CN119758961B

CN119758961B - A distributed fuzzy fault detection method for transmission-related multi-unmanned vessel systems

Info

Publication number: CN119758961B
Application number: CN202411903185.7A
Authority: CN
Inventors: 龙跃; 孙钰; 李铁山; 杨寒卿; 李耀仑
Original assignee: University of Electronic Science and Technology of China
Current assignee: University of Electronic Science and Technology of China
Priority date: 2024-12-20
Filing date: 2024-12-23
Publication date: 2025-09-23
Anticipated expiration: 2044-12-23
Also published as: CN119758961A

Abstract

The purpose of the present invention is to provide a distributed fuzzy fault detection method related to the transmission of multiple unmanned ship systems, which belongs to the field of ship control technology. The fault detection method is aimed at multiple unmanned ship systems where actuator failures may occur, and takes into account the interference caused by external factors, and establishes a T-S fuzzy model of the unmanned ship; then the communication topology and transmission delay between the unmanned ships are considered, and a special Lyapunov function is introduced, thereby introducing more variable matrices, reducing the conservatism of solving matrix inequalities, that is, increasing the possibility of finding a solution, ensuring the stability and effectiveness of the fault detection system, dealing with the problem that the matrix inequality caused by the coupling terms brought about by the information transmission between the unmanned ships is difficult to solve, and establishing a distributed fault detection judgment mechanism. The method of the present invention comprehensively considers the internal nonlinearity of the unmanned ship and the interference of the environment in which it is located, and realizes the detection of faults under the adverse influence of complex working environment and long working time.

Description

Distributed fuzzy fault detection method related to multi-unmanned ship system transmission

Technical Field

The invention belongs to the technical field of ship control, and particularly relates to a distributed fuzzy fault detection method related to multi-unmanned ship system transmission.

Background

Inspired by many interesting clustering behaviors in nature (such as fish gathering and bird migration), multi-intelligent systems emerge with the rapid development of communication technologies and automation technologies. Unmanned ship is an important tool for exploring ocean resources, and naturally becomes one of important research objects of multiple intelligent agents. However, since unmanned ships are easily affected by severe natural conditions and long-time work, failures are easily generated when tasks are performed.

From the existing research results, the related technical scheme mainly focuses on the directions of fault tolerance and invasion control methods ^[1][2] and the like of unmanned ships, and researches related to fault detection are few. Of course, fault detection schemes based on fuzzy systems exist today, such as the problem of fault detection filtering of nonlinear dynamic systems under the T-S fuzzy framework discussed in document ^[3][4], but these studies are only directed to a single agent. However, in the process of cooperation of a plurality of unmanned ships, if one or even a plurality of unmanned ships fail, failure information is likely to be transmitted to unmanned ships which do not fail, so that if unmanned ship systems fail and are not found and removed in time, system failure is likely to be caused, tasks fail, and huge economic losses are caused. In addition, since the unmanned ships receive information from neighbor nodes, the state information between the unmanned ships generates a coupling item, so that the fault detection method of the single unmanned ship is not suitable for distributed fault detection. Therefore, the research on the fault detection algorithm of the multi-unmanned ship system is of great significance.

[1] Coke aerospace unmanned ship group decision and control research [ D ]. University of Dalian maritime, 2022.DOI:10.26989/d.cnki.gdlhu.2022.000667.

[2] Zhang fault tolerant control based on fuzzy logic system for unmanned ship with actuator failure [ J ]. Ind. University of Shenyang university (Nature science edition), 2020,38 (03): 214-219.

[3]Y.Wen,X.Ye and X.Su.Event-Triggered Fault Detection Filtering of Fuzzy-Mod el-Based Systems With Prescribed Performance.IEEE Transactions on Fuzzy Systems,pp.4336-4347.

