CN108681240B - Fault diagnosis method for unmanned aerial vehicle distributed formation based on unknown input observer - Google Patents

Fault diagnosis method for unmanned aerial vehicle distributed formation based on unknown input observer Download PDF

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CN108681240B
CN108681240B CN201810206806.4A CN201810206806A CN108681240B CN 108681240 B CN108681240 B CN 108681240B CN 201810206806 A CN201810206806 A CN 201810206806A CN 108681240 B CN108681240 B CN 108681240B
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unmanned aerial
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杨蒲
董艳
许梦洋
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Nanjing University of Aeronautics and Astronautics
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Nanjing University of Aeronautics and Astronautics
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Abstract

The invention provides a method for realizing distributed formation fault diagnosis of a small unmanned aerial vehicle based on an unknown input observer. According to the hierarchical concept of the distributed control system, the design of controllers for single unmanned aerial vehicles and multi-unmanned aerial vehicle formation is respectively researched, and the flight stability of the distributed formation of the small unmanned aerial vehicles is ensured. When a fault occurs in a single unmanned aerial vehicle actuator, an effective fault detection method for a distributed system of an unknown input observer is provided, interference is thoroughly decoupled, and adverse effects of external interference on a fault diagnosis process are thoroughly eliminated. Then, a method of expanding the state vector and the fault vector into one augmented vector is proposed to estimate the fault. The invention well realizes the fault detection, separation and estimation of the distributed formation of the small unmanned aerial vehicle by combining the traditional unknown input observer and the augmentation system. According to the known parameters of the model of the small unmanned aerial vehicle, a numerical simulation can be established to carry out fault diagnosis on the actuator. The method is used for distributed formation fault diagnosis of the small unmanned aerial vehicle.

Description

Fault diagnosis method for unmanned aerial vehicle distributed formation based on unknown input observer
Technical Field
The invention relates to a small unmanned aerial vehicle formation fault diagnosis method based on unknown input observer interference decoupling, and belongs to the technical field of formation systems.
Background
In recent decades, unmanned aerial vehicle technology has developed relatively mature and is widely used in military and civil fields. The unmanned aerial vehicle formation flying technology is more and more concerned by people as one of the core concepts. Therefore, in practical applications, both attacks or damages from the outside world and failures of the internal actuators of the system may seriously affect the performance of the system. In order to prevent or reduce the influence that external disturbance caused the system, make the effective safe operation of whole unmanned aerial vehicle formation, effectual fault diagnosis method is indispensable. In a distributed system, a centralized fault diagnosis method is restricted by information transmission among all unmanned aerial vehicles, and the defects of the centralized method can be overcome by using the distributed fault diagnosis method.
Traditional formation of unmanned aerial vehicles can be roughly divided into leader followers, behavior-based modes, distributed virtual structures, and the like. The formation mode adopted by the invention is that on the basis of the traditional pilot following formation mode, the basic two-airplane formation is taken as a unit, and the large-scale airplane formation is divided into a plurality of two-airplane formation according to the concept of hierarchy. And the formation control of the large-scale cluster is realized through distributed control. Compared with the traditional piloting mode, the method reduces the burden of the piloting unmanned aerial vehicle controller on continuous processing and transmission of a large amount of data, and improves the calculation efficiency. Meanwhile, when the piloting unmanned aerial vehicle breaks down, the whole formation cluster can not be in a chaotic state due to target loss. Because the advantage of this formation is that the sensor system and control system of each drone in the formation fleet are identical. The result of this is that the trajectory tracking controller of the drone can switch between the desired trajectory (the flight trajectory of the entire formation fleet) and the forward trajectory (the flight trajectory of the drone in the previous position), and is relatively well adapted to emergency situations, and the host can be replaced and the formation can be adjusted on line by designing a control algorithm. If one unmanned aerial vehicle breaks down in the flying process, the unmanned aerial vehicles which are close to each other in the cluster can continue to execute the flying task.
