CN108667673A - Fault detection method for nonlinear networked control systems based on event-triggered mechanism - Google Patents

Abstract

The invention provides a fault detection method for a nonlinear network control system based on an event trigger mechanism, and relates to the technical field of network system fault detection. This method first establishes the T-S fuzzy model of the nonlinear network control system, sets the event trigger conditions, establishes the fuzzy fault detection filter model, establishes the fault weighting system, and then establishes the fault detection system model; and according to the fault detection system model, selects the appropriate The residual evaluation function and detection threshold of the nonlinear network control system are used to detect whether the fault occurs in the nonlinear network control system. Finally, according to the sufficient conditions for the stability of the fault detection system and the existence of the fault detection filter, the parameter matrix and event trigger matrix of the fault detection filter are further designed. The non-linear network control system fault detection method based on the event trigger mechanism provided by the present invention greatly improves the robustness to external disturbances and communication delays, and the application of the event trigger mechanism can save limited network resources and computing resources.

Description

Fault detection method for nonlinear networked control systems based on event-triggered mechanism

technical field

The invention relates to the technical field of network system fault detection, in particular to a fault detection method for a nonlinear network control system based on an event trigger mechanism.

Background technique

Due to its low cost of installation and maintenance, high security and reliability, and flexible communication structure, networked control systems have gained widespread attention in complex industrial control systems. In a networked control system, sensors, actuators, and controllers are interconnected through a shared communication network. With the increasing requirements of networked control systems for security, stability, and high performance, the problem of fault detection for networked control systems has become an important research field.

Due to the introduction of the communication network and the characteristics of the network control system itself, it inevitably brings new problems and challenges to the network control system, such as communication delay, data packet loss, data out-of-sequence, and limited bandwidth. At present, most of the research results of network control systems are the design methods of controllers and filters for the problems of time delay, packet loss, and disorder of the system, but there are relatively few fault diagnosis problems for network control systems. . In addition, most of the research results on fault detection of networked control systems are based on linear systems, but most of the industrial systems and practical systems in life are nonlinear. Therefore, the study of fault detection problems in nonlinear networked control systems It has very important theoretical research value and practical application prospect.

For fault detection problems in nonlinear networked control systems, most of them use time-triggered methods, but in actual work, not all sampling data and measurement outputs need to be transmitted. Therefore, time triggering can easily lead to waste of limited network bandwidth, and further aggravate the occurrence of network-induced delay and data packet loss. In order to reduce the transmission of "unnecessary" data in the network while ensuring the expected system performance, event-triggered communication mechanisms have received extensive attention. The basic idea of the event-triggered strategy is that sampled data is transmitted only when a preset threshold is met. The fault detection of nonlinear networked control system based on the event-triggered communication mechanism can not only detect whether a fault occurs in time and accurately, but also save limited network resources, which is in line with the development trend of fault detection.

Contents of the invention

Aiming at the defects of the prior art, the present invention provides a fault detection method of a nonlinear network control system based on an event trigger mechanism, so as to realize fault detection of the nonlinear network control system.

A fault detection method for a nonlinear networked control system based on an event-triggered mechanism, comprising the following steps:

Step 1, to the nonlinear network control system with process failure, sensor failure and output disturbance, utilize Takagi-Sugeno (i.e. T-S) fuzzy model method to carry out modeling analysis, establish the T-S fuzzy model of this nonlinear network control system;

The fuzzy rules used by the fuzzy model method are as follows:

Rule i: IF z ₁ (t) is M _i1 (z) and...and z _p (t) is M _ip (z), THEN

Among them, i is the number of the fuzzy rule, z(t)=[z ₁ (t), z ₂ (t), ..., z _p (t)] is the antecedent containing the state quantity information in the nonlinear network control system variable, p is the number of antecedent variables, M _ij is a fuzzy set, j=1, 2, ..., p, x(t) is the state variable of the nonlinear network control system, y(t) is the measurement output, ω (t) is the external disturbance, f(t) is the fault signal detected by the sensor, A _i , D _i , F _i , C _i , E _i , G _i are matrices with known appropriate dimensions;

