Fault detection method and fault detection device for multi-robot interconnection system

Abstract

The application provides a fault detection method and a fault detection device for a multi-robot interaction system. The method comprises the following steps: based on a first target model of system modeling, constructing a difference between a second target model included in the first target model and a third target model for fault observation to determine a first estimation error; based on the first estimation error, re-modeling the third target model to obtain a fourth target model; determining a first target variable based on a nonlinear interaction term of the model, and re-modeling the third target model based on the first target variable to obtain a fifth target model; constructing a nonlinear inequality matrix based on the above models and solving the nonlinear inequality matrix to obtain a target fault observer gain; updating the fifth target model based on the target fault observer gain, and determining that the multi-robot interaction system has a fault in the case that an output value of a fault judgment function is greater than a first threshold. The method solves the problem of high fault detection calculation complexity of the multi-robot interaction system in the prior art.

Description

Technical Field

[0001] This invention relates to the field of fault detection technology, and more specifically, to a fault detection method, a fault detection device, a computer-readable storage medium, and a multi-robot interconnection system. Background Technology

[0002] The increasing demand for safety in practical robot systems necessitates research into fault detection methods for robot control systems to enable timely and accurate fault detection. Furthermore, with the development of automatic control technology, single robot control systems are no longer sufficient to meet practical needs, requiring interconnection among multiple robots. Interconnection is a key characteristic of multi-robot systems, referring to effective cooperation among multiple robots rather than simple aggregation. Therefore, researching fault detection in interconnected multi-robot systems is of great significance. From the perspective of interconnection relationships in robot control systems, the structure of robot swarms is mainly divided into centralized and distributed types. To date, numerous effective centralized fault detection methods have been proposed in existing patents. However, it should be noted that in practical multi-robot systems, fault detection in centralized architectures is typically difficult due to limitations in computing power and communication bandwidth.

[0003] Therefore, to address the aforementioned issues, existing technologies have proposed a "distributed control" method, and based on distributed fault detection methods, numerous fault detection schemes have been developed. It is worth noting that most research on distributed fault detection methods focuses on linear multi-robot interconnected systems or Lipschitz nonlinear multi-robot interconnected systems, while distributed fault detection methods for more general nonlinear multi-robot interconnected systems require further investigation.

[0004] It should be noted that in existing technologies, interconnects satisfy the Lipschitz condition and related interconnect terms are handled using linear matrix inequalities. This approach imposes additional restrictions on the Lyapunov matrix, resulting in a large number of linear matrix inequalities in the design conditions, leading to high computational complexity and low monitoring efficiency. Summary of the Invention

[0005] The main objective of this application is to provide a fault detection method, fault detection device, computer-readable storage medium, and multi-robot interconnection system for a multi-robot interconnection system, so as to at least solve the problem of high computational complexity in fault detection of multi-robot interconnection systems in the prior art.

[0006] To achieve the above objectives, according to one aspect of this application, a fault detection method for a multi-robot interconnected system is provided, comprising: modeling the multi-robot interconnected system based on an interconnected nonlinear system model to obtain a first target model, the first target model including multiple second target models, each second target model being the interconnected nonlinear system model corresponding to each robot; constructing a fuzzy fault observer model based on the second target models to obtain a third target model; determining the difference between the second target model and the third target model as a first estimation error, and remodeling the second target model based on the first estimation error and the third target model to obtain a fourth target model; and determining the fault detection method in the model based on the fourth target model and the third target model. A nonlinear interconnection term is used to determine a first target variable based on the boundary constraints of the nonlinear interconnection term. Based on the first target variable, the first estimation error and residual signal of the third target model are remodeled to obtain a fifth target model. The fifth target model is verified using a Lyapunov function. If the verification is successful, multiple nonlinear inequality matrices are constructed based on the fifth model and the second model. The nonlinear inequalities are solved to obtain the target fault observer gain. The fifth target model is updated based on the target fault observer gain to obtain a sixth target model. A fault determination function is constructed based on the sixth target model. If the output value of the fault determination function is greater than a first threshold, it is determined that the multi-robot interconnection system has a fault.

[0007] Optionally, the multi-robot interconnection system is modeled based on an interconnected nonlinear system model to obtain a first target model. The first target model includes multiple second target models, including: modeling each robot in the multi-robot interconnection system and representing them in a compact set including the origin to obtain a seventh target model. Where, x i (t) represents the state of robot i. For x i The derivative of (t), i = 1, 2, ..., N, where N is the number of robots included in the multi-robot interconnection system, j = 1, 2, ..., r i r i Let t be the total number of fuzzy rules for robot i, υ(t) be the antecedent variable, and w be the total number of fuzzy rules for robot i. i (t) and f i (t) represents the disturbance and actuator failure of robot i, respectively, and w i (t) and f i (t) Energy is bounded, satisfying and B ij E ijE ij and C i Let y be the system matrix. i (t) The measurement output of robot i, g ni (x n (t) represents the nonlinear interconnection term, g ni (x n (t) satisfies ||g ni (x n (t))‖≤α ni ||x n (t)‖,α ni h is an unknown constant term. ij (υ i (t) is the membership function, h ij (υ i (t) satisfies h ij (υ i (t)≥0 and The system matrix in the seventh objective model Decomposition yields the first matrix and the second matrix. Among them, A i0 Let A be the constant term in the i-th row. ij To exclude A i0 The part other than: Based on the first matrix and the second matrix, the seventh objective is represented to obtain the second objective model: Among them, A i0 satisfy A ih satisfy B ih satisfy E ih satisfy

[0008] Optionally, a fuzzy fault observer model is constructed based on the second target model to obtain a third target model, including: constructing the fuzzy fault observer model based on the second target model: in, satisfy satisfy For x i The estimated value of (t), For y i The estimated value of (t), r is an estimate of υ(t). i (t) is the residual signal, L ij The gain of the fault detection observer, For g ni (xn The processing function for (t)).

[0009] Optionally, the difference between the second target model and the third target model is determined as a first estimation error, and the second target model is remodeled based on the first estimation error and the third target model to obtain a fourth target model, including: determining the difference between the second target model and the third target model as the first estimation error. Where e i (t) represents the first estimation error; based on the first estimation error and the third target model, the second target model is remodeled to obtain the fourth target model:

[0010]

[0011] Optionally, based on the fourth objective model and the third objective model, nonlinear interconnection terms are determined in the model, and a first objective variable is determined based on the boundary constraints of the nonlinear interconnection terms. Based on the first objective variable, the first estimation error and residual signal of the third objective model are remodeled to obtain a fifth objective model, including: determining the corresponding first boundary constraints based on the nonlinear interconnection terms. Where, η n >0 and η n The term is a constant; the first target variable is determined based on the first boundary constraint. Based on the first objective variable representing the first boundary constraint, the second boundary constraint is obtained: The processing function is reconstructed based on the second boundary constraint. Among them, P i Let P be a Lyapunov matrix, and satisfy P i >0, and It is a uniformly continuous bounded function, and satisfies An eighth objective model is constructed based on the processing function and the first objective variable. The eighth objective model is used to update the second objective variable, which is an estimate of the first objective variable. Where, r n It is a constant, and r n >0, The first target variable is defined as the second target variable; the difference between the first target variable and the second target variable is defined as the third target variable. in, The third target variable is defined as follows: Update; based on the actuator fault fi (t) and the residual signal r i (t) Determine the fault estimation error of the fuzzy fault observer model. in, Constructing the third matrix The third target model is remodeled based on the processing function, the third matrix, and the fault estimation error to obtain the fifth target model:

[0012]

[0013] Optionally, the fifth target model is verified using a Lyapunov function. If the verification passes, multiple nonlinear inequality matrices are constructed based on the fifth model and the second model. The nonlinear inequalities are solved to obtain the target fault observer gain, including: when the actuator fault f i When (t) = 0, based on the Lyapunov matrix P i Constructing the perturbation suppression performance H of the fifth objective model ∞ Lyapunov function The first function is obtained; under the disturbance w i When (t) = 0, based on the Lyapunov matrix P i Constructing the perturbation suppression performance H of the fifth target model ∞ Lyapunov function: The second function is obtained; if the output values ​​of both the first and second functions are greater than the second threshold, the verification is deemed successful; if the verification is successful, a nonlinear inequality matrix is ​​constructed based on the fifth model and the second model: Among them, P i >0, matrix K ij =P i L ij Scalar δ1 > 0, scalar δ2 > 0, scalar θ1 > 0, * represents a symmetric matrix term, and I is the identity matrix; the Lyapunov matrix P is obtained by solving using the Matlab linear matrix solver. i and the matrix K ij The Lyapunov matrix P i and the matrix K ij Substitute into the fourth matrix L ij =P i -1 K ij The gain of the target fault observer is obtained.

[0014] Optionally, and based on the sixth target model, a fault determination function is constructed. If the output value of the fault determination function is greater than a first threshold, it is determined that the multi-robot interconnection system has a fault, including: constructing a fault determination function based on the sixth target model: When the output value J(t) of the fault determination function is greater than the first threshold J th In the case of a fault in the multi-robot interconnection system, wherein,

[0015]

[0016] According to another aspect of this application, a fault detection device for a multi-robot interconnection system is provided. The device includes: a first modeling unit, configured to model the multi-robot interconnection system based on an interconnection nonlinear system model to obtain a first target model, the first target model including multiple second target models, each second target model being the interconnection nonlinear system model corresponding to each robot; a second modeling unit, configured to construct a fuzzy fault observer model based on the second target models to obtain a third target model; a third modeling unit, configured to determine the difference between the second target model and the third target model as a first estimation error, and to remodel the second target model based on the first estimation error and the third target model to obtain a fourth target model; and a fourth modeling unit, configured to model the fourth target model and the third target model... The system first determines the nonlinear interconnection terms in the model and determines the first target variable based on the boundary constraints of the nonlinear interconnection terms. Based on the first target variable, it remodels the first estimation error and residual signal of the third target model to obtain the fifth target model. A first calculation unit verifies the fifth target model using a Lyapunov function. If the verification is successful, it constructs multiple nonlinear inequality matrices based on the fifth and second models, solves the nonlinear inequalities, and obtains the target fault observer gain. A second calculation unit updates the fifth target model based on the target fault observer gain to obtain a sixth target model. It then constructs a fault determination function based on the sixth target model. If the output value of the fault determination function is greater than a first threshold, it determines that the multi-robot interconnection system has a fault.

[0017] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0018] According to another aspect of this application, a multi-robot interconnection system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing any one of the methods described.

[0019] Applying the technical solution of this application, in the fault detection method of the aforementioned multi-robot interconnection system, firstly, the multi-robot interconnection system is modeled based on an interconnection nonlinear system model to obtain a first target model. The first target model includes multiple second target models, each of which is an interconnection nonlinear system model corresponding to each robot. Then, a fuzzy fault observer model is constructed based on the second target models to obtain a third target model. Next, the difference between the second target model and the third target model is determined as a first estimation error, and the second target model is remodeled based on the first estimation error and the third target model to obtain a fourth target model. Finally, the nonlinearity in the model is determined based on the fourth target model and the third target model. The first objective model is determined by boundary constraints of the nonlinear interconnection terms. Based on these first objective variables, the first estimation error and residual signal of the third objective model are remodeled to obtain the fifth objective model. Then, the fifth objective model is validated using a Lyapunov function. If the validation passes, multiple nonlinear inequality matrices are constructed based on the fifth and second models. Solving these nonlinear inequalities yields the target fault observer gain. Finally, the fifth objective model is updated based on the target fault observer gain to obtain the sixth objective model. A fault determination function is constructed based on the sixth objective model. If the output value of the fault determination function is greater than a first threshold, a fault is determined in the multi-robot interconnection system. This application introduces an adaptive mechanism. Based on the modeling, specific variables are extracted through boundary constraints of the nonlinear interconnection terms, and the processing function of the nonlinear interconnection terms is reconstructed. The reconstructed processing function can directly solve for the fault observer gain. Compared to the existing method of scaling nonlinear interconnection terms to generate inequality matrices, this application reduces the number of inequality matrices and solves the problem of high computational complexity in fault detection of multi-robot interconnection systems in the prior art. Attached Figure Description

[0020] Figure 1 A hardware structure block diagram of a mobile terminal for a fault detection method of a multi-robot interconnection system provided in an embodiment of this application is shown.

[0021] Figure 2A flowchart illustrating a fault detection method for a multi-robot interconnection system according to an embodiment of this application is shown. Detailed Implementation

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] As described in the background section, in the prior art, interconnections satisfy the Lipschitz condition and related interconnection terms are processed using linear matrix inequalities. This processing method imposes additional restrictions on the Lyapunov matrix, resulting in a large number of linear matrix inequalities in the design conditions, leading to high computational complexity and low monitoring efficiency. To solve the problem of high computational complexity in fault detection of multi-robot interconnection systems in the prior art, embodiments of this application provide a fault detection method, fault detection device, computer-readable storage medium, and multi-robot interconnection system for multi-robot interconnection systems.

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0027] This embodiment provides a fault detection method for a multi-robot interconnection system running on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0028] Figure 1 This is a flowchart of a fault detection method for a multi-robot interconnection system according to an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0029] Step S201: Model the multi-robot interconnection system based on the interconnection nonlinear system model to obtain a first target model. The first target model includes multiple second target models, which are the interconnection nonlinear system models corresponding to each robot.

[0030] Specifically, a dynamic mathematical model of the multi-robot interconnected system is established, resulting in the aforementioned first objective model. In this process, the multi-robot interconnected system is modeled as a complex interconnected nonlinear system with uncertainties. It can be understood that the aforementioned first objective model can be represented by an interconnected fuzzy large system S composed of multiple subsystems in a compact set Θ. xi The precise representation is achieved through multiple of the aforementioned second objective models.

[0031] Step S202: Construct a fuzzy fault observer model based on the second objective model described above to obtain the third objective model;

[0032] Specifically, for each of the second objective models in the first objective model, a corresponding fault detection observer is designed, and a residual signal is generated to obtain a fault detection system. The fault observer is the third objective model.

[0033] Step S203: The difference between the second target model and the third target model is determined as the first estimation error, and the second target model is remodeled based on the first estimation error and the third target model to obtain the fourth target model;

[0034] Specifically, the first estimation error is defined as a new variable, and then the interconnected augmentation robot system is modeled according to the second objective model and the corresponding third objective model to obtain the fourth objective model.

[0035] Step S204: Based on the above fourth objective model and the above third objective model, determine the nonlinear interconnection term in the model, and determine the first objective variable based on the boundary constraints of the above nonlinear interconnection term. Based on the above first objective variable, remodel the above first estimation error and residual signal of the above third objective model to obtain the fifth objective model.

[0036] Specifically, the unknown variables are redefined based on the nonlinear interconnection terms in the fourth objective model, and adaptive updates are performed based on the unknown variables. The third objective model is then remodeled to obtain the fifth objective model.

[0037] Step S205: Based on the fifth target model mentioned above, the Lyapunov function is used for verification. If the verification is successful, multiple nonlinear inequality matrices are constructed based on the fifth model and the second model mentioned above. The nonlinear inequalities are solved to obtain the target fault observer gain.

[0038] Specifically, based on the aforementioned fifth objective model, a nonlinear inequality matrix is ​​directly constructed and solved to obtain the fault observer gain. Compared with existing technologies, this application does not require additional inequality restrictions on the Lyapunov matrix and can directly solve it, omitting a large number of nonlinear inequalities and reducing computational complexity.

[0039] Step S206: Update the fifth target model based on the gain of the target fault observer to obtain the sixth target model, and construct a fault determination function based on the sixth target model. If the output value of the fault determination function is greater than the first threshold, it is determined that the multi-robot interconnection system has a fault.

[0040] Specifically, based on the gain of the aforementioned target fault observer, the fifth target model is updated to eliminate unknown terms, and then the fault detection can be performed through the fault determination function using the observation results of the aforementioned fault observer model.

[0041] In this embodiment, firstly, a multi-robot interconnection system is modeled based on an interconnection nonlinear system model to obtain a first target model. This first target model includes multiple second target models, each representing the interconnection nonlinear system model corresponding to each robot. Then, a fuzzy fault observer model is constructed based on the second target models to obtain a third target model. Next, the difference between the second and third target models is determined as a first estimation error. Based on this first estimation error and the third target model, the second target model is remodeled to obtain a fourth target model. Finally, based on the fourth and third target models, the nonlinear interconnection terms in the model are determined, and based on the nonlinear interconnection... The boundary constraints of the interconnection terms determine the first target variable. Based on the first target variable, the first estimation error and residual signal of the third target model are remodeled to obtain the fifth target model. Then, the fifth target model is verified using a Lyapunov function. If the verification is successful, multiple nonlinear inequality matrices are constructed based on the fifth and second models. Solving these nonlinear inequalities yields the target fault observer gain. Finally, the fifth target model is updated based on the target fault observer gain to obtain the sixth target model. A fault determination function is constructed based on the sixth target model. If the output value of the fault determination function is greater than a first threshold, a fault is determined in the multi-robot interconnection system. This application introduces an adaptive mechanism. Based on the modeling, specific variables are extracted through boundary constraints of the nonlinear interconnection terms, and the processing function of the nonlinear interconnection terms is reconstructed. The reconstructed processing function can directly solve for the fault observer gain. Compared with the existing method of scaling nonlinear interconnection terms to generate inequality matrices, this application reduces the number of inequality matrices and solves the problem of high computational complexity in fault detection of multi-robot interconnection systems in the prior art.