In a distributed unmanned aerial vehicle formation system, the problem of fault diagnosis of the distributed system is challenging because a centralized fault diagnosis method is constrained by limited computing capability and communication bandwidth of a single computing node, the complex structure of the system, time delay of signal transmission, the complicated coupling relationship among all links of the system and the like. To solve this problem, the method can overcome the disadvantages of the centralized method. A distributed fault diagnosis method is provided on the basis of an unknown input observer. The diagnosis method not only presents robustness to unknown input, but also only needs to utilize information obtained by the subsystem, thereby greatly improving the detection efficiency.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the prior art, a method for realizing distributed formation fault diagnosis of small unmanned aerial vehicles based on unknown input observers is provided, and when a single unmanned aerial vehicle actuator has a fault, an effective method for detecting the fault of the distributed system of the unknown input observers is provided, so that the interference is thoroughly decoupled, and the adverse effect of external interference on the fault diagnosis process is thoroughly eliminated. Then, a method of expanding the state vector and the fault vector into one augmented vector is proposed to estimate the fault.
The technical scheme is as follows: a method for realizing distributed formation fault diagnosis of a small unmanned aerial vehicle based on an unknown input observer is characterized by comprising the following steps: according to the hierarchical concept of the distributed control system, the design of controllers for single unmanned aerial vehicles and multi-unmanned aerial vehicle formation is respectively researched, and the flight stability of the distributed formation of the small unmanned aerial vehicles is ensured. When a fault occurs in a single unmanned aerial vehicle actuator, an effective fault detection method for a distributed system of an unknown input observer is provided, interference is thoroughly decoupled, and adverse effects of external interference on a fault diagnosis process are thoroughly eliminated. Then, a method for expanding the state vector and the fault vector into an augmented vector is provided to estimate the fault, and the method comprises the following specific steps:
step 1) adopting a distributed control strategy: dividing a large-scale unmanned aerial vehicle formation into a plurality of two-machine formations according to a hierarchy concept, wherein each unit formation is closely connected, and finally multi-machine formation is realized; considering a kinematic model in the process of formation of two machines, the equations are listed according to the instantaneous position and speed vectors of the chairman and the bureaucratic machines marked in the reference coordinate system:
Figure GSB0000190773930000021
step 2) linearizing the flight control system of the nonlinear small unmanned aerial vehicle to obtain a state equation and an output equation of each flight control system of the small unmanned aerial vehicle, establishing a system model with actuator faults, and expressing the ith intelligent system model as:
Figure GSB0000190773930000022
wherein the state variable is mi＝[vi αi qi θi Hi βi φi pi ri ψi]T∈R10The parameters are respectively forward speed, attack angle, pitch angle speed, pitch angle, altitude, sideslip angle, roll angle speed, yaw angle speed and yaw angle, and the single-machine input variable at the bottom layer is deltai＝[δie δiT δia δir]∈R4Elevator, throttle, aileron and flap are indicated, respectively. Gamma rayi(t) is process and actuator failure,
Figure GSB0000190773930000023
representing interference and noise. Mij(mj) The information that is the information that ith unmanned aerial vehicle and jth unmanned aerial vehicle passed each other.
And 3) after the decoupling modeling of the longitudinal direction and the transverse direction is realized, respectively designing a longitudinal controller and a transverse lateral controller according to different relations between longitudinal variables and transverse variables.
Step 3.1) longitudinal control of height and speed. The control law expression of the height maintenance control system is as follows:
Figure GSB0000190773930000024
wherein the content of the first and second substances,
Figure GSB0000190773930000025
ΔH＝Hg-H-kHh is the height deviation signal.
The speed error signal includes two parts, one is the difference between the desired speed and the actual speed output, and the other is the speed differential signal. The expression form of the control law is as follows:
Figure GSB0000190773930000026
wherein Δ V ═ Vg-V。
The lateral direction is controlled by yaw. The lateral force is generated by rolling the airplane, so that the airplane generates yaw, and the expression form of the control law of the airplane is as follows:
Figure GSB0000190773930000027
wherein, delta phi is phig-φ-kpp，Δ(ψ-β)＝ψg-ψ-kψψ-(βg-β-kββ)。
Note: the following table shows the values of the desired variables for g, which are known.