[4]Q.Liu,Y.Long,T.Li,J.H.Park and C.L.P.Chen,"Fault Detection for Unman ned Marine Vehicles Under Replay Attack,"in IEEE Transactions on Fuzzy Systems,pp.1716-1728.

Disclosure of Invention

Aiming at the problems existing in the background technology, the invention aims to provide a distributed fuzzy fault detection method related to the transmission of a multi-unmanned ship system. The fault detection method aims at a multi-unmanned ship system with the possibility of actuator faults, considers interference caused by external factors such as wind, waves and the like, establishes a T-S fuzzy model of the unmanned ship, then considers communication topology and transmission delay between the unmanned ships, introduces a special Lyapunov function, introduces more variable matrixes, reduces the conservation of matrix inequality solution, namely increases the possibility of finding solutions, ensures the stability and the effectiveness of the fault detection system, solves the problem that matrix inequality caused by coupling terms caused by information transfer between the unmanned ships is difficult to solve, and establishes a distributed fault detection judgment mechanism. The method comprehensively considers the internal nonlinearity of the unmanned ship and the interference of the environment, and realizes the detection of faults under the adverse effects of complex working environment, long-time working and the like.

In order to achieve the above purpose, the technical scheme of the invention is as follows:

a distributed fuzzy fault detection method related to multi-unmanned ship system transmission comprises the following steps:

Step 1, determining a dynamic model of a single unmanned ship by combining the actual running condition of the unmanned ship, wherein the dynamic model comprises a kinematic equation and a dynamic equation;

step 2, determining a state space model according to the dynamics model of the single unmanned ship in the step1, taking the communication topological structure and the transmission delay between unmanned ships into consideration, and obtaining a state equation and a measurement equation of the unmanned ship under a T-S fuzzy system through a fuzzy modeling method;

step 3, constructing a corresponding distributed fault detection filter according to the state equation of the single unmanned ship under the T-S fuzzy system obtained in the step 2, and obtaining residual errors;

Step 4, introducing a known fault weight matrix for improving the performance of the distributed fault detection method;

Substituting the unmanned ship measurement output equation into a distributed fault detection filter equation, and taking the communication topological structure between unmanned ships into consideration to obtain a global fault detection system equation;

Step 6, designing a solving and calculating method of the filter gain, solving and obtaining a gain matrix of the fault detection filter constructed in the step 3, and enabling a global fault detection system equation to have a specified H _∞ performance on disturbance;

Step 7, designing a residual evaluation function J _r (t) according to the gain matrix of the fault detection filter obtained in the step 6 and the residual constructed in the step 3;

and 8, according to actual requirements, based on the residual evaluation function obtained in the step 6, a threshold and an alarm strategy are formulated, namely, if the residual evaluation function value obtained by real-time detection is larger than the preset residual evaluation function threshold, alarm is given, and if not, the alarm is not given, so that fault detection is completed.

Further, the specific process of the step 1 is as follows:

the dynamics model of the mth unmanned ship comprises a kinematics equation and a dynamics equation, specifically,

Kinematic equation:

kinetic equation:

Wherein phi _m (t) is the position information of the unmanned ship under the earth coordinate system, As course angle information, v _m (t) is ship self information, the matrix M _m,N_m,Z_m respectively represents an inertial matrix, a damping matrix and a mooring force matrix of the ship, mu _m (t) is a control signal generated by a controller, d _m (t) is an interference signal caused by external factors such as environment and the like,And (3) expressing derivation, wherein J (·) is a transformation matrix from the unmanned ship body coordinate system to the ground coordinate system.

Further, in step 1, the position information Φ _m (t) of the unmanned ship includes coordinate information (x _mp(t),y_mp (t)) and heading angle information The ship self information v _m (t) includes the heave speed v _m1 (t), the roll speed v _m2 (t) and the yaw speed v _m3(t),v_m(t)＝col{v_m2(t),v_m2(t),v_m2 (t) of the unmanned ship.

