Polynomial fuzzy fault estimation observer design method and related equipment

Abstract

The invention provides a polynomial fuzzy fault estimation observer design method and related equipment, and relates to the technical field of fault diagnosis. The design method of the polynomial fuzzy fault estimation observer comprises the following steps: establishing an interval II-type polynomial fuzzy model according to a nonlinear uncertain system; designing a polynomial fuzzy fault estimation observer containing a compensation vector; the polynomial fuzzy fault estimation observer can estimate an actuator fault and a sensor fault at the same time; a compensation vector is determined by solving a stability analysis condition to construct an available polynomial fuzzy fault estimation observer. According to the design method of the polynomial fuzzy fault estimation observer, the fault diagnosis technology of a nonlinear system is perfected, fault diagnosis is achieved under the condition that the forepart variable is unmeasurable, state convergence of an original system is not needed, the application range is wider, and the design method is more beneficial to adapting to complex and changeable task requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and in particular to a polynomial fuzzy fault estimation observer design method and related equipment. Background Art

[0002] With the rapid development of industrial systems, the complexity and nonlinearity of systems are becoming increasingly prominent, and the fault diagnosis technology of nonlinear systems is the core of protecting production safety. Fault estimation, as one of the core tasks of fault diagnosis, can provide the necessary basis for active fault-tolerant control and give fault characteristics and related information. It is a research hotspot in the field of fault diagnosis.

[0003] At present, nonlinear systems are generally processed by TS fuzzy system. However, when the nonlinearity of the system is more complex, the fuzzy system has a dimensional explosion problem due to the large number of fuzzy rules. At the same time, it may suffer from actuator failure and sensor failure, which leads to unmeasurable antecedent variables. The existing processing methods often assume that the antecedent variables can be measured as a premise for fault estimation. Therefore, they are not well applicable to nonlinear systems that suffer from actuator failure and sensor failure at the same time.

[0004] There is currently no effective technical solution to the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a polynomial fuzzy fault estimation observer design method and related equipment, which improves the fault diagnosis technology for nonlinear systems that suffer from actuator faults and sensor faults at the same time, realizes fault diagnosis when the antecedent variables are unmeasurable, does not require the original system state to converge, has a wider scope of application and is more conducive to adapting to complex and changeable task requirements.

[0006] In a first aspect, the present invention provides a polynomial fuzzy fault estimation observer design method, which is applied to a nonlinear uncertain system with both actuator faults and sensor faults, comprising the following steps: S1. Establishing an interval type II polynomial fuzzy model according to the nonlinear uncertain system; S2. Designing a polynomial fuzzy fault estimation observer including a compensation vector; the polynomial fuzzy fault estimation observer is capable of simultaneously estimating the actuator fault and the sensor fault; S3. Based on the interval type II polynomial fuzzy model, the compensation vector is determined by solving the stability analysis condition to construct an available polynomial fuzzy fault estimation observer.

[0007] The polynomial fuzzy fault estimation observer design method provided by the present invention utilizes compensation vectors to compensate for the influence of unmeasurable antecedent variables, meets the requirements of wider and more complex and changeable tasks, and is effectively applicable to nonlinear systems that suffer from actuator faults and sensor faults at the same time.

[0008] Furthermore, the specific steps in step S1 include: S11. Modeling the nonlinear uncertain system and giving fuzzy rules; S12. Obtain the interval type II polynomial fuzzy model according to the fuzzy rules.

[0009] Furthermore, the expression of the interval II type polynomial fuzzy model is: ; in, for The derivative of Reference , for The transposed matrix of for The transposed matrix of is the system state vector, for The transposed matrix of is the first A fuzzy rule, , is the number of fuzzy rules of the interval II type polynomial fuzzy model, is the interval II type polynomial fuzzy model The first membership function of z after the normalization of the fuzzy rules, Reference , is the interval II type polynomial fuzzy model Fuzzy rules about The first system matrix, Reference , is the interval II type polynomial fuzzy model Fuzzy rules about The first input matrix, is the control input, Refers to the actuator failure, Reference , is the interval II type polynomial fuzzy model Fuzzy rules about The second input matrix of external interference, is the identity matrix, Reference , For external interference, for The derivative of To indicate the sensor failure, is the system output, Reference , is the first output matrix, is the second output matrix.

