition mode, and the starting time of the transition mode is (k)1-2)×H+(t1-1)×h+1；
For confirmation of the transition modality end time, assume that the modality is from kth2The transition is started from the last window of the transition mode, i.e. the kth window2-1 window starts to be reanalyzed and judged with a shorter sliding window L, the sliding step length being h;
further, the average value of the small sliding windows obtained by analyzing the two windows is calculated, and the average value of the small sliding windows is recorded as
Defining the similarity between the sliding window and the subsequent stable mode as
Wherein,
is the mean value of the variables of the subsequent stable mode;
analyzing the relation between each similarity and the threshold value, and determining the ending moment of the transition mode as
The above rule is expressed as when from the t-th2The small windows start to continuously have r small windows all meeting gammatIf not less than alpha, the multi-modal process is considered to be from the tth2The small window enters a subsequent stable mode;
thus will t2The starting position of the small window is used as the ending of the transition mode, and the ending time of the transition mode is (k)2-2)×H+(t2-1)×h+1；
And 4, step 4: establishing a multi-mode model set based on the divided 4 stable modes, designing a mode switching strategy based on a transition mode, and constructing an RBCC aerospace vehicle switching system by combining the multi-mode model set and the mode switching strategy;
the RBCC aerospace vehicle system is written as a multi-input multi-output switching system as shown in the specification
Wherein, three channel attitude angle X1＝[θ ψ φ]ΤAnd attitude angular velocity X2＝[ωx ωy ωz]ΤIs a state variable, theta, psi, phi, omegax，ωyAnd ωzPitch angle, yaw angle, roll angular velocity, yaw angular velocity and pitch angular velocity, respectively; u. ofσ(t)＝[δx,σ(t) δy,σ(t) δz,σ(t)]ΤTo control the input, δi,σ(t)X, y and z are respectively a rolling rudder deflection, a yawing rudder deflection and a pitching rudder deflection; sigma (t) belongs to {1,2,3,4} as a switching signal, and corresponds to an injection mode, a sub-combustion punching mode, a super-combustion punching mode and a rocket mode in sequence;
the nonlinear function is as follows:
f1,σ(t)＝0
wherein, JiX, y, and z are rotational inertia in x, y, and z directions, respectively; q is dynamic pressure, S is 334.73m2Is a reference area; l isbEach lateral direction 18.288m, Lc24.384m is the longitudinal reference length; alpha is an attack angle, and beta is a sideslip angle;
is pneumatically operatedForce coefficient, delta term includes parameters, model uncertainty and linearization error;
the switching signal corresponds to 4 stable modes which are divided, and the switching strategy is specifically described as follows: switching signals are switched according to the multi-modal process time sequence, and in a stable mode, calling a corresponding mode model and designing a controller; in the transition mode, because the transition mode time is short, if the starting time of the transition mode is in the last window of the previous stable mode, the model corresponding to the previous stable mode is called, if the starting time of the transition mode is in the first window of the transition mode, the model corresponding to the next stable mode is called, and meanwhile, soft switching control is designed.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present disclosure.
Claims (2)
1. A multimode accurate division method for wide-area flight is characterized by comprising the following steps:
step 1: analyzing the demand of the aircraft in the wide-area climbing process based on the flight task and the instruction knowledge base, and determining key factors influencing the multi-mode division of the flight as modal indicating variables;
step 2: the empirical risk of modal identification is fully considered, the stable modal is preliminarily determined according to the priori knowledge, and the number N of the stable modal of the wide-area climbing of the aircraft is given0And the operating time S of each stable moden,n＝1,2,…,N0Further analyzing the logic relation among the multiple modes to ensure that the modes are arranged according to the actual time sequence;
and step 3: carrying out modal division and identification on the offline data of the aircraft based on an improved K-means clustering algorithm to realize accurate division of a stable mode and a transition mode;
and 4, step 4: establishing a multi-modal model set based on a plurality of divided stable modes, designing a modal switching strategy based on a transition mode, and constructing an aircraft switching system facing wide-area climbing by combining the multi-modal model set and the modal switching strategy;
writing a wide-area-climb-oriented aircraft system as a multiple-input multiple-output switching system as shown below
Wherein, XiI is 1,2, …, n is a state variable, fi,σ(t)And gi,σ(t)As a non-linear function, uσ(t)For control input, σ (t) [ [0, ∞) → M ═ 1,2, …, M } is the switching signal;
for the switching signal, the number m is equal to the number N of stable modes of the division0The switching strategy is specifically described as follows: switching signals are switched according to the multi-modal process time sequence, and in a stable mode, calling a corresponding mode model and designing a controller; in the transition mode, because the transition mode time is short, if the starting time of the transition mode is in the last window of the previous stable mode, the model corresponding to the previous stable mode is called, if the starting time of the transition mode is in the first window of the transition mode, the model corresponding to the next stable mode is called, and meanwhile, soft switching control is designed.