The T-S fuzzy model of the nonlinear networked control system of described establishment is as follows:

Among them, r is the number of fuzzy rules, w _i (z(t)) is the membership function of the nonlinear network control system;

Step 2. Set the event trigger condition, and determine whether the measurement output of the sensor should be transmitted to the filter according to the event trigger condition;

The event trigger conditions for the settings are as follows:

Among them, h is the sampling time interval of the sensor, i _k h is the transmission time of the sensor’s sampling signal, i _k+1 h is the time when the next sampling signal is transmitted, Φ>0 is the weighting matrix that needs to be designed, e _k ( i _k h+jh)=y(i _k h+jh)-y(i _k h) is the threshold error, ε is the event trigger parameter, y(i _k h) is the measurement output of the sensor’s sampling signal at the previous moment, y (i _k h+jh) is the current measurement output of the sensor;

When the sampling signal y(i _k h) was transmitted at the last moment, only when the current sampling signal meets the trigger condition, the sampling signal will be transmitted to the filter;

Step 3, utilizing the T-S fuzzy model method to establish a fuzzy fault detection filter model;

The fuzzy rules used for modeling the fuzzy fault detection filter by using the T-S fuzzy model method are as follows:

Rulej: IF z ₁ (i _k h)is N _j1 and...and z _p (i _k h)is N _jp ，THEN

Among them, x _f (t) is the state vector of the fuzzy fault detection filter, is the real input of the fuzzy filter, z _f (t) is the residual signal, A _fj , B _fj , C _fj and D _fj are the gain matrices of the fuzzy filter to be designed, j is the fuzzy rule number, N _ji is the fuzzy Set, z (i _k h)=[z ₁ (i _k h), z ₂ (i _k h), ..., z _p (i _k h)] is the antecedent variable of the fuzzy fault detection filter;

The model of the fuzzy fault detection filter set up is as follows:

Among them, w _j (z(i _k h)) is the membership function of the fault detection filter;

Step 4. Establish a fault weighting system that can improve the design freedom of the fault detection system;

The fault weighting system is shown in the following formula:

Among them, f(s) is the fault signal, W(s) is the weighting matrix, is the weighted fault signal;

The state space form of fault signal f(s) and weighting matrix W(s) is shown in the following formula:

Among them, x _w (t) is a state space vector, f _w (t) is a weighted fault signal, A _w , B _w , C _w and D _w are constant matrices;

Step 5, establish a fault detection system model according to the T-S fuzzy model of the nonlinear network control system, the event trigger condition, the T-S fuzzy model of the filter and the fault weighting matrix;

The established fault detection system model is shown in the following formula:

in, is the residual error of the fault detection system,

Step 6. Select an appropriate residual evaluation function and detection threshold according to the fault detection system model, and detect whether a nonlinear network control system fault occurs by comparing the numerical values of the residual evaluation function and the detection threshold;

The residual evaluation function H(z _f ) and the detection threshold J _th are shown in the following formulas:

Based on the residual evaluation function and the detection threshold, the following relationship is used to judge whether a fault occurs in the nonlinear networked control system:

When the residual evaluation function is greater than the detection threshold, the nonlinear networked control system has a fault, and the fault detection system alarms; otherwise, the nonlinear networked control system works normally and does not alarm;

Step 7. Construct the fuzzy Lyapuonv function, use the Lyapunov stability theory, related lemmas and linear matrix inequalities to obtain sufficient conditions for the stability of the fault detection system and the existence of the fault detection filter, and further design the parameter matrix A _fj of the fault detection filter, B _fj , C _fj and D _fj and the event trigger matrix Φ.