[0010] Furthermore, the expression of the polynomial fuzzy fault estimation observer is: ; in, for The derivative of Reference , for The transposed matrix of for The transposed matrix of for The estimated value of for The transposed matrix of for The estimated value of is the first A fuzzy rule, , is the number of fuzzy rules of the polynomial fuzzy fault estimation observer, is the polynomial fuzzy fault estimation observer The fuzzy rules are normalized The second membership function of is the polynomial fuzzy fault estimation observer Fuzzy rules about The second system matrix, is the polynomial fuzzy fault estimation observer Fuzzy rules about The third input matrix, for The estimated value of is the polynomial fuzzy fault estimation observer Fuzzy rules about The first gain matrix, for Estimated value, is the compensation vector, is the first A fuzzy rule, , is the polynomial fuzzy fault estimation observer The fuzzy rules are normalized The second membership function of is the polynomial fuzzy fault estimation observer Fuzzy rules about The second gain matrix, for The derivative of for The derivative of is the polynomial fuzzy fault estimation observer about The third gain matrix.

[0011] Furthermore, the specific steps in step S3 include: S31. Based on the interval II type polynomial fuzzy model and the polynomial fuzzy fault estimation observer given stability analysis conditions; S32. Solving the constant matrix and variables that satisfy the stability analysis conditions; S33. Obtain the compensation vector according to the constant matrix and the variable.

[0012] Further, the constant matrix includes a first constant matrix; the variables include a first variable, a second variable, a third variable and a fourth variable; The specific steps in step S33 include: S331. Calculate the compensation vector according to the following formula: ; ; ; ; in, is the compensation vector, for The norm of Reference = , For the first preset small amount, , and All refer to the components of the compensation vector, Reference , is the first largest singular value, for The singular values ​​of Reference , Reference , is the interval II type polynomial fuzzy model Fuzzy rules about The first system matrix, is the first A fuzzy rule, , Reference , is the first variable, for The inverse matrix of is the first constant matrix, Reference , is the third variable, for The transposed matrix of for The transposed matrix of Reference , is the second variable, Reference , is the fourth variable, Reference , is the second largest singular value, for The singular values ​​of Reference , Reference , is the interval II type polynomial fuzzy model Fuzzy rules about The first input matrix, for The transposed matrix of .

[0013] In a second aspect, the present invention provides a polynomial fuzzy fault estimation observer design device, which is applied to a nonlinear uncertain system with both actuator faults and sensor faults, comprising: An establishing module is used to establish an interval type II polynomial fuzzy model according to the nonlinear uncertain system; A design module, used for designing a polynomial fuzzy fault estimation observer including a compensation vector; the polynomial fuzzy fault estimation observer can simultaneously estimate the actuator fault and the sensor fault; A solution module is used to determine the compensation vector by solving stability analysis conditions based on the interval type II polynomial fuzzy model to construct an available polynomial fuzzy fault estimation observer.

[0014] The polynomial fuzzy fault estimation observer design device provided by the present invention realizes fault diagnosis when the antecedent variables are unmeasurable, meets the requirements of wider and more complex and changeable tasks, and is effectively applicable to nonlinear systems that suffer from actuator faults and sensor faults at the same time.

[0015] Further, the establishing module is executed when being used to establish the interval type II polynomial fuzzy model according to the nonlinear uncertain system: S11. Modeling the nonlinear uncertain system and giving fuzzy rules; S12. Obtain the interval type II polynomial fuzzy model according to the fuzzy rules.

[0016] In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the polynomial fuzzy fault estimation observer design method provided in the first aspect are executed.

[0017] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps in the polynomial fuzzy fault estimation observer design method provided in the first aspect are executed.

[0018] From the above, it can be seen that the polynomial fuzzy fault estimation observer design method provided by the present invention compensates for the influence of unmeasurable antecedent variables by introducing a compensation vector in the observer. It does not require the original system state to converge and does not require the influence of the antecedent variables to meet the linear growth condition. It expands the scope of application and is better suitable for nonlinear systems that suffer from actuator failures and sensor failures at the same time, and is more conducive to adapting to complex and changeable task requirements.

[0019] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flow chart of a polynomial fuzzy fault estimation observer design method provided by an embodiment of the present invention.

[0021] Figure 2This is a diagram showing the effect of actuator fault estimation under dissipation conditions in an embodiment of the present invention.