2. The multimode accurate partition method for wide-area flight according to claim 1, characterized in that the specific process of step 3 is as follows:
(a) window cutting
Selecting a cutting window with the length of H to cut the two-dimensional flight data matrix X along the sampling direction, wherein the length of the cutting window is H
N＝K×H+d (1)
Wherein N is the number of the sampling data, K is the number of the cutting windows, d is the sampling data which are not cut, and d is more than or equal to 0 and less than or equal to H;
recording the two-dimensional matrix of the window data obtained by cutting as
Calculating the mean vector of these two-dimensional matrices
As a basic unit of modal division;
(b) clustering process
Clustering window mean vectors by using an improved K-means clustering algorithm, wherein the input of the algorithm is a window mean vector set
And a minimum distance threshold θ for the centers of the two subclasses of clusters, the output of the algorithm being the membership u (k) of each window belonging to a different subclass:
number of sum subclasses Cst；
Therefore, the whole multi-modal process can be clustered into C by the algorithmstA subclass;
(c) time interval division
Dividing window units which are continuous in time and belong to the same subclass into the same time period according to the time sequence of window mean vector arrangement;
the divided sub-period is recorded as
Wherein M is0Is the number of divided time periods. Each sub-period belongs to a membership of a different subclass of
Wherein C is a clustered subclass;
the time length of the sub-period is recorded as
Determining a stable mode of the aircraft;
(d) stable modality determination
Determining the shortest operation time of the stable mode based on the operation time of each mode in the step 2
Smin＝min{S1,S2,…,Sn} (3)
By comparing sub-period time lengths
And stable mode minimum operation time SminTo determine a stable mode
Wherein the stable modes belonging to the same subclass are defined as the same stable mode;
further introducing the stable mode time interval in the step 2 to analyze the identified stable mode, and if the identified stable mode is in the corresponding time interval, attributing to the specific stable mode;
(e) modal precision partitioning
Deeply analyzing windows of a stable mode and a transition mode, and judging that the starting time of the transition mode occurs at the rear half section of the last window of a previous stable mode or the front half section of the first window of the transition mode, and the ending time of the transition mode occurs at the rear half section of the last window of the transition mode or the front half section of the first window of the next stable mode;
for confirmation of transition modality start time, assume that the modality starts from kth1Starting the transition from one window, the k-th window, which is the last window of the stable mode in the front sequence1-1 window starts to be reanalyzed and judged with a shorter sliding window L, the sliding step length being h;
further, the average value of the small sliding windows obtained by analyzing the two windows is calculated, and the average value of the small sliding windows is recorded as
Defining the similarity between the sliding window and the preamble stable mode as
Wherein,
the average value of variables of the pre-order stable modes, and J is the number of multi-mode process variables;
introducing a similarity threshold value alpha as a boundary parameter, analyzing the relation between each similarity and the threshold value, and determining the initial moment of the transition mode as
The above rule is expressed as when from the t-th1The small windows start to continuously have r small windows all meeting gammat<α, then consider the multimodal process from the t1The small window enters a transition mode;
thus will t1The starting position of the small window is used as the start of the transition mode, and the starting time of the transition mode is (k)1-2)×H+(t1-1)×h+1；
For confirmation of the transition modality end time, assume that the modality is from kth2The transition is started from the last window of the transition mode, i.e. the kth window2-1 window starts to be reanalyzed and judged with a shorter sliding window L, the sliding step length being h;
further, the average value of the small sliding windows obtained by analyzing the two windows is calculated, and the average value of the small sliding windows is recorded as
Defining the similarity between the sliding window and the subsequent stable mode as
Wherein,
is the mean value of the variables of the subsequent stable mode;
analyzing the relation between each similarity and the threshold value, and determining the ending moment of the transition mode as
The above rule is expressed as when from the t-th2The small windows start to continuously have r small windows all meeting gammatIf not less than alpha, the multi-modal process is considered to be from the tth2The small window enters a subsequent stable mode;
thus will t2The starting position of the small window is used as the ending of the transition mode, and the ending time of the transition mode is (k)2-2)×H+(t2-1)×h+1。
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