It can be known from the above technical solution that the beneficial effect of the present invention lies in that the event-triggered mechanism-based nonlinear network control system fault detection method provided by the present invention takes into account process faults and sensor faults during the modeling process of the nonlinear network control system. , and the impact of external disturbances on the networked control system. In the process of fault diagnosis, problems such as communication delay, data packet loss, and data disorder in the network system are considered and solved. The antecedent variables of the fuzzy model of the filter and the fuzzy model of the network control system are asynchronous, which can improve the flexibility of the filter design and reduce the cost; the application of the fuzzy Lyapunov function reduces the conservatism of the system. Compared with the existing technology, using the event-triggered fuzzy H _∞ filter designed by the present invention for fault detection, on the one hand, this technology not only greatly improves the fault sensitivity to the nonlinear network control system, but also is sensitive to external disturbances and data Packet loss is more robust and can effectively solve the problem of fault detection in nonlinear network control systems; on the other hand, the introduction of event-triggered communication mechanisms can effectively reduce the use of network bandwidth and save limited network resources. At the same time, it can save computing resources.

Description of drawings

Fig. 1 is the flow chart of the non-linear network control system fault detection method based on the event triggering mechanism provided by the embodiment of the present invention;

FIG. 2 is a schematic diagram of a residual signal provided by an embodiment of the present invention;

Fig. 3 is a schematic diagram of a residual evaluation function and a detection threshold changing with time according to an embodiment of the present invention;

Fig. 4 is a schematic diagram of the variation of the residual evaluation function with time when there is a fault and when there is no fault according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of the relationship between the release time and the release interval of the event-triggered policy provided by the embodiment of the present invention.

Detailed ways

The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

In this embodiment, the fault detection method of the nonlinear network control system based on the event-triggered mechanism of the present invention is used to detect the fault of the nonlinear network control system.

The fault detection method for nonlinear networked control systems based on event-triggered mechanism, as shown in Figure 1, includes the following steps:

Step 1: For a nonlinear networked control system with process faults, sensor faults and output disturbances, the Takagi-Sugeno (T-S) fuzzy model method is used for modeling and analysis, and the T-S fuzzy model of the nonlinear networked control system is established;

The fuzzy rules used by the fuzzy model method are as follows:

Rule i: IF z ₁ (t) is M _i1 (z) and...and z _p (t) is M _ip (z), THEN

Among them, i is the number of the fuzzy rule, z(t)=[z ₁ (t), z ₂ (t), ..., z _p (t)] is the antecedent containing the state quantity information in the nonlinear network control system variable, p is the number of antecedent variables, M _ij is a fuzzy set, j=1, 2, ..., p, x(t) is the state variable of the nonlinear network control system, y(t) is the measurement output, ω (t) is the external disturbance, f(t) is the fault signal detected by the applied sensor, A _i , D _i , F _i , C _i , E _i , G _i are matrices with known appropriate dimensions;

The T-S fuzzy model of the nonlinear networked control system of described establishment is as follows:

Among them, r is the number of fuzzy rules, w _i (z(t)) is the membership function of the nonlinear network control system;

The TS fuzzy model is a nonlinear network control system model including process faults, sensor faults and external disturbances, which is a more extensive form. fault signal Among them, f _p (t) and f _s (t) represent process fault and sensor fault respectively; define F _i ＝[F _pi 0| and G _i ＝[0 G _si ], where F _pi and G _si are faults coefficient matrix. When F _i =[F _pi 0], G _i =[0 0], the fault signal f(t) in the established TS fuzzy model represents the process fault; when F _i =[0 0], G _i =[0 G _si ], the fault signal f(t) in the established TS fuzzy model represents the sensor fault. The fault signals in this embodiment include sensor faults and process faults.