[0022] Figure 3 This is a diagram showing the effect of sensor fault estimation under dissipation conditions in an embodiment of the present invention.

[0023] Figure 4 This is a diagram showing the effect of actuator fault estimation in an embodiment of the present invention.

[0024] Figure 5 This is a diagram showing the effect of sensor failure estimation in an embodiment of the present invention.

[0025] Figure 6 A schematic diagram of the structure of a polynomial fuzzy fault estimation observer design device provided by an embodiment of the present invention.

[0026] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0027] Description of labels: 100, establishment module; 200, design module; 300, solution module; 13, electronic device; 1301, processor; 1302, memory; 1303, communication bus. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0029] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0030] The present invention provides a polynomial fuzzy fault estimation observer design method, which is applied to a nonlinear uncertain system with both actuator faults and sensor faults, and comprises the following steps: S1. Establish interval type II polynomial fuzzy model based on nonlinear uncertain system; S2. Design a polynomial fuzzy fault estimation observer including a compensation vector; the polynomial fuzzy fault estimation observer can simultaneously estimate actuator faults and sensor faults; S3. Based on the interval type II polynomial fuzzy model, the compensation vector is determined by solving the stability analysis condition to construct a usable polynomial fuzzy fault estimation observer.

[0031] This embodiment designs a polynomial fuzzy fault estimation observer including a compensation vector and solves the compensation vector based on the established interval type II polynomial fuzzy model, thereby compensating for the impact of unmeasurable antecedent variables. It does not require the original system state to converge and does not require the impact of the antecedent variables to meet the linear growth condition. It realizes fault diagnosis when the antecedent variables are unmeasurable, meets the requirements of a wider range of more complex and changeable tasks, and is effectively applicable to nonlinear systems that suffer from actuator faults and sensor faults at the same time.

[0032] In some embodiments, the specific steps in step S1 include: S11. Model nonlinear uncertain systems and give fuzzy rules; S12. Obtain an interval type II polynomial fuzzy model based on fuzzy rules.

[0033] In this embodiment, a nonlinear uncertain system that is simultaneously subject to actuator failure, sensor failure and external interference can be modeled by an interval type II polynomial fuzzy model, and then the interval type II polynomial fuzzy model is obtained by rewriting and used as the basis for the subsequent design of the observer.

[0034] Specifically, for Fuzzy rules: If belong ,in, For the The fuzzy rule antecedent variables, For the antecedent variables, , is the number of antecedent variables, For the interval type II fuzzy sets of fuzzy rules.

[0035] The expressions for each fuzzy rule are: ; in, Expressed as The derivative of is the control input, for The transposed matrix of Sensor failure, there is and And all belong to ( space), for The derivative of For actuator failure, for The derivative of For external interference and belongs to .

[0036] The interval II type polynomial fuzzy model can be rewritten through fuzzy reasoning, and the expression of the interval II type polynomial fuzzy model is: ; in, for The derivative of Reference , for The transposed matrix of for The transposed matrix of is the system state vector, for The transposed matrix of is the interval type II polynomial fuzzy model A fuzzy rule, , is the number of fuzzy rules of interval type II polynomial fuzzy model, The interval II type polynomial fuzzy model The first membership function of z after the normalization of the fuzzy rules, Reference , The interval II type polynomial fuzzy model Fuzzy rules about The first system matrix, Reference , The interval II type polynomial fuzzy model Fuzzy rules about The first input matrix is is the control input, Indicates an actuator failure. Reference , The interval II type polynomial fuzzy model Fuzzy rules about The second input matrix of external interference, is the identity matrix, Reference , For external interference, for The derivative of To indicate sensor failure, is the system output, Reference , is the first output matrix, is the second output matrix.

[0037] In the above embodiment, , and are the i-th fuzzy rules about The descending function of , ,and , and meet , , Reference , For the The upper bound of the membership function of the fuzzy rule with respect to z is: , For the The lower bound of the membership function of the fuzzy rule with respect to z is: .

[0038] In some embodiments, the expression of the polynomial fuzzy fault estimation observer is: ; in, for The derivative of Reference , for The transposed matrix of for The transposed matrix of for The estimated value of for The transposed matrix of for The estimated value of is the first part of the polynomial fuzzy fault estimation observer A fuzzy rule, , is the number of fuzzy rules of the polynomial fuzzy fault estimation observer, is the polynomial fuzzy fault estimation observer The fuzzy rules are normalized The second membership function of is the polynomial fuzzy fault estimation observer Fuzzy rules about The second system matrix, is the polynomial fuzzy fault estimation observer Fuzzy rules about The third input matrix is for The estimated value of is the polynomial fuzzy fault estimation observer Fuzzy rules about The first gain matrix, for Estimated value, is the compensation vector, is the first part of the polynomial fuzzy fault estimation observer A fuzzy rule, , is the polynomial fuzzy fault estimation observer The fuzzy rules are normalized The second membership function of is the polynomial fuzzy fault estimation observer Fuzzy rules about The second gain matrix, for The derivative of for The derivative of is a polynomial fuzzy fault estimation observer about The third gain matrix.

[0039] In this embodiment, in addition to solving the compensation vector, the first gain matrix, the second gain matrix and the third gain matrix need to be solved to obtain the usable polynomial fuzzy fault estimation observer. The fuzzy rules are normalized The second membership function of can be preset according to the requirements of the polynomial fuzzy fault estimation observer, but it needs to satisfy and , , is the polynomial fuzzy fault estimation observer The fuzzy rules are normalized The second membership function of .

[0040] In practical applications, the number of fuzzy rules of the nonlinear uncertain system (i.e., the original system) and the number of fuzzy rules of the polynomial fuzzy fault estimation observer are not required to be the same, and the normalized membership function form is not required to be the same as that of the original system. That is, it allows the design of completely mismatched polynomial fuzzy fault estimation observers, which not only improves the design freedom, but also effectively reduces the design cost of the polynomial fuzzy fault estimation observer when the nonlinearity of the nonlinear uncertain system is strong.

[0041] Furthermore, the polynomial fuzzy fault estimation observer designed in this embodiment does not require stabilization of the original system and is applicable to fault estimation problems under a wider range of tasks. In addition, the design process avoids iterative calculations, effectively reducing computational complexity.

[0042] In some embodiments, the specific steps in step S3 include: S31. Given stability analysis conditions based on interval type II polynomial fuzzy model and polynomial fuzzy fault estimation observer; S32. Solve the constant matrix and variables that meet the stability analysis conditions; S33. Obtain a compensation vector based on the constant matrix and the variable.

[0043] In this embodiment, the system evaluation output is given based on the interval II type polynomial fuzzy model: ; in, Output for system evaluation: For the given third output matrix, Reference , Reference , is the given fourth output matrix.

[0044] Then, according to the performance index requirements, a constant is selected. Finally, a performance matrix that satisfies the following performance index parameters is given: (1) and ; (2) ; (3) ; (4) ; (5) ; in, , , and All are performance matrices. for The transposed matrix of for The transposed matrix of .

[0045] Furthermore, given the stability analysis conditions: (1) is the sum of squares; (2) is the sum of squares; (3) is the sum of squares; (4) is the sum of squares; (5) is the sum of squares; (6) is the sum of squares; (7) is the sum of squares; (8) is the sum of squares; (9) is the sum of squares; in, for The transposed matrix of for The transposed matrix of for The transposed matrix of for The transposed matrix of for The transposed matrix of for The transposed matrix of , , and Both and Any vector that is unrelated, For nonlinear uncertain systems A fuzzy rule, For the and fuzzy rules are related to and Irrelevant given vector, For the fuzzy rules are related to and Irrelevant given vector, , Reference , is the first constant matrix, is the second constant matrix, , , , , and These are the second small quantities to ensure the absolute positive definiteness of the conditions. for The transposed matrix of , , and are variables, is the third relaxation matrix, Reference , Reference , Reference , Reference , for The transposed matrix of Reference , is the polynomial fuzzy fault estimation observer Fuzzy rules about The first polynomial matrix of is the polynomial fuzzy fault estimation observer Fuzzy rules about The second polynomial matrix of Reference , Reference , for The transposed matrix of Reference , is the third polynomial matrix, Reference , Reference , for The transposed matrix of Reference , is the polynomial fuzzy fault estimation observer Fuzzy rules about The second polynomial matrix of Reference , Reference , for The transposed matrix of Reference , for The transposed matrix of for The lower bound of for The upper bound of for The estimated value of Reference , is a diagonal matrix, is the first relaxation matrix, is the second relaxation matrix.