Further, the specific process of the step 2 is as follows:

Order the The kinetic equation can be expressed as:

wherein E _1m is a known fault coefficient matrix, and f _m (t) is a fault signal generated on the unmanned ship;

The state space model of the unmanned ship system comprises a state equation and a measurement output equation, which are respectively:

y_m(t)＝C_mx_m(t)

Wherein C _m is a coefficient matrix, a _m、D_m and E _m are augmentation matrices;

obtaining a fuzzy model of the unmanned ship through a fuzzy modeling method:

Plant Rule i:If p_m1(δ_m(t))isand p_m2(δ_m(t))isTHEN

Wherein i is the ith fuzzy subsystem, d _m (t) is external interference, τ _mn (t) represents the delay of information transmission from the nth unmanned ship to the mth unmanned ship, the upper bound is h, the matrix A _mi,E_mi,D_mi,K_mn is a known coefficient matrix, y (t) is a measurement equation of the system, For the collection of all neighbor unmanned ships of the mth unmanned ship,The filter state on the neighbor node of unmanned ship m;

the state equation and the measurement equation of the global T-S fuzzy system of the unmanned ship are:

Wherein ρ _i(δ_m (t)) is a membership function corresponding to different fuzzy rules, λ _i is a membership function after normalization processing, and δ _m (t) is a foresight variable of the fuzzy rules.

Further, in the step2,I.e. delta _m (t) membership functionSum is 1, and

Further, the specific form of the distributed fuzzy fault detection filter constructed in the step 3 is as follows:

Wherein, the R _m (t) is the state vector, measurement output vector and residual signal of the distributed fuzzy fault detection filter,Normalized membership function for fault detection filterFor the gain matrix of the fault detection filter to be designed, l _mn is an element in the adjacency matrix, j is a sequence number of the filter fuzzy rule, and s is the number of the filter fuzzy rule.

Further, in step 4, a known failure weight matrix is introduced, such that,

Wherein a _wm,B_wm,C_wm is a known matrix.

Further, the global fault detection system equation constructed in step 5 is:

Wherein, the

Is the Cronecker product, and the addition is the Hadamard product.

Further, the specific process of step 6 is as follows:

(1) Based on the design principle of stability and robustness to disturbance, the error augmentation system is required to be ensured to be gradually stable and have specified H _∞ performance gamma, so that the following formula is established for the disturbance signal omega (t),

Gamma is a performance index for measuring disturbance attenuation capability, and an upper corner mark T represents a turn rank;

(2) When the gamma distributed fuzzy fault detection filter with H _∞ performance exists, the matrix containing the gain information is directly obtained through the formulated solving condition And a correlation matrix L ₂, obtaining a gain matrix of the fault detection filter by the following operation

Further, in step 7, the specific form of the residual evaluation function R _m(r_m (t)) is:

Wherein t is the detection duration, and t ₀ is the initial time.

Further, the specific process of step 8 fault detection is as follows:

setting a threshold form of a residual evaluation function:

the distributed fuzzy fault detection strategy of the multi-unmanned ship system comprises the steps of detecting the obtained residual evaluation function value in real time, alarming if the residual evaluation function value is larger than the threshold value of the residual evaluation function, otherwise, not alarming, wherein the expression is as follows:

in summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:

In the system modeling aspect, the method for detecting the faults utilizes the T-S fuzzy system to process nonlinear items in the coordinate transformation matrix, considers the communication topological structure between unmanned ships and unavoidable delay phenomenon during information transmission, develops a multi-unmanned ship system under the T-S fuzzy model frame, and simultaneously considers external interference in the system safety aspect, and the designed distributed fuzzy fault detection filter can detect the faults of the current node and the executors of the unmanned ships of the neighbor nodes.

Drawings

Fig. 1 is a flow chart of a fault detection method of the present invention.

Fig. 2 is a topology of an unmanned ship.

Fig. 3 is a residual signal generated by four filters.

Fig. 4 is a residual evaluation function corresponding to four residual signals.

Detailed Description

The present invention will be described in further detail with reference to the embodiments and the accompanying drawings, for the purpose of making the objects, technical solutions and advantages of the present invention more apparent.

The invention discloses a distributed fuzzy fault detection method related to multi-unmanned ship system transmission, wherein a flow chart of the method is shown in figure 1, and the method specifically comprises the following steps:

step 1, the kinetic model of the mth unmanned ship is as follows, and specifically comprises a kinematic equation and a kinetic equation,

Kinematic equation:

kinetic equation:

Wherein phi _m (t) is the position information of the unmanned ship under the earth coordinate system, As course angle information, v _m (t) is ship self information, and the matrix M _m,N_m,Z_m represents an inertia matrix, a damping matrix and a mooring force matrix of the ship respectively. The positional information phi _m (t) of the unmanned ship includes coordinate information (x _mp(t),y_mp (t)) and heading angle information The ship self information v _m (t) includes the heave speed v _m1 (t), the roll speed v _m2 (t) and the yaw speed v _m3(t),v_m(t)＝col{v_m2(t),v_m2(t),v_m2 (t) of the unmanned ship.

Step 2, orderThe kinetic equation can be expressed as:

Wherein E _m is a known failure coefficient matrix;

Further, a state equation and a measurement output equation of the unmanned ship system are obtained:

y_m(t)＝C_mx_m(t)

Wherein, the

Obtaining a fuzzy model of the unmanned ship through a fuzzy modeling method:

Plant Rule i:If p_m1(δ_m(t))isand p_m2(δ_m(t))isTHEN

wherein i is an ith fuzzy subsystem, d _m (t) is external interference, τ _mn (t) represents delay of information transmission from an nth unmanned ship to an mth unmanned ship, the upper bound of the delay is h, and a matrix A _mi,E_mi,D_mi,K_mn is a known coefficient matrix;

wherein ρ _i(δ_m (t)) is a corresponding membership function under different fuzzy rules, I.e. delta _m (t) membership functionSum is 1, and

The T-S fuzzy system is introduced to approach nonlinearity in the unmanned ship system by using a plurality of linear subsystems so as to process the unmanned ship system by using a method for processing the linear system, and the nonlinear terms in the coordinate transformation matrix are processed by using a sector nonlinearity method so as to obtain a specific expression form of a membership function, wherein only lambda _i(δ_m (T)) ∈0 and lambda _i(δ_m (T)) ∈0 are considered in the subsequent analysisThe solution of the filter gain problem in the later step will not involve the true value of the specific membership function;

And 3, constructing a specific form of the distributed fuzzy fault detection filter:

Wherein, the R _m (t) is the state vector, measurement output vector and residual signal of the distributed fuzzy fault detection filter,Normalized membership function for fault detection filterA gain matrix for a fault detection filter to be designed;

step 4, introducing a fault weight matrix to improve the performance of the fault detection system, wherein the specific form is as follows:

Wherein a _wm,B_wm,C_wm is a known matrix;

Selecting different parameters in the step can enable the residual error to have different performances, selecting proper parameters can enable the constructed residual error to have sensitivity to faults and robustness to interference, and is generally selected according to the experience of an expert;

step 5, comprehensively considering the unmanned ship model state equation, the filter dynamic equation and the fault weight matrix to obtain a global fault detection system equation, wherein the specific form is as follows:

Wherein, the

It can be seen that the distributed multi-unmanned ship system produces compared to a single unmanned ship systemThe coupling terms with adjacency matrix information are equal, which brings great difficulty to solving the constructed matrix inequality in the step 6, different decoupling modes are adopted in the subsequent process, the matrix P is decomposed into p=diag { L ₁,L₂, T },

Step 6, designing a solving and calculating method of the filter gain, solving and obtaining a gain matrix of the fault detection filter constructed in the step 4, and enabling the residual error constructed in the step 5 to have robustness to disturbance and sensitivity to faults, wherein the specific process is as follows:

(2) When the gamma distributed fuzzy fault detection filter with H _∞ performance exists, the matrix containing the gain information is directly obtained through the formulated solving conditionAnd a correlation matrix L ₂, obtaining a gain matrix of the fault detection filter by the following operation

Since τ _mn (t) is a time-varying function, the global fault detection system equation can be regarded as an infinite-dimensional system, so that the stability of the system cannot be judged by adopting a conventional Lyapunov stability criterion;

In the invention, the following form of the Liapunov function is adopted as a stability criterion:

If the time-varying time-lag τ _mn (t) is not considered, the Lyapunov function can be chosen as

The sufficient conditions for finding the gain conditions of a distributed fuzzy fault detection filter that a fault detection filter with H _∞ performance gamma exists are that if there is a suitable symmetry matrix L ₁＞0,L₂＞0,T＞0,Q₁＞0,Q₂＞0,R₁＞0,R₂ >0, Z >0, X >0 and several scalar gamma >0, 0≤τ (t). Ltoreq.h, the resulting distributed fault detection system is progressively stable when the following conditions are met and has an H _∞ performance index gamma:

ψ₁₂₁＝R₁-S₁-P₁+P₂

ψ₇₇＝h^-2(R₂-2L₂)

Π₃₃＝-R₁-Q₁,Π₄₄＝-R₂-Q₂,Π₅₅＝-γ²I

ψ₅₆＝[D E]^T(R₁+Z),ψ₆₆＝-h^-2(R₁+Z),ψ₈₈=-I

Directly obtaining a matrix containing gain information of the matrix through formulated solving conditions And a correlation matrix L ₂, obtaining a gain matrix of the fault detection filter by the following operation

For a single system, the variables to be solved of the matrix inequality are all 6-dimensional matrices, but for a multi-agent system, the matrices are 6×n-dimensional, and the elements in each matrix to be solved are unknown variables, so after the dimensions of the matrix to be solved are increased, the number of the unknown variables is multiplied, so that the realization of a distributed fault detection scheme is more difficult than the realization of a single unmanned ship fault detection scheme, and the matrix P is further required to have a special structure;

And 7, designing a residual evaluation function R _m(r_m (t)) according to the gain matrix of the fault detection filter obtained in the step 6 and the residual constructed in the step3, wherein the specific form is as follows:

Wherein t is the detection duration, and t ₀ is the initial time;

And 8, formulating a threshold value and an alarm strategy according to actual demands, namely, alarming if a residual evaluation function value obtained by real-time detection is larger than a preset residual evaluation function threshold value, otherwise, not alarming, thereby completing fault detection, wherein the specific process comprises the following steps:

setting a threshold form of a residual evaluation function:

Wherein t is the detection duration;

Therefore, the strategy of fault detection of the networked unmanned ship system is that the residual evaluation function value obtained by real-time detection is larger than the threshold value of the residual evaluation function, and the alarm is given, otherwise, the alarm is not given, and the expression is as follows:

example 1

In the simulation process, 4 unmanned vessels are assumed, the topology structure of the unmanned vessels is shown in fig. 2, each unmanned vessel has the same interference, the form of the interference signal is assumed,

d_m1(t)＝28sin(1.28t)e^(-0.3t),(0<t<6),

d_m2(t)＝-30sin(1.5t)e^(-1.3t),d_m3(t)＝24sin(1.24t)e^(-0.5t),(0<t<5.5)。

Assuming that only the 3 rd unmanned ship fails, the failure signal is f ₃ (t) =20sin (t-14), (14 < t < 20), and the upper bound of the transmission delay is 0.3s.

Fig. 3 shows residual signals generated by 4 filters under fault and fault-free conditions, a dotted line shows a fault-free condition, and a solid line shows a fault condition, and it can be seen that between 14s and 20s, a red solid line has obvious fluctuation compared with a blue dotted line, so that the fault signals have a certain influence on the system.

Fig. 4 shows residual evaluation functions, threshold values, and detection effects corresponding to the 4 filters. It can be seen that filter 1 fails to detect if the system is malfunctioning, filter 2 detects the malfunction at 14.46 seconds, filter 3 detects the malfunction at 14.12 seconds, and filter 4 detects the malfunction at 14.58 seconds. Thus, the system detects the fault and triggers an alarm at 14.12 seconds.

In the foregoing description, only the specific embodiments of the invention have been described, and any features disclosed in this specification may be substituted for other equivalent or alternative features serving a similar purpose, and all the features disclosed, or all the steps in a method or process, except for mutually exclusive features and/or steps, may be combined in any manner.

Claims

1. A distributed fuzzy fault detection method for transmission-related multi-unmanned vessel systems, comprising the following steps:

Step 1: Based on the actual operation of unmanned ships, determine the dynamic model of a single unmanned ship, including kinematic equations and dynamic equations;

Step 2: Based on the dynamic model of a single unmanned ship in step 1, determine the state space model, consider the communication topology and transmission delay between unmanned ships, and use fuzzy modeling methods to obtain the state equation and measurement equation of the unmanned ship under the T-S fuzzy system;

Step 3: Based on the state equation of a single unmanned ship in the T-S fuzzy system obtained in step 2, a corresponding distributed fault detection filter is constructed and the residual is obtained;

Step 4: To improve the performance of the distributed fault detection method, a known fault weight matrix is introduced;

Step 5: Substitute the UAV measurement output equation into the distributed fault detection filter equation and consider the communication topology between UAVs to obtain the global fault detection system equation;

The constructed global fault detection system equation is:

in,

v(t)＝[f ^T (t)d ^T (t)] ^T ,e(t)＝r(t)-f _w (t)

is the Kronecker product, ⊙ is the Hadamard product; represents the derivative, f(t) is the fault signal occurring on the unmanned ship; the subscript i is the i-th fuzzy subsystem, j is the sequence number of the filter fuzzy rule, s is the number of filter fuzzy rules, and m is the m-th unmanned ship; λ _i is the membership function after normalization, δ _m (t) is the foreseeable variable of the fuzzy rule; x(t) is the state vector of the unmanned ship, is the state vector of the distributed fuzzy fault detection filter, r(t) is the residual signal; A _wm , B _wm , C _wm are known matrices; t is the detection time; C _m is the coefficient matrix, A _m , D _m and _Em are augmented matrices; is the gain matrix of the fault detection filter to be designed; the superscript T represents the rank;

Step 6: Design a method to calculate the filter gain, solve the gain matrix of the fault detection filter constructed in step 3, and make the global fault detection system equation have the specified H _∞ performance to the disturbance;

Step 7: Based on the gain matrix of the fault detection filter obtained in step 6 and the residual constructed in step 3, design the residual evaluation function _Jr (t);

Step 8: According to actual needs, based on the residual evaluation function obtained in step 6, a threshold and alarm strategy are formulated. That is, if the residual evaluation function value obtained by real-time detection is greater than the pre-set residual evaluation function threshold, an alarm is issued; otherwise, no alarm is issued, thereby completing fault detection.

2. The distributed fuzzy fault detection method according to claim 1, wherein the specific process of step 1 is as follows:

The dynamic model of the mth unmanned ship includes kinematic equations and dynamic equations, specifically,

Kinematic equations:

Kinetic equation:

Wherein, φ _m (t) is the position information of the unmanned ship in the earth coordinate system, is the heading angle information, _vm (t) is the ship's own information, matrices _Mm , _Nm , _Zm represent the ship's inertia matrix, damping matrix and mooring force matrix respectively, _μm (t) is the control signal generated by the controller, _dm (t) is the interference signal caused by external factors such as the environment, J(·) is the transformation matrix from the unmanned ship's fuselage coordinate system to the ground coordinate system; the position information _φm (t) of the unmanned ship includes coordinate information ( _xmp (t), _ymp (t)) and heading angle information The ship's own information _vm (t) includes the unmanned ship's longitudinal velocity _vm1 (t), transverse velocity _vm2 (t) and bowing velocity _vm3 (t), _vm (t) = col{vm2(t), _vm2 ( _t ), _vm2 (t)}.

3. The distributed fuzzy fault detection method according to claim 2, wherein the specific process of step 2 is as follows:

make Then the kinetic equation can be expressed as:

Among them, E _1m is the known fault coefficient matrix, f _m (t) is the fault signal occurring on the unmanned ship;

The state space model of the unmanned ship system includes the state equation and the measurement output equation, which are:

y _m (t) = C _m x _m (t)

Among them, _Cm is the coefficient matrix, _Am , _Dm and _Em are augmented matrices;

The fuzzy model of the unmanned ship is obtained through the fuzzy modeling method:

Where i is the i-th fuzzy subsystem, d _m (t) is the external interference, τ _mn (t) represents the delay of information transmission from the n-th unmanned ship to the m-th unmanned ship, and its upper bound is h. The matrices A _mi , E _mi , D _mi , and K _mn are all known coefficient matrices. y(t) is the measurement equation of the system. is the set of all neighboring unmanned ships of the mth unmanned ship, is the filter state on the neighboring node of the unmanned ship m;

Then the state equation and measurement equation of the global T-S fuzzy system of the unmanned ship are:

Among them, ρ _i (δ _m (t)) is the membership function corresponding to different fuzzy rules, λ _i is the membership function after normalization, and δ _m (t) is the foreseeable variable of the fuzzy rule.

4. The distributed fuzzy fault detection method according to claim 3, wherein in step 2, That is, the membership function of δ _m (t) The sum of is 1, and

5. The distributed fuzzy fault detection method according to claim 3, wherein the specific form of the distributed fuzzy fault detection filter constructed in step 3 is:

in, r _m (t) is the state vector, measurement output vector and residual signal of the distributed fuzzy fault detection filter, is the normalized membership function of the fault detection filter; the matrix is the gain matrix of the fault detection filter to be designed, l _mn is the element in the adjacency matrix, j is the sequence number of the filter fuzzy rule, and s is the number of filter fuzzy rules.

6. The distributed fuzzy fault detection method according to claim 3, wherein in step 4, a known fault weight matrix is introduced so that:

Among them, A _wm , B _wm , C _wm are known matrices.

7. The distributed fuzzy fault detection method according to claim 6, wherein the specific process of step 6 is:

(1) Based on the design principle of stability and robustness to disturbances, it is necessary to ensure that the error augmentation system is asymptotically stable and has a specified _H∞ performance γ, so that for the disturbance signal ω(t), the following equation holds:

γ is a performance indicator for measuring the disturbance attenuation capability, and the superscript T represents the transition rank;

(2) When a γ-distributed fuzzy fault detection filter with _H∞ performance exists, the matrix containing its gain information can be directly obtained through the formulated solution conditions. And the correlation matrix L ₂ , through the following operation, the gain matrix of the fault detection filter is obtained

8. The distributed fuzzy fault detection method according to claim 1, wherein the residual evaluation function R _m (r _m (t)) in step 7 is specifically in the form of:

Among them, t is the detection time and _t0 is the initial time.

9. The distributed fuzzy fault detection method according to claim 1, wherein the specific process of the fault detection in step 8 is as follows:

Set the threshold form of the residual evaluation function:

The strategy of distributed fuzzy fault detection for multi-unmanned ship systems is: if the residual evaluation function value obtained by real-time detection is greater than the threshold of the residual evaluation function, an alarm is issued; otherwise, no alarm is issued.