Step 2: Set the event trigger condition, and determine whether the measurement output of the sensor should be transmitted to the filter according to the event trigger condition;

The set event trigger conditions are as follows:

Among them, h is the sampling interval of the sensor, i _k h is the transmission moment of the sensor’s sampling signal, ik ₊₁ h is the moment when the next sampling signal is transmitted, Φ>0 is the weighting matrix that needs to be designed, e _k (i _k h+jh)=y(i _k h+jh)-y(i _k h) is the threshold error, ε is the event trigger parameter, y(i _k h) is the measurement output of the sensor’s sampling signal at the last moment, y( i _k h+jh) is the current measurement output of the sensor;

When the sampling signal y(i _k h) was transmitted at the last moment, only when the current sampling signal meets the trigger condition, the sampling signal will be transmitted to the filter;

Applying the event trigger condition to the fault detection process, the next trigger moment is not only related to the selected trigger parameters, but also related to the measured output of the sampler transmitted at the last moment. The use of the event trigger mechanism can effectively reduce data transmission and save limited network resources. At the same time, the event trigger condition only needs to calculate the threshold value at each trigger moment, that is, εy ^T (i _k h)Φy(i _k h) is a constant in the time interval (i _k h, i _k+1 h], Therefore, the event trigger mechanism can save computing resources while saving network resources.

Step 3: Considering the influence of the event trigger mechanism and network-induced delay, the fuzzy fault detection filter model is established by using the T-S fuzzy model method;

The fuzzy rules used to model the fuzzy fault detection filter using the T-S fuzzy model method are as follows:

Rule j: IF z ₁ (i _k h)is N _j1 and...and z _p (i _k h)is N _jp ，THEN

Among them, x _f (t) is the state vector of the fuzzy fault detection filter, is the real input of the fuzzy filter, z _f (t) is the residual signal, A _fj , B _fj , C _fj and D _fj are the gain matrices of the fuzzy filter to be designed, j is the fuzzy rule number, N _ji is the fuzzy Set, z (i _k h)=[z ₁ (i _k h), z ₂ (i _k h), ..., z _p (i _k h)] is the antecedent variable of the fuzzy fault detection filter;

The established fuzzy fault detection filter model is as follows:

Among them, w _j (z(i _k h)) is the membership function of the fault detection filter;

The fault detection filter is used to generate the residual signal. The residual signal is very important in the fault detection system. It is used to determine whether there is a fault in the current network control system. Different from the traditional parallel distributed compensation strategy, the antecedent variable z(i _k h) of the fault detection filter is not equal to the antecedent variable z(t) of the NCS. Therefore, the fault detection filter using asynchronous antecedent variables can improve design flexibility and reduce design cost in practical applications.

Step 4: Establish a fault weighting system that can improve the design freedom of the fault detection system;

The fault weighting system is shown in the following formula:

Among them, f(s) is the fault signal, W(s) is the weighting matrix, is the weighted fault signal;

The state space form of fault signal f(s) and weighting matrix W(s) is shown in the following formula:

Among them, x _w (t) is a state space vector, f _w (t) is a weighted fault signal, A _w , B _w , C _w and D _w are known constant matrices;

Step 5, establish a fault detection system model according to the T-S fuzzy model of the nonlinear network control system, the event trigger condition, the T-S fuzzy model of the filter and the fault weighting matrix;

The established fault detection system model is shown in the following formula:

in, is the residual error of the fault detection system,

In this fault detection system, network-induced delays, process faults, sensor faults, and external disturbances are simultaneously included. In practical applications, faults and disturbances are likely to exist at the same time, and network-induced delays are unavoidable during data transmission over the network.

Step 6. Select an appropriate residual evaluation function and detection threshold according to the fault detection system model, and detect whether a nonlinear network control system fault occurs by comparing the numerical values of the residual evaluation function and the detection threshold;

The residual evaluation function H(z _f ) and the detection threshold J _th are shown in the following formulas:

Based on the residual evaluation function and the detection threshold, the following relationship is used to judge whether a fault occurs in the nonlinear networked control system:

When the residual evaluation function is greater than the detection threshold, the nonlinear networked control system has a fault, and the fault detection system alarms; otherwise, the nonlinear networked control system is normal and does not alarm. In the selection process of the detection threshold, the maximum value of the residual evaluation function is selected as the detection threshold under the condition of no fault, that is, when there is only disturbance in the system, no matter how big the disturbance is, the fault detection system will not alarm. In this way, the fault detection system can accurately detect faults in a disturbed environment.