[0046] In practical application, the sostools toolbox software in MATLAB is used to solve the problem so that for all and The constant matrix (including and ), polynomial matrices (including , , and ), relaxation matrix (including and ) and variables including , , and ).

[0047] Furthermore, after solving the constant matrix, the above polynomial matrix and variables, the gain matrix is ​​obtained: .

[0048] Further, in some embodiments, the constant matrix includes a first constant matrix; the variables include a first variable, a second variable, a third variable, and a fourth variable; The specific steps in step S33 include: S331. Calculate the compensation vector according to the following formula: ; ; ; ; in, is the compensation vector, for The norm of Reference = , For the first preset small amount, , and All refer to the components of the compensation vector, Reference , is the first largest singular value, for The singular values ​​of Reference , Reference , is the interval II type polynomial fuzzy model Fuzzy rules about The first system matrix, is the interval II type polynomial fuzzy model A fuzzy rule, , Reference , is the first variable, for The inverse matrix of is the first constant matrix, Reference , is the third variable, for The transposed matrix of for The transposed matrix of Reference , is the second variable, Reference , is the fourth variable, Reference , is the second largest singular value, for The singular values ​​of Reference , Reference , is the interval II type polynomial fuzzy model Fuzzy rules about The first input matrix, for The transposed matrix of .

[0049] The following is a simulation example of a single-section rigid body robot arm (i.e., a nonlinear uncertain system) to verify the effectiveness of the polynomial fuzzy fault estimation observer obtained by the polynomial fuzzy fault estimation observer design method of the present application: Set the simulation time to 25s and the minimum simulation step size to s.

[0050] The dynamic equation of the robot arm is ; is the movement angle of the robot arm.

[0051] The physical parameters are set as follows: kgm 2 , kg, kg, m. m / s 2 , .

[0052] The system state of the interval type II polynomial fuzzy model corresponding to the robotic arm is: , .

[0053] The relevant parameters corresponding to the robotic arm are: ; ; ; ; ; ; ; ; in, The first fuzzy rule of the nonlinear uncertain system is The first system matrix, The first fuzzy rule of the nonlinear uncertain system is The first input matrix is The second fuzzy rule of the nonlinear uncertain system is The first system matrix, The second fuzzy rule of the nonlinear uncertain system is The first input matrix is The first fuzzy rule of the nonlinear uncertain system is The second input matrix of external interference, The second fuzzy rule of the nonlinear uncertain system is The second input matrix of external interference.

[0054] Further, we get The upper bound (including and ) and the lower bound (including and )for: ; ; ; ; in, for when The corresponding value when for when The corresponding value when for when The corresponding value when for when The corresponding value when Reference , is the system state vector under the first fuzzy rule, Reference .

[0055] Furthermore, ; ; in, is the first fuzzy rule after normalization of the polynomial fuzzy fault estimation observer. The second membership function of for The estimated value of The second fuzzy rule of the polynomial fuzzy fault estimation observer after normalization is The second membership function of .

[0056] Furthermore, ;in, For time.

[0057] ; ; ; ; Furthermore, the interference and fault signals are given as: ; in, is a random variable with mean 0 and variance 0.04.

[0058] ; ; Furthermore, ; ; ; ; Further, the following is given for dissipation and The simulation verification results of the two cases, the corresponding performance matrix of the two cases is: (1) Dissipation: , , , ; Given .

[0059] (2) Condition: , , , ; According to the stability analysis conditions, the minimum It is 0.4167.

[0060] in, The situation is a common performance indicator in this field and is common knowledge in this field. and Explain the definition of It is a performance indicator parameter.

[0061] It should be noted that It reflects the performance of In case, The smaller the size, the better the performance. Figure 2 , Attachment Figure 3 , Attachment Figure 4 and attached Figure 5 , it can be seen that in the corresponding Under these conditions, the polynomial fuzzy fault estimation observer obtained by the polynomial fuzzy fault estimation observer design method of the present application can perform effective fault estimation for nonlinear uncertain systems that are simultaneously subject to actuator faults, sensor faults and external interference. Therefore, the effectiveness of the polynomial fuzzy fault estimation observer of the present application can be confirmed.