[0042] To construct the second target model described above, in one optional implementation, step S201 includes:

[0043] Step S2011: Model each of the robots in the above multi-robot interconnection system and represent them in a compact set including the origin to obtain the seventh target model:

[0044]

[0045] Where, x i (t) represents the state of robot i mentioned above. For x i The derivative of (t), i = 1, 2, ..., N, where N is the number of robots included in the above multi-robot interconnection system, j = 1, 2, ..., r i ri Let t be the total number of fuzzy rules for robot i, υ(t) be the antecedent variable, and w be the total number of fuzzy rules for robot i. i (t) and f i (t) represents the disturbance and actuator failure of robot i, respectively, w i (t) and f i (t) Energy is bounded, satisfying and B ij E ij E ij and C i Let y be the system matrix. i (t) The measurement output of robot i above, g ni (x n (t) represents the aforementioned nonlinear interconnection term, g ni (x n (t) satisfies ||g ni (x n (t))‖≤α ni ||x n (t)‖,α ni h is an unknown constant term. ij (υ i (t) is the membership function, h ij (υ i (t) satisfies h ij (υ i (t)≥0 and

[0046] Specifically, in the first objective model described above, the initial model of the i-th second objective model can be represented as:

[0047]

[0048] Furthermore, the aforementioned second objective model satisfies the above-mentioned constraints.

[0049] Step S2012, the system matrix in the seventh objective model is... Decomposition yields the first matrix and the second matrix. Among them, A i0 Let A be the constant term in the i-th row. ij To exclude A i0 Other parts:

[0050] Specifically, to facilitate the subsequent Lyapunov matrix representation and calculation, this application sets up a method for representing the aforementioned system matrix. Decompose into Among them, A i0 Let A be the constant term in the i-th row. ij To exclude Ai0 The part other than that.

[0051] Step S2013: Based on the first matrix and the second matrix, the seventh objective is represented to obtain the second objective model:

[0052]

[0053] Among them, A i0 satisfy A ih satisfy B ih satisfy E ih satisfy

[0054] Specifically, based on the system matrix after the above decomposition, the seventh objective model is re-represented to obtain the second objective model:

[0055]

[0056] To construct the aforementioned third target model, in one optional implementation, step S202 includes:

[0057] Step S2021: Construct the fuzzy fault observer model based on the second target model:

[0058]

[0059] in, satisfy satisfy For x i The estimated value of (t), For y i The estimated value of (t), r is an estimate of υ(t). i (t) is the residual signal, L ij The gain of the fault detection observer, For g ni (x n The processing function for (t)).

[0060] Specifically, for the i-th of the aforementioned first objective models, the constructed third objective model is as follows:

[0061]

[0062] It is understandable that the processing function mentioned above is only defined here to handle unknown nonlinear interconnect terms, and the gain of the fault observer mentioned above is only defined, without a specific solution formula given here.

[0063] To construct the above processing function, in one optional implementation, step S203 includes:

[0064] Step S2031: The difference between the second objective model and the third objective model is determined as the first estimation error. Where e i (t) represents the first estimation error mentioned above;

[0065] Specifically, the estimation error is redefined as a new variable. That is, the first estimation error mentioned above.

[0066] Step S2032: Based on the first estimation error and the third objective model, the second objective model is remodeled to obtain the fourth objective model.

[0067]

[0068] Then, based on the i-th second objective model and the i-th third objective model mentioned above, the i-th interconnected augmented robot system is remodeled:

[0069]

[0070] To construct the above processing function, in one optional implementation, step S204 includes:

[0071] Step S2041: Determine the corresponding first boundary constraint based on the above nonlinear interconnection terms:

[0072] Where, η n >0 and η n For constant terms;

[0073] Specifically, based on the representation of the fourth objective model described above, restrictions can be imposed on the aforementioned nonlinear interconnection terms:

[0074]

[0075] Where, η n It is a constant term and η n >0, that is, η n The arbitrary constant has no effect on the subsequent solution of the model.

[0076] Step S2042: Determine the first target variable based on the first boundary constraint.

[0077] Specifically, based on the aforementioned fourth objective model and the aforementioned first boundary constraint, new unknown variables are introduced. That is, the first target variable mentioned above.