Step 7: Construct the fuzzy Lyapuonv function, use the Lyapunov stability theory, correlation lemma and linear matrix inequality to obtain sufficient conditions for the stability of the fault detection system and the existence of the fault detection filter, and further design the parameters of the fault detection filter and the event trigger matrix ;

Step 7.1: Construct the fuzzy Lyapunov function V(t), and derive the fuzzy Lyapunov function V(t) with respect to time, and obtain the sufficient conditions for the stability of the fault detection system and the existence of the fault detection filter, that is, 1) when When , the filter residual system is asymptotically stable; 2) Under zero initial conditions, the filter residual error signal satisfies where γ>0 is the H _∞ attenuation level,

The constructed fuzzy Lyapunov function is as follows:

in,

For the fault detection problem of nonlinear networked control system, it is assumed that the known positive real number φ _k (k=1,...,r) satisfies Given constants γ, τ ₁ , τ ₃ , ε and known filter gain matrices A _fj , B _fj , C _fj , D _fj , the fault detection system is asymptotically stable and satisfies the H _∞ performance when and Only when there are symmetric matrix Z1 and symmetric positive definite matrix P _k > 0, Φ > 0, M > 0, R _κ > 0, so that the following inequalities hold,

in,

H ₁ =[I0 0], Ξ23=(3-m)(R ₂ -S ₂ ), Ξ ₂₅ =(m-2)(R ₃ -S ₃ ), Ξ ₃₃ =Q ₁ -MR ₁ -R ₂ , Ξ ₃₄ =Q ₃ +(3-m)S ₂ +(m-2)R ₂ , Ξ ₄₄ =Q ₂ -Q ₁ -R ₂ -R ₃ , Ξ ₄₅ =(3-m)R ₃ +(m-2)S ₃ -Q ₃ , Ξ ₅₅ =-Q ₂ -R ₃ , Ξ ₆₆ =(ε-1)Φ, Ξ ₇₇ =-γ ² I, Ξ ₈₈ =-γ ² I, D _1i =[D _i F _i ], E _1i =[E _i G _i ],

Ψ ₂₂ =diag{-R ₁ , -R ₂ , -R ₃ , -εΦ, -I}.

Step 7.2: Design the parameter matrix and event trigger matrix Φ of the fault detection filter;

definition

in,

Then define the inversion matrix as follows:

Multiply the formula (10) by J ₂ =diag{J, I, I, I, I, I, I, I, I, I}, and use Multiply (10) and then define

X _k =U _1k ,

thus get

in,

Υ ₆₆ = (ε-1)Φ, Y ₇₇ = Y ₈₈ = diag{-γ ² I,-γ ² I},

H ₃ =[0 I],

F ₂₂ =diag{-R ₁ -R ₂ -R ₃ -εΦ -I}. A positive real number φ _k (k=1,...,r) satisfies τ ₁ , τ ₃ , ε, h, γ are known constants, Z ₂ is a symmetric matrix, U _1l >0, U _1k >0, X _k >0, V>0, Y>0, M>0, R _k > 0 (k=1, 2, 3) is a symmetric positive definite matrix,

Since U(k)>0, according to Schur's complement lemma,

X _k -Y>0 (13)

According to formula (11), get:

Same as step 7.1, get:

Furthermore, if the conditions (12)-(14) have feasible solutions, the fault detection filter can ensure that the fault detection system is asymptotically stable and satisfies the H _∞ performance;

defined from The mapping operator to z _f (t) is further draw

in

Substituting the matrix in (15) for the corresponding matrix in (11), we get

Therefore, the parameters of the H _∞ fault detection filter are So far, the design scheme of fault detection filter based on event triggering has been completed.

