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

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

Technical Field

[0001] The present invention belongs to the technical field of ship control, and in particular relates to a distributed fuzzy fault detection method related to transmission of a multi-unmanned ship system. Background Art

[0002] Inspired by many fascinating flocking behaviors in nature, such as fish flocking and bird migration, multi-agent systems have emerged alongside the rapid development of communication and automation technologies. Unmanned vessels, as a crucial tool for exploring marine resources, have naturally become a key research topic for multi-agent systems. However, due to their vulnerability to harsh natural conditions and long operating times, unmanned vessels are prone to malfunctions while performing their missions.

[0003] Judging from the existing research results, the relevant technical solutions mainly focus on the fault-tolerant and intrusion-tolerant control methods of unmanned ships. [1][2] There are few studies related to fault detection. Of course, there are already fault detection schemes based on fuzzy systems, such as the literature [3][4] The problem of fault detection filtering for nonlinear dynamic systems within the TS fuzzy framework was discussed in

[15] , but these studies focused solely on single agents. However, in a collaborative system of multiple unmanned vessels, if one or even several fail, this fault information is likely to be transmitted to unaffected vessels. Therefore, if a failure in the unmanned vessel system is not promptly detected and corrected, it is likely to lead to system failure and mission failure, resulting in significant economic losses. Furthermore, because unmanned vessels receive information from neighboring nodes, the state information between them is coupled, making fault detection methods for a single unmanned vessel unsuitable for distributed fault detection. Therefore, research on fault detection algorithms for multi-unmanned vessel systems is of great significance.

[0004] [1] Jiao Yuhang. Research on decision-making and control of unmanned ship clusters[D]. Dalian Maritime University, 2022. DOI: 10.26989 / d.cnki.gdlhu.2022.000667.

[0005] [2] Zhang Ao. Fault-tolerant control of unmanned ship with actuator failure based on fuzzy logic system[J]. Journal of Shenyang Normal University (Natural Science Edition), 2020, 38(03): 214-219.

[0006] [3]Y.Wen,X.Ye and

[0007] [4] Q. Liu, Y. Long, T. Li, JH Park and CLP Chen, "Fault Detection for Unmanned Marine Vehicles Under Replay Attack," in IEEE Transactions on FuzzySystems, pp.1716-1728. Summary of the Invention

[0008] In view of the problems existing in the background technology, the purpose of the present invention is to provide a distributed fuzzy fault detection method related to the transmission of multiple unmanned ship systems. This 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 such as wind and waves, and establishes a TS 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 environments and long working hours.

[0009] To achieve the above object, the technical solution of the present invention is as follows:

[0010] A distributed fuzzy fault detection method for transmission-related multi-unmanned vessel systems includes the following steps:

[0011] 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;

[0012] 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 TS fuzzy system;

[0013] Step 3: Based on the state equation of a single unmanned ship in the TS fuzzy system obtained in step 2, a corresponding distributed fault detection filter is constructed and the residual is obtained;

[0014] Step 4: To improve the performance of the distributed fault detection method, a known fault weight matrix is ​​introduced;

[0015] 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;

[0016] 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 a specified H for disturbance ∞ performance;

[0017] 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 J r (t);

[0018] 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.

[0019] Furthermore, the specific process of step 1 is:

[0020] The dynamic model of the mth unmanned ship includes kinematic equations and dynamic equations, specifically,

[0021] Kinematic equations:

[0022] Kinetic equation:

[0023] Among them, φ m (t) is the position information of the unmanned ship in the earth coordinate system, is the heading angle information, v m (t) is the ship's own information, matrix M m ,N m ,Z m Represent the ship's inertia matrix, damping matrix and mooring force matrix respectively, μ m (t) is the control signal generated by the controller, d m (t) is the interference signal caused by external factors such as the environment, represents the derivative, and J(·) is the transformation matrix from the UAV body coordinate system to the ground coordinate system.

[0024] Furthermore, in step 1, the position information φ of the unmanned vessel m (t) contains coordinate information (x mp (t),y mp (t)) and heading angle information Ship's own information m (t) includes the longitudinal velocity v of the unmanned ship m1 (t), sway velocity v m2 (t) and the bow speed v m3 (t), v m (t) = col{v m2 (t),v m2 (t),v m2 (t)}.

[0025] Furthermore, the specific process of step 2 is:

[0026] make Then the kinetic equation can be expressed as:

[0027]

[0028] Among them, E 1m is the known failure coefficient matrix, f m (t) is the fault signal occurring on the unmanned ship;

[0029] The state space model of the unmanned ship system includes the state equation and the measurement output equation, which are:

[0030]

[0031] y m (t) = C m x m (t)

[0032] Among them, C m is the coefficient matrix, A m 、D m and E m is the augmented matrix;

[0033]

[0034] The fuzzy model of the unmanned ship is obtained through the fuzzy modeling method:

[0035] Plant Rule i:If p m1 (δ m (t))is and p m2 (δ m(t))is THEN

[0036]

[0037] Among them, i is the i-th 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, and its upper bound is h. The matrix A mi ,E mi ,D mi ,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;

[0038] Then the state equation and measurement equation of the global TS fuzzy system of the unmanned ship are:

[0039]

[0040] Among them, ρ i (δ m (t)) is the membership function corresponding to different fuzzy rules, λ i is the membership function after normalization, δ m (t) is the prerequisite variable of the fuzzy rule.

[0041] Furthermore, in step 2, That is δ m (t) Membership function The sum of is 1, and

[0042] Furthermore, the specific form of the distributed fuzzy fault detection filter constructed in step 3 is:

[0043]

[0044] 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 an element in the adjacency matrix, j is the sequence number of the filter fuzzy rule, and s is the number of filter fuzzy rules.

[0045] Furthermore, in step 4, a known fault weight matrix is ​​introduced so that

[0046]

[0047] Among them, A wm ,B wm ,C wm is a known matrix.

[0048] Furthermore, the global fault detection system equation constructed in step 5 is:

[0049]

[0050] in,

[0051]

[0052] is the Kronecker product, and ⊙ is the Hadamard product.

[0053] Furthermore, the specific process of step 6 is:

[0054] (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 the specified H ∞ Performance γ, so that for the disturbance signal ω(t), the following formula holds:

[0055]

[0056] γ is a performance indicator for measuring the disturbance attenuation capability, and the superscript T represents the transition rank;

[0057] (2) When H ∞ When a γ-distributed fuzzy fault detection filter with good performance exists, the matrix containing its gain information is directly obtained through the formulated solution conditions. And the correlation matrix L2, through the following operation, the gain matrix of the fault detection filter is obtained

[0058]

[0059] Furthermore, the residual evaluation function R in step 7 m (r m (t)) The specific form is:

[0060]

[0061] Among them, t is the detection time and t0 is the initial time.

[0062] Furthermore, the specific process of fault detection in step 8 is as follows:

[0063] Set the threshold form of the residual evaluation function:

[0064]

[0065] 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 triggered; otherwise, no alarm is triggered. Its expression is:

[0066]

[0067] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0068] In terms of system modeling, the fault detection method of the present invention uses the TS fuzzy system to process the nonlinear terms in the coordinate transformation matrix, takes into account the communication topology structure between unmanned ships and the inevitable delay phenomenon during information transmission, and develops a multi-unmanned ship system under the TS fuzzy model framework. At the same time, in terms of system security, external interference is taken into account, and a distributed fuzzy fault detection filter is designed, which can detect actuator failures occurring on the unmanned ships of the current node and neighboring nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 Flowchart of the fault detection method of the present invention.

[0070] Figure 2 The topological structure of the unmanned ship.

[0071] Figure 3 is the residual signal generated by the four filters.

[0072] Figure 4 is the residual evaluation function corresponding to the four residual signals. DETAILED DESCRIPTION

[0073] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the implementation methods and drawings.

[0074] The present invention discloses a distributed fuzzy fault detection method for multi-unmanned ship system transmission related, the flow chart of the method is as follows Figure 1 As shown, the specific steps include:

[0075] Step 1: The dynamic model of the mth unmanned ship is as follows, including the kinematic equation and the dynamic equation.

[0076] Kinematic equations:

[0077] Kinetic equation:

[0078] Among them, φm (t) is the position information of the unmanned ship in the earth coordinate system, is the heading angle information, v m (t) is the ship's own information, matrix M m ,N m ,Z m Represent the inertia matrix, damping matrix and mooring force matrix of the ship respectively. The position information of the unmanned ship φ m (t) contains coordinate information (x mp (t),y mp (t)) and heading angle information Ship's own information m (t) includes the longitudinal velocity v of the unmanned ship m1 (t), sway velocity v m2 (t) and the bow speed v m3 (t), v m (t) = col{v m2 (t),v m2 (t),v m2 (t)}.

[0079] Step 2: Make Then the kinetic equation can be expressed as:

[0080]

[0081] Among them, E m is the known failure coefficient matrix;

[0082] Furthermore, the state equation and measurement output equation of the unmanned ship system are obtained:

[0083]

[0084] y m (t) = C m x m (t)

[0085] in,

[0086]

[0087] The fuzzy model of the unmanned ship is obtained through the fuzzy modeling method:

[0088] Plant Rule i:If p m1 (δ m (t))is and p m2 (δ m (t))is THEN

[0089]

[0090] Among them, i is the i-th 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, and its upper bound is h. The matrix A mi ,E mi ,D mi ,K mn are all known coefficient matrices; y(t) is the measurement equation of the system;

[0091] Then the state equation and measurement equation of the global TS fuzzy system of the unmanned ship are:

[0092]

[0093] Among them, ρ i (δ m (t)) is the membership function corresponding to different fuzzy rules, That is δ m (t) Membership function The sum of is 1, and

[0094] The purpose of introducing the TS fuzzy system is to approximate the nonlinearity in the unmanned ship system with multiple linear subsystems, so as to deal with the unmanned ship system by the method of dealing with linear systems; by using the sector nonlinear method to deal with the nonlinear terms in the coordinate transformation matrix, the specific expression of the membership function is obtained. In the subsequent analysis, only λ is considered. i (δ m (t))≥0 and The nature of the filter gain problem in the subsequent steps will not involve the true value of the specific membership function;

[0095] Step 3: The specific form of the constructed distributed fuzzy fault detection filter is:

[0096]

[0097] 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;

[0098] Step 4: Introduce the fault weight matrix to improve the performance of the fault detection system. Its specific form is:

[0099]

[0100] Among them, A wm ,B wm ,C wm is a known matrix;

[0101] Selecting different parameters in this step will result in different performance of the residual. Selecting appropriate parameters will make the constructed residual sensitive to faults and robust to interference. Generally, the parameters are selected based on the experience of experts.

[0102] Step 5: Comprehensively consider the fuzzy state equation of the unmanned ship, the filter dynamic equation and the fault weight matrix to obtain the global fault detection system equation, which is in the form of:

[0103]

[0104] in,

[0105]

[0106] It can be seen that compared with a single unmanned ship system, the distributed multi-unmanned ship system produces This brings great difficulties to solving the constructed matrix inequality in step 6. In the subsequent process, different decoupling methods are adopted to decompose the matrix P into P = diag{L1, L2, T},

[0107] Step 6: Design a calculation method for the filter gain to obtain the gain matrix of the fault detection filter constructed in step 4, and make the residual constructed in step 5 robust to disturbances and sensitive to faults. The specific process is as follows:

[0108] (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 the specified H ∞ Performance γ, so that for the disturbance signal ω(t), the following formula holds:

[0109]

[0110] (2) When H ∞ When a γ-distributed fuzzy fault detection filter with good performance exists, the matrix containing its gain information is directly obtained through the formulated solution conditions. And the correlation matrix L2, through the following operation, the gain matrix of the fault detection filter is obtained

[0111]

[0112] Since τ mn (t) is a time-varying function, so the above global fault detection system equation can be regarded as an infinite-dimensional system. Therefore, the conventional Lyapunov stability criterion cannot be used to judge the stability of the system;

[0113] The present invention adopts the following form of Lyapunov function as the stability criterion:

[0114]

[0115] If we do not consider the time-varying delay τ mn (t), the Lyapunov function can be chosen as

[0116] Therefore, the distributed fuzzy fault detection filter gain condition is obtained: with H ∞ A sufficient condition for the existence of a fault detection filter with performance γ is: if there exists a suitable symmetric matrix L1>0,L2>0,T>0,Q1>0,Q2>0,R1>0,R2>0,Z>0,X≥0 and several scalars γ>0,0≤τ(t)≤h, then the resulting distributed fault detection system is asymptotically stable and has H if the following conditions are met ∞ Performance indicator γ:

[0117]

[0118]

[0119] ψ 121 =R1-S1-P1+P2

[0120] ψ 77 =h -2 (R2-2L2)

[0121]

[0122] Π 33 =-R1-Q1,Π 44 =-R2-Q2,Π 55 =-γ 2 I

[0123] ψ 56 =[DE] T (R1+Z),ψ 66 =-h -2 (R1+Z),ψ 88 =-I

[0124] By formulating the solution conditions, the matrix containing its gain information is directly obtained And the correlation matrix L2, through the following operation, the gain matrix of the fault detection filter is obtained

[0125]

[0126] For a single system, the variables to be solved in the above matrix inequality are all 6-dimensional matrices. However, for a multi-agent system, the above matrix is ​​6×N-dimensional. Each element in the matrix to be solved is an unknown variable. Therefore, as the dimension of the matrix to be solved increases, the number of unknown variables also increases exponentially, making it more difficult to implement a distributed fault detection solution than to implement a single unmanned ship fault detection solution. This further requires that the matrix P needs to have a special structure.