[0062] Please refer to Figure 6 , Figure 6A polynomial fuzzy fault estimation observer design device in some embodiments of the present invention is applied to a nonlinear uncertain system with both actuator faults and sensor faults. The polynomial fuzzy fault estimation observer design device is integrated in a back-end control device in the form of a computer program, and includes: Establishing module 100, establishing an interval type II polynomial fuzzy model according to a nonlinear uncertain system; A design module 200 is used to design a polynomial fuzzy fault estimation observer including a compensation vector; the polynomial fuzzy fault estimation observer can simultaneously estimate actuator faults and sensor faults; The solution module 300 is used to determine the compensation vector by solving the stability analysis condition based on the interval type II polynomial fuzzy model to construct an available polynomial fuzzy fault estimation observer.

[0063] In some embodiments, the building module 100 is executed when used to build an interval type II polynomial fuzzy model according to a nonlinear uncertain system: S11. Model nonlinear uncertain systems and give fuzzy rules; S12. Obtain an interval type II polynomial fuzzy model based on fuzzy rules.

[0064] In some embodiments, the solution module 300 is executed when being used to obtain an available polynomial fuzzy fault estimation observer by solving the compensation vector based on the interval type II polynomial fuzzy model: S31. Given stability analysis conditions based on interval type II polynomial fuzzy model and polynomial fuzzy fault estimation observer; S32. Solve the constant matrix and variables that meet the stability analysis conditions; S33. Obtain a compensation vector based on the constant matrix and the variable.

[0065] Please refer to Figure 7 , Figure 7A structural schematic diagram of an electronic device provided by an embodiment of the present invention, the present invention provides an electronic device 13, including: a processor 1301 and a memory 1302, the processor 1301 and the memory 1302 are interconnected and communicate with each other through a communication bus 1303 and / or other forms of connection mechanisms (not marked), the memory 1302 stores computer-readable instructions executable by the processor 1301, when the electronic device is running, the processor 1301 executes the computer-readable instructions to execute the polynomial fuzzy fault estimation observer design method in any optional implementation of the above embodiment to achieve the following functions: establish an interval type II polynomial fuzzy model according to a nonlinear uncertain system; design a polynomial fuzzy fault estimation observer including a compensation vector; the polynomial fuzzy fault estimation observer can simultaneously estimate actuator faults and sensor faults; based on the interval type II polynomial fuzzy model, determine the compensation vector by solving the stability analysis condition to construct a usable polynomial fuzzy fault estimation observer.

[0066] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the polynomial fuzzy fault estimation observer design method in any optional implementation of the above-mentioned embodiment is executed to achieve the following functions: establishing an interval type II polynomial fuzzy model according to a nonlinear uncertain system; designing a polynomial fuzzy fault estimation observer including a compensation vector; the polynomial fuzzy fault estimation observer can simultaneously estimate actuator faults and sensor faults; based on the interval type II polynomial fuzzy model, determining the compensation vector by solving a stability analysis condition to construct a usable polynomial fuzzy fault estimation observer.

[0067] Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0068] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0069] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0070] Furthermore, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0071] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0072] The above description is only an embodiment of the present invention and is not intended to limit the protection scope of the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A polynomial fuzzy fault estimation observer design method is applied to nonlinear uncertain systems with both actuator faults and sensor faults, characterized in that: The following steps are involved: S1. Establishing an interval type II polynomial fuzzy model according to the nonlinear uncertain system; S2. Designing a polynomial fuzzy fault estimation observer including a compensation vector; the polynomial fuzzy fault estimation observer is capable of simultaneously estimating the actuator fault and the sensor fault; S3. Based on the interval type II polynomial fuzzy model, the compensation vector is determined by solving the stability analysis condition to construct an available polynomial fuzzy fault estimation observer.

2. The polynomial fuzzy fault estimation observer design method according to claim 1, characterized in that: The specific steps in step S1 include: S11. Modeling the nonlinear uncertain system and giving fuzzy rules; S12. Obtain the interval type II polynomial fuzzy model according to the fuzzy rules.