Step 7.3 provides sufficient conditions for the joint design of the event trigger matrix Φ and the fuzzy fault detection filter. There are feasible solutions to the linear matrix inequalities (LMIs) (12)-(14), then the fault detection filter parameters A _fj , B _fj , C _fj , D _fj and event trigger matrix Φ are obtained. Through the LMI toolkit in MATLAB, the linear matrix inequality is solved to obtain the parameters of the fault detection filter and the event trigger matrix.

The parameter setting of the nonlinear network control system adopted in this embodiment is as follows:

C ₁ =[1 0], C ₂ =[1 0], E ₁ =0.5, E ₂ =0.5, G ₁ =0.1, G ₂ =0.1.

The membership function of the fuzzy device is chosen as: w ₂ (z(t))=1-w ₁ (z(t)); The parameters of the fault weighting system are set as: A _w ＝0.1, B _w ＝0.25, C _w ＝0.2, D _w ＝0.65;

The external disturbance signal ω(t) is set as:

Meanwhile, the fault signal looks like this:

Let the sampling interval h=10ms, τ ₁ =0.002, τ ₃ =0.2, φ ₁ =φ ₂ =0.1, ε=0.1. By calculation, the minimum value of H _∞ attenuation level γ=0.6501. Using the LMI toolbox in MATLAB, let γ=1, and get the corresponding event-triggered weight matrix Φ=3.7474. Let the initial state x(0)=[0 0] ^T , x _f (0)=[0 0] ^T , x _w (0)=0, then in this embodiment, the residual signal of the fault detection system is shown in Figure 2 As shown, the change of the residual evaluation function and detection threshold with time is shown in Figure 3. For this fault detection system, the detection threshold The simulation results show that, It can be seen that the fault was detected 0.8s after it occurred.

This embodiment also provides the time-varying diagram of the residual evaluation function when there is a fault and no fault as shown in Figure 4, which proves that the residual signal can not only detect whether a fault occurs, but also distinguish between faults and faults. The impact of disturbances on the system. This embodiment also provides the release time and release interval of the event trigger strategy as shown in Figure 5. It can be seen from the figure that within 40s of the simulation time, the event trigger is only triggered 179 times, compared to the 2000 times of the time trigger times, the number of triggers is significantly reduced. When a fault occurs, event triggering increases significantly, ensuring the reliability of fault detection. It can be seen that the designed fault detection filter can not only detect faults in time, but also effectively reduce the use of limited bandwidth.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still be Modifications are made to the technical solutions described in the foregoing embodiments, or equivalent replacements are made to some or all of the technical features; these modifications or replacements do not make the essence of the corresponding technical solutions depart from the scope defined by the claims of the present invention.

Claims (7)

1. A fault detection method of a nonlinear network control system based on an event trigger mechanism is characterized in that: the method comprises the following steps:

step 1, carrying out modeling analysis on a nonlinear network control system with process faults, sensor faults and output disturbance by using a Takagi-Sugeno (namely T-S) fuzzy model method, and establishing a T-S fuzzy model of the nonlinear network control system;

step 2, setting an event trigger condition, and determining whether the measurement output of the sensor should be transmitted to a filter according to the event trigger condition;

step 3, establishing a fuzzy fault detection filter model by using a T-S fuzzy model method;

step 4, establishing a fault weighting system capable of improving the design freedom of the fault detection system;

step 5, establishing a fault detection system model according to a T-S fuzzy model of the nonlinear network control system, an event trigger condition, a T-S fuzzy model of a filter and a fault weighting matrix;

step 6, selecting a proper residual error evaluation function and a proper detection threshold value according to the fault detection system model, and detecting whether the fault of the nonlinear network control system occurs or not by comparing the values of the residual error evaluation function and the detection threshold value;

when the residual evaluation function is larger than the detection threshold, the nonlinear network control system has a fault, and the fault detection system gives an alarm; otherwise, the nonlinear network control system works normally without alarming;

step 7, constructing a fuzzy Lyapunov function, obtaining sufficient conditions of the stability of the fault detection system and the existence of the fault detection filter by utilizing a Lyapunov stability theory, a related theory and a linear matrix inequality, and further designing a parameter matrix A of the fault detection filter_fj，B_fj，C_fjAnd D_fjAnd an event trigger matrix Φ.