[0127] 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 R m (r m (t)), the specific form is as follows:

[0128]

[0129] Among them, t is the detection duration, t0 is the initial time;

[0130] Step 8: According to actual needs, formulate thresholds and alarm strategies, that is, if the residual evaluation function value obtained by real-time detection is greater than the preset residual evaluation function threshold, an alarm is triggered; otherwise, no alarm is triggered, thus completing fault detection. The specific process is as follows:

[0131] Set the threshold form of the residual evaluation function:

[0132]

[0133] Where t is the detection time;

[0134] Therefore, the fault detection strategy of the networked unmanned ship system 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 given; otherwise, no alarm is given; its expression is:

[0135]

[0136] Example 1

[0137] This method is used for fault detection. In the simulation process, it is assumed that there are 4 unmanned ships, and the topological structure of the unmanned ships is as follows: Figure 2 As shown, each unmanned ship receives the same interference. Assuming that the interference signal is in the form of,

[0138] dm1 (t) = 28sin(1.28t)e (-0.3t) ,(0<t<6),

[0139] d m2 (t)=-30sin(1.5t)e (-1.3t) ,d m3 (t) = 24sin(1.24t)e (-0.5t) ,(0<t<5.5).

[0140] Assume that only the third unmanned ship fails, the fault signal is: f3(t) = 20sin(t-14), (14<t<20), and the upper bound of the transmission delay is 0.3s.

[0141] Figure 3 Figure 2 is the residual signal generated by the four filters in the presence and absence of faults. The dotted line indicates the absence of faults, and the solid line indicates the presence of faults. It can be seen that between 14s and 20s, the red solid line fluctuates significantly compared to the blue dotted line, indicating that the fault signal will have a certain impact on the system.

[0142] Figure 4 Figure 2 shows the residual error evaluation function, threshold, and detection performance for each of the four filters. It can be seen that the first filter failed to detect a system failure. The second filter detected a failure at 14.46 seconds, the third filter at 14.12 seconds, and the fourth filter at 14.58 seconds. Therefore, the system detected a failure at 14.12 seconds and triggered an alarm.

[0143] The above description is only a specific embodiment of the present invention. Any feature disclosed in this specification, unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes; all disclosed features, or all steps in the methods or processes, except for mutually exclusive features and / or steps, can be combined in any way.

Claims

1. A distributed fuzzy fault detection method for transmission-related multi-unmanned vessel systems, characterized in that: The following steps are involved: 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 TS fuzzy system; Step 3: Based on the state equation of a single unmanned ship in the TS 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 is a known matrix; t is the detection time; C m is the coefficient matrix, A m 、D m and E m is the augmented matrix; 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 a specified H for disturbance ∞ performance; 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 J r (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, characterized in that: The specific process of step 1 is: The dynamic model of the mth unmanned ship includes kinematic equations and dynamic equations, specifically, Kinematic equations: Kinetic equation: Among them, φ m (t) is the position information of the unmanned ship in the earth coordinate system, is the heading angle information, v m (t) is the ship's own information, matrix M m ,N m ,Z m Represent the ship's inertia matrix, damping matrix and mooring force matrix respectively, μ m (t) is the control signal generated by the controller, d m (t) is the interference signal caused by external factors such as the environment, J(·) is the transformation matrix from the UAV body coordinate system to the ground coordinate system; the position information of the UAV φ m (t) contains coordinate information (x mp (t),y mp (t)) and heading angle information Ship's own information m (t) includes the longitudinal velocity v of the unmanned ship m1 (t), sway velocity v m2 (t) and the bow speed v m3 (t), v m (t) = col{v m2 (t),v m2 (t),v m2 (t)}.

3. The distributed fuzzy fault detection method according to claim 2, characterized in that: The specific process of step 2 is: make Then the kinetic equation can be expressed as: Among them, E 1m is the known failure 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, C m is the coefficient matrix, A m 、D m and E m is the augmented matrix; The fuzzy model of the unmanned ship is obtained through the fuzzy modeling method: Among them, i is the i-th 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, and its upper bound is h. The matrix A mi ,E mi ,D mi ,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 TS 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, δ m (t) is the prerequisite variable of the fuzzy rule.

4. The distributed fuzzy fault detection method according to claim 3, characterized in that: In step 2, That is δ m (t) Membership function The sum of is 1, and 5. The distributed fuzzy fault detection method according to claim 3, characterized in that: 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 an 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, characterized in that: In step 4, the known fault weight matrix is ​​introduced so that Among them, A wm ,B wm ,C wm is a known matrix.

7. The distributed fuzzy fault detection method according to claim 6, characterized in that: 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 the specified H ∞ Performance γ, so that for the disturbance signal ω(t), the following formula holds: γ is a performance indicator for measuring the disturbance attenuation capability, and the superscript T represents the transition rank; (2) When H ∞ When a γ-distributed fuzzy fault detection filter with good performance exists, the matrix containing its gain information is directly obtained through the formulated solution conditions. And the correlation matrix L2, 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: Residual evaluation function R in step 7 m (r m (t)) The specific form is: 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 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.

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