Step 3.2) when the tracking control of a wing plane to a farm plane is designed, the control of the three aspects of the wing planes in the formation is mainly completed by utilizing the three-channel control of x, y and z. Wherein the forward distance, the lateral distance and the height can be paired
Figure GSB0000190773930000031
And
Figure GSB0000190773930000032
the control of the device achieves the aims of formation maintenance and transformation. The form of the control law is as follows:
Figure GSB0000190773930000033
Figure GSB0000190773930000034
Figure GSB0000190773930000035
wherein the content of the first and second substances,
Figure GSB0000190773930000036
and 4) taking the formation model and the single-machine model of the formation of the small unmanned aerial vehicles with interference and actuator faults into consideration on the basis of stabilizing the formation controller through the step 2) and the step 3). After the state vector and the fault vector of the aircraft flight control system are expanded into an augmentation vector, the ith single-machine system model is expressed as:
Figure GSB0000190773930000037
defining an augmentation variable:
Figure GSB0000190773930000038
Figure GSB0000190773930000039
the following can be obtained:
Figure GSB00001907739300000310
and step 5) when the fault of a single unmanned aerial vehicle actuator occurs, an effective fault detection method for the distributed system of the unknown input observer is provided, interference is thoroughly decoupled, and adverse effects of external interference on a fault diagnosis process are thoroughly eliminated. In fault diagnosis, for the case of unknown input, the corresponding observer is designed as follows:
Figure GSB00001907739300000311
wherein the content of the first and second substances,
Figure GSB00001907739300000312
and
Figure GSB00001907739300000313
respectively observed values of the state variable and the output variable,
Figure GSB00001907739300000314
is the value of the fault estimate.
Figure GSB0000190773930000041
Wherein K is to be designed.
Definition of
Figure GSB0000190773930000042
Then
Figure GSB0000190773930000043
Because of the fact that
Figure GSB0000190773930000044
Carry-in type
Figure GSB0000190773930000045
The following can be obtained:
Figure GSB0000190773930000046
Figure GSB0000190773930000047
according to
Figure GSB0000190773930000048
Then it can be obtained:
Figure GSB0000190773930000049
from theorem 1: assuming that a symmetric matrix P exists such that matrices R and Q are positive, matrix R:
R＝-(NTP+PN)＞0
wherein R is a symmetric array.
The matrix Q is a matrix of a number of,
Figure GSB00001907739300000410
the observer error can be shown to asymptotically converge to zero.
Step 6) according to a group of gain matrixes of the formation fault diagnosis observer of the unmanned aerial vehicles based on the unknown input observer, the method can rewrite a fault estimation algorithm as follows:
Figure GSB0000190773930000051
has the advantages that: according to the method for realizing the fault diagnosis of the distributed formation of the small unmanned aerial vehicles based on the unknown input observer, the design of controllers for single unmanned aerial vehicles and multiple unmanned aerial vehicles are respectively researched according to the hierarchical concept of a distributed control system, and the flight stability of the distributed formation of the small unmanned aerial vehicles is ensured. When a fault occurs in a single unmanned aerial vehicle actuator, an effective fault detection method for a distributed system of an unknown input observer is provided, interference is thoroughly decoupled, and adverse effects of external interference on a fault diagnosis process are thoroughly eliminated. Then, a method of expanding the state vector and the fault vector into one augmented vector is proposed to estimate the fault. Has the following specific advantages:
firstly, the method is based on an unknown input observer, fully considers the coupling relation of systems in a distributed unmanned aerial vehicle formation system, thoroughly decouples interference, and eliminates the adverse effect of external interference on the fault diagnosis process;
compared with other formation methods, the method reduces the burden of a piloting unmanned aerial vehicle controller on continuous processing and transmission of a large amount of data and improves the calculation efficiency;
the invention combines the unknown input observer and the augmentation system, and can realize the whole process of fault detection, isolation and estimation, thereby ensuring that the fault diagnosis algorithm has stronger robustness and is relatively simple and clear.
The fault diagnosis method of the distributed formation system provided by the invention is an improved method for interference decoupling, fault detection, isolation and estimation, has certain application significance, is easy to implement, has good real-time performance, can effectively improve the safety of the control system, has strong operability, and can be widely applied to the research and analysis of fault diagnosis of the formation of the small unmanned aerial vehicle.
Drawings
Fig. 1 is an undirected graph of a distributed control drone formation flight control system of the method of the present invention;
FIGS. 2-6 are graphs of state error curves for drones 1-5;
fig. 7 is a state error curve of the drone 1 with actuator failure;
FIG. 8 is an actual value versus an error value for an actuator fault;
Detailed Description
The invention is further explained below with reference to the drawings.
The invention provides a method for realizing distributed formation fault diagnosis of a small unmanned aerial vehicle based on an unknown input observer, and provides an effective fault detection method of a distributed system of the unknown input observer when a single unmanned aerial vehicle actuator has a fault, so that interference is thoroughly decoupled, and adverse effects of external interference on a fault diagnosis process are thoroughly eliminated. Then, a method of expanding the state vector and the fault vector into one augmented vector is proposed to estimate the fault.
Step 1) adopting a distributed control strategy: dividing a large-scale unmanned aerial vehicle formation into a plurality of two-machine formations according to a hierarchy concept, wherein each unit formation is closely connected, and finally multi-machine formation is realized; considering a kinematic model in the process of formation of two machines, the equations are listed according to the instantaneous position and speed vectors of the chairman and the bureaucratic machines marked in the reference coordinate system:
Figure GSB0000190773930000052
step 2) linearizing the flight control system of the nonlinear small unmanned aerial vehicle to obtain a state equation and an output equation of each flight control system of the small unmanned aerial vehicle, establishing a system model with actuator faults, and expressing the ith intelligent system model as:
Figure GSB0000190773930000061
wherein the state variable is mi＝[vi αi qi θi Hi βi φi pi ri ψi]T∈R10The parameters are respectively forward speed, attack angle, pitch angle speed, pitch angle, altitude, sideslip angle, roll angle speed, yaw angle speed and yaw angle, and the single-machine input variable at the bottom layer is deltai＝[δie δiT δia δir]∈R4Elevator, throttle, aileron and flap are indicated, respectively. Gamma rayi(t) is process and actuator failure,
Figure GSB0000190773930000062
representing interference and noise. Mij(mj) The information that is the information that ith unmanned aerial vehicle and jth unmanned aerial vehicle passed each other.
And 3) after the decoupling modeling of the longitudinal direction and the transverse direction is realized, respectively designing a longitudinal controller and a transverse lateral controller according to different relations between longitudinal variables and transverse variables.
Step 3.1) longitudinal control of height and speed. The control law expression of the height maintenance control system is as follows:
Figure GSB0000190773930000063
wherein the content of the first and second substances,
Figure GSB0000190773930000064
ΔH＝Hg-H-kHh is the height deviation signal.
The speed error signal includes two parts, one is the difference between the desired speed and the actual speed output, and the other is the speed differential signal. The expression form of the control law is as follows:
Figure GSB0000190773930000065
wherein Δ V ═ Vg-V。
The lateral direction is controlled by yaw. The aircraft rolls to generate lateral force so that the aircraft can yaw, and the expression form of the control law is as follows:
Figure GSB0000190773930000066
wherein, delta phi is phig-φ-kpp，Δ(ψ-β)＝ψg-ψ-kψψ-(βg-β-kββ)。
Note: the following table shows the values of the desired variables for g, which are known.
Step 3.2) when the tracking control of a wing plane to a farm plane is designed, the control of the three aspects of the wing planes in the formation is mainly completed by utilizing the three-channel control of x, y and z. Wherein the forward distance, the lateral distance and the height can be paired
Figure GSB0000190773930000067
And
Figure GSB0000190773930000068
the control of the device achieves the aims of formation maintenance and transformation. The form of the control law is as follows:
Figure GSB0000190773930000069
Figure GSB00001907739300000610
Figure GSB00001907739300000611
wherein the content of the first and second substances,
Figure GSB00001907739300000612
and 4) taking the formation model and the single-machine model of the formation of the small unmanned aerial vehicles with interference and actuator faults into consideration on the basis of stabilizing the formation controller through the step 2) and the step 3). After the state vector and the fault vector of the aircraft flight control system are expanded into an augmentation vector, the ith single-machine system model is expressed as:
Figure GSB0000190773930000071
defining an augmentation variable:
Figure GSB0000190773930000072
Figure GSB0000190773930000073
the following can be obtained:
Figure GSB0000190773930000074
the variable matrix of the single-frame unmanned aerial vehicle model is as follows:
Figure GSB0000190773930000075
C＝[C1 C2]，F＝[F1 F2]，
Figure GSB0000190773930000076
Figure GSB0000190773930000077
Figure GSB0000190773930000078
Figure GSB0000190773930000079
Figure GSB00001907739300000710
and step 5) when the fault of a single unmanned aerial vehicle actuator occurs, an effective fault detection method for the distributed system of the unknown input observer is provided, interference is thoroughly decoupled, and adverse effects of external interference on a fault diagnosis process are thoroughly eliminated. In fault diagnosis, for the case of unknown input, the corresponding observer is designed as follows:
Figure GSB0000190773930000081
1. observer with unknown design
Figure GSB0000190773930000082
Wherein τ is 2.
2. According to the inequality
Figure GSB0000190773930000083
Figure GSB0000190773930000084
Figure GSB0000190773930000085
Then it is available
Figure GSB0000190773930000086
Figure GSB0000190773930000087
Figure GSB0000190773930000088
Figure GSB0000190773930000089
Figure GSB0000190773930000091
According to Y ═ P-1Y1，K＝P-1K1, and
Figure GSB0000190773930000092
to obtain
Figure GSB0000190773930000093
3. It is verified whether the matrix P is such that the matrix R, Q meets the requirements.
Figure GSB0000190773930000094
Figure GSB0000190773930000095
Figure GSB0000190773930000096
Since both R and Q are positive definite matrices, P is valid.
Step 6) according to a group of gain matrixes of the formation fault diagnosis observer of the unmanned aerial vehicles based on the unknown input observer, the method can rewrite a fault estimation algorithm as follows:
Figure GSB0000190773930000097
initial value of unmanned aerial vehicle formation system:
m1(0)＝[0.1 0.1 -0.15]T，m2(0)＝[0.2 0 -0.1]T，m3(0)＝[0.2 0.1 -0.2]T，
m4(0)＝[0.15 0.15 -0.3]T，m5(0)＝[0.1 0 -0.02]T。
initial value of unknown input observer:
z1(0)＝[0.3 0.3 -0.3]T，z2(0)＝[0.3 0.3 -0.3]T，z3(0)＝[0.3 0.3 -0.3]T，z4(0)＝[0.3 0.3 -0.3]T，z5(0)＝[0.3 0.3 -0.3]T。
for a faultless unmanned aerial vehicle formation system, error curves of speed, altitude and angular speed are shown in fig. 2, 3, 4, 5 and 6, and the error is finally stabilized at 0.
When the time t is 20s, an actuator fault is added in the simulation experiment, the state error of the unmanned aerial vehicle 1 does not approach to 0, and the simulation result shows that the unmanned aerial vehicle 1 with the fault is as shown in fig. 7. Because the unmanned aerial vehicle formation adopts a bidirectional transmission distributed control strategy, when a single actuator breaks down, the broken-down unmanned aerial vehicle observer can well diagnose the fault.
The fault detection and separation method not only detects and separates faults of the actuator, but also estimates the faults, and the fault estimation curve of the actuator is shown in figure 8.

Claims (1)

1. A method for realizing distributed formation fault diagnosis of a small unmanned aerial vehicle based on an unknown input observer is characterized by comprising the following steps: according to the hierarchical concept of the distributed control system, the design of controllers for single unmanned aerial vehicles and multi-unmanned aerial vehicle formation is respectively researched, and the flight stability of the distributed formation of the small unmanned aerial vehicles is ensured; when a fault occurs in a single unmanned aerial vehicle actuator, an effective fault detection method for a distributed system of an unknown input observer is provided, interference is thoroughly decoupled, and adverse effects of external interference on a fault diagnosis process are thoroughly eliminated; then, a method for expanding the state vector and the fault vector into an augmented vector is provided to estimate the fault, and the method comprises the following specific steps:
step 1) adopting a distributed control strategy: dividing a large-scale unmanned aerial vehicle formation into a plurality of two-machine formations according to a hierarchy concept, wherein each unit formation is closely connected, and finally multi-machine formation is realized; considering a kinematic model in the process of formation of two machines, the equations are listed according to the instantaneous position and speed vectors of the chairman and the bureaucratic machines marked in the reference coordinate system:
Figure FSB0000190773920000011
step 2) linearizing the flight control system of the nonlinear small unmanned aerial vehicle to obtain a state equation and an output equation of each flight control system of the small unmanned aerial vehicle, establishing a system model with actuator faults, and expressing the ith intelligent system model as:
Figure FSB0000190773920000012
wherein the state variable is mi＝[vi αi qi θi Hi βi φi pi ri ψi]T∈R10The parameters are respectively forward speed, attack angle, pitch angle speed, pitch angle, altitude, sideslip angle, roll angle speed, yaw angle speed and yaw angle, and the single-machine input variable at the bottom layer is deltai＝[δie δiT δia δir]∈R4Respectively elevator, throttle, aileron and flap, gammai(t) is process and actuator failure,
Figure FSB0000190773920000013
representing interference and noise, Mij(mj) Information which is mutually transmitted between the ith unmanned aerial vehicle and the jth unmanned aerial vehicle;
step 3) after the decoupling modeling of the longitudinal direction and the transverse direction is realized, respectively designing a longitudinal controller and a transverse lateral controller according to different relations between longitudinal variables and transverse variables;
step 3.1) longitudinally controlling the height and the speed; the control law expression of the height maintenance control system is as follows:
Figure FSB0000190773920000014
wherein the content of the first and second substances,
Figure FSB0000190773920000015
ΔH＝Hg-H-kHH is the height deviation signal;
the speed error signal comprises two parts, one part is the difference value between the expected speed and the actual speed output, the other part is a speed differential signal, and the expression form of the control law is as follows:
Figure FSB0000190773920000016
wherein Δ V ═ Vg-V；
The lateral direction is controlled by yaw, the airplane rolls to generate lateral force to enable the airplane to yaw, and the expression form of the control law is as follows:
Figure FSB0000190773920000021
wherein, delta phi is phig-φ-kpp，Δ(ψ-β)＝ψg-ψ-kψψ-(βg-β-kββ)；
Subscript g is the value of the known desired variable;
step 3.2) in the tracking control of the wing plane to the farm plane, the control of the three aspects of the wing plane in formation is mainly accomplished by using the three channels x, y and z, wherein the forward distance, the lateral distance and the height can be controlled by the pair of the wing plane in formation
Figure FSB0000190773920000022
And
Figure FSB0000190773920000023
the control of the method achieves the aims of maintaining and transforming formation, and the form of the control law is as follows:
Figure FSB0000190773920000024
Figure FSB0000190773920000025
Figure FSB0000190773920000026
wherein the content of the first and second substances,
Figure FSB0000190773920000027
step 4) through the step 2) and the step 3), on the basis of stabilizing the formation controller, taking the formation model and the single-machine model of the formation of the small unmanned aerial vehicles with interference and actuator faults into consideration; after the state vector and the fault vector of the aircraft flight control system are expanded into an augmentation vector, the ith single-machine system model is expressed as:
Figure FSB0000190773920000028
defining an augmentation variable:
Figure FSB0000190773920000029
the following can be obtained:
Figure FSB00001907739200000210
step 5) when a single unmanned aerial vehicle actuator fails, an effective unknown input observer distributed system fault detection method is provided, interference is thoroughly decoupled, adverse effects of external interference on a fault diagnosis process are thoroughly eliminated, and in fault diagnosis, corresponding observers are designed according to the condition of unknown input:
Figure FSB00001907739200000211
wherein the content of the first and second substances,
Figure FSB0000190773920000031
and
Figure FSB0000190773920000032
respectively observed values of the state variable and the output variable,
Figure FSB0000190773920000033
is the value of the fault estimate;
Figure FSB0000190773920000034
wherein K is required to be designed;
definition of
Figure FSB0000190773920000035
Then
Figure FSB0000190773920000036
Because of the fact that
Figure FSB0000190773920000037
Carry-in type
Figure FSB0000190773920000038
The following can be obtained:
Figure FSB0000190773920000039
according to
Figure FSB00001907739200000310
Figure FSB00001907739200000311
Then it can be obtained:
Figure FSB00001907739200000312
from theorem 1: assuming that a symmetric matrix P exists such that matrices R and Q are positive, matrix R:
R＝-(NTP+PN)＞0
wherein R is a symmetric array;
the matrix Q is a matrix of a number of,
Figure FSB0000190773920000041
the observer error can be proven to converge asymptotically to zero;
step 6) according to a group of gain matrixes of the formation fault diagnosis observer of the unmanned aerial vehicles based on the unknown input observer, the method can rewrite a fault estimation algorithm as follows:
Figure FSB0000190773920000042
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