2. The method for detecting the fault of the nonlinear network control system based on the event trigger mechanism according to claim 1, wherein: the fuzzy rule used by the fuzzy model method in step 1 is as follows:

Rule i：IF z₁(t)is M_i1(z)and...and z_p(t)is M_ip(z)，THEN

wherein i is a fuzzy rule number, and z (t) ═ z₁(t)，z₂(t)，...，z_p(t)]For containing state quantity information in nonlinear network control systemVariable, p is the number of the antecedent variables, M_ijIs a fuzzy set, j is 1, 2, …, p, x (t) is a state variable of the nonlinear network control system, y (t) is a measurement output, omega (t) is an external disturbance, f (t) is a fault signal detected by a sensor, A_i，D_i，F_i，C_i，E_i，G_iA matrix of known suitable dimensions;

the established T-S fuzzy model of the nonlinear network control system is as follows:

where r is the number of fuzzy rules, w_i(z (t)) is a membership function of the nonlinear network control system.

3. The method for detecting the fault of the nonlinear network control system based on the event trigger mechanism according to claim 2, wherein: the event trigger conditions set in step 2 are as follows:

where h is the sampling time interval of the sensor, i_kh is the transmission time of the sampling signal of the sensor, i_k+1h is the time at which the next sampled signal is transmitted, > 0 is the weighting matrix that needs to be designed, e_k(i_kh+jh)＝y(i_kh+jh)-y(i_kh) For threshold error, ε is the event trigger parameter, y (i)_kh) The measured output of the sampled signal at a time on the sensor, y (i)_kh + jh) is the current measurement output of the sensor;

sampling the signal y (i) at the previous moment_kh) When transmitted, the sampled signal will be transmitted to the filter only if the current sampled signal satisfies the trigger condition.

4. The method for detecting the fault of the nonlinear network control system based on the event trigger mechanism according to claim 3, wherein: step 3, the fuzzy rule used for modeling the fuzzy fault detection filter by using the T-S fuzzy model method is as follows:

Rulej：IF z₁(i_kh)is N_j1and...and z_p(i_kh)is N_jp，THEN

wherein x is_f(t) is the state vector of the fuzzy fault detection filter,as the true input to the blurring filter, z_f(t) is a residual signal, A_fj，B_fj，C_fjAnd D_fjFor the gain matrix of the fuzzy filter to be designed, j is the fuzzy rule number, N_jiTo blur the set, z (i)_kh)＝[z₁(i_kh)，z₂(i_kh)，...，z_p(i_kh)]Detecting a front-piece variable of a filter for the fuzzy fault;

the model of the established fuzzy fault detection filter is as follows:

wherein, w_j(z(i_kh) Is a membership function of the fault detection filter.

5. The method for detecting the fault of the nonlinear network control system based on the event trigger mechanism according to claim 4, wherein: step 4, the fault weighting system is shown as the following formula:

wherein f(s) is a fault signal, W(s) is a weighting matrix,is a weighted fault signal;

the state space form of the fault signal f(s) and the weighting matrix w(s) is shown as follows:

wherein x is_w(t) is a state space vector, f_w(t) is the weighted fault signal, A_w，B_w，C_wAnd D_wIs a constant matrix.

6. The method for detecting the fault of the nonlinear network control system based on the event trigger mechanism according to claim 5, wherein: the fault detection system model established in the step 5 is shown as the following formula:

wherein,in order to detect the systematic residual error for a fault,ξ(t)＝[ω^T(t) f^T(t)]^T，

7. the method for detecting the fault of the nonlinear network control system based on the event trigger mechanism according to claim 6, wherein: step 6 residual evaluation function H (z)_f) And a detection threshold J_thAs shown in the following equation:

based on the residual evaluation function and the detection threshold, whether the fault of the nonlinear network control system occurs is judged according to the following relation: