CN114545907B - Fault detection method of flight control system based on filter - Google Patents
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- 238000011156 evaluation Methods 0.000 claims abstract description 23
- 230000000750 progressive effect Effects 0.000 claims abstract description 7
- 230000006641 stabilisation Effects 0.000 claims abstract description 6
- 238000011105 stabilization Methods 0.000 claims abstract description 6
- 238000011426 transformation method Methods 0.000 claims abstract description 5
- 230000003416 augmentation Effects 0.000 claims description 9
- 230000005540 biological transmission Effects 0.000 claims description 6
- 230000010354 integration Effects 0.000 claims description 4
- 238000005259 measurement Methods 0.000 claims description 4
- 230000000295 complement effect Effects 0.000 claims description 3
- 230000003190 augmentative effect Effects 0.000 claims 1
- 230000035945 sensitivity Effects 0.000 abstract description 6
- 230000008569 process Effects 0.000 description 5
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Abstract
The embodiment of the disclosure provides a fault detection method of a filter-based flight control system, which belongs to the technical field of data identification and specifically comprises the following steps: establishing an aircraft model according to a flight control system of the aircraft; constructing a fault detection filter and a fault weighting system; the method comprises the steps of amplifying and integrating to obtain an overall dynamic system for fault detection; constructing a Lyapunov-Krasovskii function to give progressive stabilization conditions of the overall dynamic system; and solving a filter matrix corresponding to the fault detection filter by using an LMI tool box and a congruence transformation method, and judging whether the flight control system has faults or not according to comparison of a residual error evaluation function and a threshold value. According to the scheme, the performance of the fault detection system is improved by adopting a weighted fault signal method, and the residual signal is robust to disturbance by introducing the weighted fault signal system, so that the sensitivity and the robustness of fault detection are improved.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of data identification, in particular to a fault detection method of a flight control system based on a filter.
Background
Currently, flight control systems are an integral part of the equipment onboard an aircraft, which are susceptible to failure as aircraft architecture becomes increasingly complex and control tasks become increasingly burdensome. At present, common faults comprise defect, loosening and locking of rudder wings, and proper countermeasures are adopted after the faults occur, so that accidents can be effectively avoided. However, these countermeasures are all required to be completed within a few seconds, and if the faults cannot be found timely, catastrophic accidents may occur, so that it is extremely important to detect the faults of the flight control system timely.
In order to avoid the occurrence of flight accidents and improve the safety and reliability of a flight control system, a fault detection system needs to be established, and whether the system fails or not is judged by detecting system information. And (3) immediately giving early warning when the fault starts, and taking countermeasures by pilots in time so as to prevent the occurrence of flight accidents. The fault diagnosis method of the existing flight control system is mainly divided into a model-based fault detection method and a knowledge-based fault detection method. The fault detection method based on the model can fully utilize the internal information of the system and can effectively detect the system fault; the knowledge-based fault detection method is too dependent on historical fault diagnosis experience, experience knowledge is difficult to acquire, and faults which are not found are easy to diagnose and fail, so that the method is limited in application in the flight control system.
The flight control system has the characteristics of complex structure, multiple state quantities and the like, and can generate uncertain factors such as interference, noise and the like in the operation process, thereby influencing the performance of the fault diagnosis system and reducing the sensitivity of the system.
It can be seen that there is a need for a fault detection method for a flight control system that is sensitive to faults and robust to external disturbances.
Disclosure of Invention
In view of the above, embodiments of the present disclosure provide a method for detecting faults of a flight control system based on a filter, which at least partially solves the problems of poor sensitivity and robustness in the prior art.
The embodiment of the disclosure provides a fault detection method of a filter-based flight control system, which comprises the following steps:
step 1, building an aircraft model according to a flight control system of the aircraft;
step 2, constructing a fault detection filter and a fault weighting system;
step 3, obtaining an overall dynamic system of fault detection by augmentation and integration according to the aircraft model, the fault detection filter and the fault weighting system;
step 4, constructing Lyapunov-Krasovskii function to give that the overall dynamic system satisfies H ∞ Progressive stabilization conditions for performance indicators;
and 5, solving a filter matrix corresponding to the fault detection filter by using an LMI tool box and a congruence transformation method, and judging whether the flight control system has faults or not according to comparison of an evaluation function of a residual signal output by the fault detection filter and a threshold value.
According to a specific implementation of an embodiment of the disclosure, the expression of the aircraft model isWhere x (t) represents a system state vector, u (t) represents an input vector, w (t) represents an external disturbance input, f (t) represents a fault vector, y (t) represents a measurement output, and a, B, C, G, H represent a known constant matrix.
According to a specific implementation manner of the embodiment of the present disclosure, the step 2 specifically includes:
step 2.1, taking the output of a flight control system as input, taking a residual signal as output and taking transmission delay into consideration to construct the fault detection filter;
and 2.2, selecting an appropriate weighting matrix for the faults to form the fault weighting system. According to a specific implementation manner of the embodiment of the disclosure, the expression of the fault detection filter is thatWherein A is f ,B f ,C f For the fault detection filter parameters to be designed, d (t) is the transmission delay and r (t) is the residual signal.
According to a specific implementation manner of the embodiment of the present disclosure, the step 5 specifically includes:
step 5.1, designing the fault detection filter for the flight control system to generate a residual signal, and solving parameters of the fault detection filter for a given scalar to ensure the gradual stabilization of the augmentation system;
and 5.2, calculating a residual evaluation value according to the residual signal and the residual evaluation function, comparing the residual evaluation value with the threshold value, and determining whether the flight control system has faults or not.
According to a specific implementation manner of the embodiment of the present disclosure, after the step 5, the method further includes:
and forming a residual curve according to the residual signal output by the fault detection filter in a preset period of time and analyzing the moment when the fault of the aircraft occurs according to the residual curve.
The fault detection scheme of the filter-based flight control system in the embodiment of the disclosure comprises the following steps: step 1, building an aircraft model according to a flight control system of the aircraft; step 2, constructing a fault detection filter and a fault weighting system; step 3, obtaining an overall dynamic system of fault detection by augmentation and integration according to the aircraft model, the fault detection filter and the fault weighting system; step 4, constructing Lyapunov-Krasovskii function to give that the overall dynamic system satisfies H ∞ Progressive stabilization conditions for performance indicators; and 5, solving a filter matrix corresponding to the fault detection filter by using an LMI tool box and a congruence transformation method, and judging whether the flight control system has faults or not according to comparison of a residual signal evaluation function and a threshold value.
The beneficial effects of the embodiment of the disclosure are that: according to the scheme, the performance of the fault detection system is improved by adopting the method for weighting the fault signals, and the residual signals are robust to disturbance by introducing the weighted fault signal system, so that the sensitivity and the robustness of fault detection are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
Fig. 1 is a schematic flow chart of a fault detection method of a filter-based flight control system according to an embodiment of the disclosure;
FIG. 2 is a block diagram of a fault detection filter according to an embodiment of the present disclosure;
FIG. 3 is a graph of injected fault signals provided by an embodiment of the present disclosure;
FIG. 4 is a residual signal response diagram of a fault detection filter provided by an embodiment of the present disclosure;
fig. 5 is a graph of residual evaluation functions in both faulty and non-faulty cases provided by embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
Other advantages and effects of the present disclosure will become readily apparent to those skilled in the art from the following disclosure, which describes embodiments of the present disclosure by way of specific examples. It will be apparent that the described embodiments are merely some, but not all embodiments of the present disclosure. The disclosure may be embodied or practiced in other different specific embodiments, and details within the subject specification may be modified or changed from various points of view and applications without departing from the spirit of the disclosure. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the following claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the present disclosure, one skilled in the art will appreciate that one aspect described herein may be implemented independently of any other aspect, and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. In addition, such apparatus may be implemented and/or such methods practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should also be noted that the illustrations provided in the following embodiments merely illustrate the basic concepts of the disclosure by way of illustration, and only the components related to the disclosure are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided in order to provide a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
The embodiment of the disclosure provides a fault detection method of a filter-based flight control system, which can be applied to a fault detection process of the flight control system.
Referring to fig. 1, a flow chart of a fault detection method of a filter-based flight control system according to an embodiment of the disclosure is provided. As shown in fig. 1 and 2, the method mainly comprises the following steps:
step 1, building an aircraft model according to a flight control system of the aircraft;
optionally, the expression of the aircraft model isWhere x (t) represents a system state vector, u (t) represents an input vector, w (t) represents an external disturbance input, f (t) represents a fault vector, y (t) represents a measurement output, and a, B, C, G, H represent a known constant matrix.
In practice, the following equations of motion of the aircraft are considered:
wherein:
v is the total speed of the aircraft, alpha is the attack angle, and beta is the sideslip angle; p, q, r represent yaw rate, pitch rate, roll rate of the aircraft, respectively; g 1 ，g 2 ，g 3 A gravitational component;is an aerodynamic moment; />Is aerodynamic; d is resistance, Y is lateral force, and L is aircraft lift; />Is the aerodynamic coefficient; f (F) T For aircraft thrust, b is the reference span, < ->Average aerodynamic chord length>For aerodynamic pressure, S is the aircraft wing surface area, ρ is the air density, and the moment of inertia component is given by
Γc 2 ＝(I x -I y -I z )-I xz ，Γc 3 ＝I z ，Γc 4 ＝I xz ，Γc 9 ＝I x 。I x 、I y 、I z 、I xz Is the moment of inertia.
The aircraft model can be described in terms of a velocity component V using a linear time-invariant system x 、V y 、V z And q, r P is the system state, delta hl 、δ hr 、δ al 、δ ar 、δ r Is a system control input which represents the left and right elevator angles, the left and right aileron angles, and rudder angles, respectively. The aircraft has external interference in the flight process, and the state equation of the system when the influence of fault signals is considered is as follows:
wherein A is a state matrix, B is an actuator matrix, G is an interference matrix, H is a fault matrix, y (t) is a system measurement output, w (t) is a system bounded external input disturbance, and f (t) is a system fault as shown in FIG. 3.
Step 2, constructing a fault detection filter and a fault weighting system;
further, the step 2 specifically includes:
step 2.1, taking the output of a flight control system as input, taking a residual signal as output and taking transmission delay into consideration to construct the fault detection filter;
and 2.2, selecting an appropriate weighting matrix for the faults to form the fault weighting system.
Optionally, the expression of the fault detection filter isWherein A is f ,B f ,C f For the fault detection filter parameters to be designed, d (t) is the transmission delay and r (t) is the residual signal.
In particular implementation, the specific construction steps may be as follows:
(1) The fault detection filter is constructed with the system output y (t) as the input to the filter, and because the communication connection between the system output and the filter is not perfect, there will be a signal propagation delay, assuming that propagation delay d (t) is bounded, i.eFirst a fault detection filter of the form
Wherein x is f (t) is the state vector of the fault detection filter, r (t) is the residual signal, A f ,B f ,C f Is the filter parameter to be designed.
(2) Fault weighting system
To enhance the performance of the fault detection system, an appropriate weight matrix equation is chosen for f(s), i.e., f w (s) =w(s) f(s), where f(s), f w (s) are f (t), f w Laplace transform of (t), f w (s) =w(s) f(s) into the following state space model:
wherein x is w (t) is a state vector, A w ,B w ,C w Is given a matrix known.
Meanwhile, it should be noted that the problem of designing the fault detection filter can be divided into two steps:
the first step: the system (2) is designed with a filter (3) to produce a residual signal, and the filter parameter A is determined for a given scalar gamma > 0 f ,B f ,C f Ensuring that the augmentation system (4) is asymptotically stable and that, under zero initial conditions, the residual error e (t) satisfies the following properties:
and a second step of: after generating the residual signal, calculating a residual evaluation value r|by a residual evaluation function T And compares it with a predefined threshold value J th Comparison is performed:
in fault detection, the residual evaluation function and the threshold value satisfy the following relation
||r|| T ≥J th Has faults of
||r|| T ＜J th No fault
Step 3, obtaining an overall dynamic system of fault detection by augmentation and integration according to the aircraft model, the fault detection filter and the fault weighting system;
in particular, an estimation error is definedThe combined system (3) (4) augmentation system (2) obtains an overall dynamic detection system of faults:
in the middle of
Step 4, constructing Lyapunov-Krasovskii function to give that the overall dynamic system satisfies H ∞ Progressive stabilization conditions for performance indicators;
in particular, givenA gamma > 0, assuming a fault detection filter matrix A f ,B f ,C f It is known that the presence matrix P > 0, Q.gtoreq.0, Z > 0, N > 0, constructs the Lyapunov-Krasovskii function as:
derived from V (t)
The inequality is added on both sidesThe method comprises the following steps:
wherein the method comprises the steps ofK＝[0 I 0]＝[K 1 0]
N＝[N 1 T N 2 T N 3 T ] T ，
N 1 ＝[N 11 T N 12 T ] T ，τ＝τ 1 +Ξ+Ξ T +τ 2 τ 2 T +τ 3 T hK T ZKτ 3 ，
To make forτ < 0, derived from Schur's complement:
wherein,Ξ＝[NK N 0]，
Π＝τ 1 +Ξ+Ξ T ，
if inequality (10) holds, system (5) is progressively stable and the residual signal meets performance index (6).
And 5, solving a filter matrix corresponding to the fault detection filter by using an LMI tool box and a congruence transformation method, and judging whether the flight control system has faults or not according to comparison of an evaluation function of a residual signal output by the fault detection filter and a threshold value.
Further, the step 5 specifically includes:
step 5.1, designing the fault detection filter for the flight control system to generate a residual signal, giving out conditions for guaranteeing progressive stability of the augmentation system according to Lyapunov stability theorem, and solving parameters of the fault detection filter by using an LMI tool kit;
and 5.2, calculating a residual evaluation value according to the residual signal and the residual evaluation function, comparing the residual evaluation value with the threshold value, and determining whether the flight control system has faults or not.
In practice, the initial aim is to determine the filter matrix A f ,B f ,C f Based on the stability analysis to solve the design problem of the fault detection filter, the parameters of the fault detection filter in (3) are given to ensure that the system (5) meeting the performance (6) is asymptotically stable, and J is used 2 And J 2 T The method (10) comprises the following steps:
for a given constant γ > 0, if there is a matrix Q.gtoreq.0, Z > 0, R > 0,S > 0,n satisfies the following LMI
Then a fault detection filter of form (3) is present, so that the system (5) is progressively stable and meets the performance (6).
Wherein,R＝P 1 ，/> K 1 ＝[0 I]
further, if the above condition is satisfied, the parameter matrix in the failure detection filter (3):
and calculating a residual evaluation value according to the residual signal and the residual evaluation function, comparing the residual evaluation value with a threshold value, and determining whether the flight control system has faults or not.
Optionally, after the step 5, the method further includes:
and forming a residual curve according to the residual signal output by the fault detection filter in a preset period of time and analyzing the moment when the fault of the aircraft occurs according to the residual curve.
In the implementation, in order to facilitate the subsequent analysis of the flight data of the aircraft, a residual curve may be formed according to the residual signal output by the fault detection filter in the preset period, and then the residual curve is stored, so that the subsequent analysis of the moment when the aircraft fault occurs may be performed accordingly.
According to the fault detection method of the filter-based flight control system, provided by the embodiment, the performance of the fault detection system is improved by adopting a method of weighting fault signals, and the residual signals are made to have robustness to disturbance by introducing the weighted fault signal system, and meanwhile, H is guaranteed ∞ The performance index is sensitive to faults, meanwhile, the expression of expected filter parameters is directly obtained through matrix decomposition, congruence transformation and other technologies, the operation process is simple, the moment of faults of the aircraft can be intuitively observed through a residual curve, and the sensitivity and the robustness of fault detection are improved.
The following will describe the present solution in connection with a specific embodiment, the system state parameters of an aircraft at a altitude of 500m and a flight speed of 153m/s are as follows:
G＝[0.0481 -0.9568 0.0046 0 0 0] T ；H＝[0 0 0 1 1 1] T
because the continuous work of the actuator in the running process of the aircraft easily causes the mechanical jam and causes the jam fault of the actuator, the fault signal considered by the invention is the jam fault, and the following formula is shown in the specification
The interference of the system in the simulation is white noise, and a fault weighting system (4) matrix is selected as follows: a is that w ＝-0.4，B w ＝0.25，C W ＝-0.05。
By solving the LMI (11), a matrix can be obtainedS and satisfies γ= 0.3015 in the performance (6), and then (12) obtains a filter matrix a f ,B f ,C f The simulation results are shown in fig. 4 and 5, which show the residual response and the residual evaluation function response of the system when the fault of formula (13) is introduced, respectively, and as can be seen from fig. 4, when the fault occurs for 70s, the sudden increase of the residual signal is no longer near 0, which illustrates the sensitive identification of the residual signal to the fault. As can be seen from fig. 5, when a fault occurs, the residual evaluation function value is significantly larger than that in the absence of the fault, and thus a system fault is effectively detected.
In summary, the present invention provides a method based on H ∞ A fault detection method for a flight control system of a filter. Firstly, building an aircraft model, and constructing a fault detection filter on the basis of the aircraft model to ensure that an error system is gradually stable and can meet the specified H ∞ Performance requirements; secondly, using Lyapunov stability theorem to give out the existence condition of the designed fault detection filter, and using a linear matrix inequality tool box to solve the parameters of the fault detection filter; finally, simulation results show that the designed fault detection filter can ensure the sensitivity of residual signals to faults and the robustness of external interference, and effectively improve the accuracy of fault detection.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.
The foregoing is merely specific embodiments of the disclosure, but the protection scope of the disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the disclosure are intended to be covered by the protection scope of the disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (2)
1. A method for fault detection in a filter-based flight control system, comprising:
step 1, building an aircraft model according to a flight control system of the aircraft, wherein the expression of the aircraft model is as followsWherein x (t) represents a system state vector, u (t) represents an input vector, w (t) represents an external disturbance input, f (t) represents a fault vector, y (t) represents a measurement output, and a, B, C, G, H represent a known constant matrix;
step 2, constructing a fault detection filter and a fault weighting system;
the step 2 specifically includes:
step 2.1, taking the output of the flight control system as the input, taking the residual signal as the output and taking the transmission delay into consideration to construct the fault detection filter, wherein the expression of the fault detection filter is as followsWherein A is f ,B f ,C f D (t) is transmission delay, and r (t) is residual signal;
step 2.2, selecting a proper weighting matrix for the faults to form the fault weighting system, wherein the expression of the fault weighting system is as follows
Wherein x is w (t) is a state vector, A w ,B w ,C w To know a given matrix, f w (t) is f w Inverse Laplace transform of(s) with relation f w (s)＝W(s)f(s)；
Step 3, obtaining an overall dynamic system of fault detection by augmentation and integration according to the aircraft model, the fault detection filter and the fault weighting system;
the step 3 specifically includes:
defining an estimation errorCombining the fault detection filter and the state space model, augmenting the aircraft model to obtain an overall dynamic system for fault detection:
in the middle ofθ(t)＝[u T (t) w T (t) f T (t)] T
Step 4, constructing Lyapunov-Krasovskii function to give that the overall dynamic system satisfies H ∞ Progressive stabilization conditions for performance indicators;
the step 4 specifically includes:
given a gamma > 0, assume a fault detection filter matrix A f ,B f ,C f It is known that the presence matrix P > 0, Q.gtoreq.0, Z > 0, N > 0, constructs the Lyapunov-Krasovskii function as:
derived from V (t)
Both sides of the inequalityAdding e T (t)e(t)-γ 2 θ T (t) θ (t) is:
wherein the method comprises the steps ofK＝[0 I 0]＝[K 1 0]
φ(t)＝[ξ T (t) ξ T (t-d(t))K T θ T (t)] T ，N＝[N 1 T N 2 T N 3 T ] T ，
N 1 ＝[N 11 T N 12 T ] T ，τ＝τ 1 +Ξ+Ξ T +τ 2 τ 2 T +τ 3 T hK T ZKτ 3 ，
To make forτ < 0, derived from Schur's complement:
wherein,Ξ＝[NK N 0]，
Π＝τ 1 +Ξ+Ξ T ，
if the inequality of the Schur complement theory is established, the overall dynamic system of fault detection is progressively stable and the residual signal meets the performance index of the residual error;
step 5, solving a filter matrix corresponding to the fault detection filter by using an LMI tool box and a congruence transformation method, and judging whether the flight control system has faults or not according to comparison of an evaluation function of residual signals output by the fault detection filter and a threshold value;
the step 5 specifically includes:
step 5.1, designing the fault detection filter for the flight control system to generate a residual signal, giving out conditions for guaranteeing progressive stability of the augmentation system according to Lyapunov stability theorem, and solving parameters of the fault detection filter by using an LMI tool kit;
and 5.2, calculating a residual evaluation value according to the residual signal and the residual evaluation function, comparing the residual evaluation value with the threshold value, and determining whether the flight control system has faults or not.
2. The method according to claim 1, wherein after step 5, the method further comprises:
and forming a residual curve according to the residual signal output by the fault detection filter in a preset period of time and analyzing the moment when the fault of the aircraft occurs according to the residual curve.
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