3. The polynomial fuzzy fault estimation observer design method according to claim 2, characterized in that: The expression of the interval II type polynomial fuzzy model is: ； in, for The derivative of Reference , for The transposed matrix of for The transposed matrix of is the system state vector, for The transposed matrix of is the first A fuzzy rule, , is the number of fuzzy rules of the interval II type polynomial fuzzy model, is the interval II type polynomial fuzzy model The first membership function of z after the normalization of the fuzzy rules, Reference , is the interval II type polynomial fuzzy model Fuzzy rules about The first system matrix, Reference , is the interval II type polynomial fuzzy model Fuzzy rules about The first input matrix is is the control input, Refers to the actuator failure, Reference , is the interval II type polynomial fuzzy model Fuzzy rules about The second input matrix of external interference, is the identity matrix, Reference , For external interference, for The derivative of To indicate the sensor failure, is the system output, Reference , is the first output matrix, is the second output matrix.

4. The polynomial fuzzy fault estimation observer design method according to claim 3, characterized in that: The expression of the polynomial fuzzy fault estimation observer is: ； in, for The derivative of Reference , for The transposed matrix of for The transposed matrix of for The estimated value of for The transposed matrix of for The estimated value of is the first A fuzzy rule, , is the number of fuzzy rules of the polynomial fuzzy fault estimation observer, is the polynomial fuzzy fault estimation observer The fuzzy rules are normalized The second membership function of is the polynomial fuzzy fault estimation observer Fuzzy rules about The second system matrix, is the polynomial fuzzy fault estimation observer Fuzzy rules about The third input matrix is for The estimated value of is the polynomial fuzzy fault estimation observer Fuzzy rules about The first gain matrix, for Estimated value, is the compensation vector, is the first A fuzzy rule, , is the polynomial fuzzy fault estimation observer The fuzzy rules are normalized The second membership function of is the polynomial fuzzy fault estimation observer Fuzzy rules about The second gain matrix, for The derivative of for The derivative of is the polynomial fuzzy fault estimation observer about The third gain matrix.

5. The polynomial fuzzy fault estimation observer design method according to claim 4, characterized in that: The specific steps in step S3 include: S31. Based on the interval II type polynomial fuzzy model and the polynomial fuzzy fault estimation observer given stability analysis conditions; S32. Solving the constant matrix and variables that satisfy the stability analysis conditions; S33. Obtain the compensation vector according to the constant matrix and the variable.

6. The polynomial fuzzy fault estimation observer design method according to claim 5, characterized in that: The constant value matrix includes a first constant value matrix; the variables include a first variable, a second variable, a third variable and a fourth variable; The specific steps in step S33 include: S331. Calculate the compensation vector according to the following formula: ； ； ； ； in, is the compensation vector, for The norm of Reference = , For the first preset small amount, , and All refer to the components of the compensation vector, Reference , is the first largest singular value, for The singular values ​​of Reference , Reference , is the interval II type polynomial fuzzy model Fuzzy rules about The first system matrix, is the first A fuzzy rule, , Reference , is the first variable, for The inverse matrix of is the first constant matrix, Reference , is the third variable, for The transposed matrix of for The transposed matrix of Reference , is the second variable, Reference , is the fourth variable, Reference , is the second largest singular value, for The singular values ​​of Reference , Reference , is the interval II type polynomial fuzzy model Fuzzy rules about The first input matrix is for The transposed matrix of .

7. A polynomial fuzzy fault estimation observer design device, applied to a nonlinear uncertain system with both actuator faults and sensor faults, characterized in that: include: An establishing module is used to establish an interval type II polynomial fuzzy model according to the nonlinear uncertain system; A design module for designing a polynomial fuzzy fault estimation observer including a compensation vector; The polynomial fuzzy fault estimation observer is capable of simultaneously estimating the actuator fault and the sensor fault; A solution module is used to determine the compensation vector by solving stability analysis conditions based on the interval type II polynomial fuzzy model to construct an available polynomial fuzzy fault estimation observer.

8. The polynomial fuzzy fault estimation observer design device according to claim 7, characterized in that: The establishment module is executed when being used to establish the interval type II polynomial fuzzy model according to the nonlinear uncertain system: S11. Modeling the nonlinear uncertain system and giving fuzzy rules; S12. Obtain the interval type II polynomial fuzzy model according to the fuzzy rules.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the polynomial fuzzy fault estimation observer design method according to any one of claims 1 to 6 are executed.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the polynomial fuzzy fault estimation observer design method according to any one of claims 1 to 6 are executed.

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