[0078] Step S2043: Based on the first objective variable representing the first boundary constraint, the second boundary constraint is obtained:

[0079]

[0080] Specifically, based on the definition of the first target variable above, the first boundary constraint is re-expressed as follows:

[0081]

[0082] Step S2044: Reconstruct the processing function based on the second boundary constraint.

[0083]

[0084] Among them, P i Let P be a Lyapunov matrix, and satisfy P i >0, and It is a uniformly continuous bounded function, and satisfies

[0085] Specifically, based on the aforementioned fourth objective model and the aforementioned second boundary constraint, the aforementioned processing function is constructed as follows:

[0086]

[0087] Step S2045: Construct an eighth objective model based on the above processing function and the above first objective variable. The eighth objective model is used to update the second objective variable, which is an estimate of the above first objective variable.

[0088]

[0089] Where, r n It is a constant, and r n >0, The second objective variable mentioned above;

[0090] Specifically, based on the first objective variable mentioned above, the second objective variable is defined as follows: The estimated value of the first objective variable is used to represent the value of the second objective variable, and the update of the second objective variable is applicable to the eighth objective model.

[0091] Step S2046: The difference between the first target variable and the second target variable is determined as the third target variable.

[0092] in, The third objective variable is defined above, and the third objective variable is defined according to... renew;

[0093] Specifically, the estimation error corresponding to the definitions of the first and second target variables mentioned above is: Then, an adaptive update formula for the above estimation error is constructed.

[0094] It is understandable that the above adaptive update ensures that the above estimation error is applicable to any processing function, without the need to restrict and process the nonlinear interconnection terms through nonlinear inequalities as in the prior art.

[0095] Step S2047, based on the aforementioned actuator fault f i (t) and the aforementioned residual signal r i (t) Determine the fault estimation error of the above fuzzy fault observer model. in,

[0096] Specifically, based on the above definition, the fault estimation error of the reconstructed fuzzy fault observer model is: in That is, a matrix of length m.

[0097] Step S2048, construct the third matrix

[0098] Specifically, redefining the matrix That is, the third matrix mentioned above.

[0099] Step S2049: Based on the above processing function, the above third matrix, and the above fault estimation error, the above third objective model is remodeled to obtain the above fifth objective model:

[0100]

[0101] Specifically, based on the aforementioned fault estimation error and the aforementioned third matrix, the aforementioned third objective model is remodeled to obtain the i-th aforementioned fifth objective model in the new multi-robot interconnection system as follows:

[0102]

[0103] To obtain the aforementioned target fault observer gain, in one optional implementation, step S205 includes:

[0104] Step S2051, in the above-mentioned actuator failure f i When (t) = 0, based on the Lyapunov matrix P mentioned above i The perturbation suppression performance H of the fifth objective model described above is constructed. ∞ Lyapunov function The first function is obtained;

[0105] Specifically, in the aforementioned actuator failure f i When (t) = 0, establish the H of the Lyapunov function analysis system. ∞ Perturbation suppression performance, Lyapunov function V i (t) is as follows:

[0106]

[0107] Step S2052, under the above disturbance w i When (t) = 0, based on the Lyapunov matrix P mentioned above i The perturbation suppression performance H of the fifth objective model described above is constructed. ∞ Lyapunov function: The second function is obtained;

[0108] Specifically, in the aforementioned disturbance w i When (t) = 0, establish the H of the Lyapunov function analysis system. ∞ Perturbation suppression performance, Lyapunov function V fi (t) is as follows:

[0109]

[0110] In the above embodiments, the performance of the target fuzzy fault observer is verified by the above two verification methods. If the verification is successful, the gain is further solved to obtain the final target fuzzy fault observer.

[0111] Step S2053: If the output values ​​of both the first function and the second function are greater than the second threshold, the verification is deemed successful.

[0112] Step S2054: If the verification passes, construct the nonlinear inequality matrix based on the fifth model and the second model described above:

[0113]

[0114] Among them, P i >0, matrix K ij =P i L ijScalar δ1 > 0, scalar δ2 > 0, scalar θ1 > 0, * is a symmetric matrix term, and I is the identity matrix;

[0115] Specifically, if there exists a matrix P i >0, matrix K ij =P i L ij Scalar δ1 > 0 and scalar δ2 > 0, and a given constant matrix M ij If the inequalities θ1 > 0 and θ2 > 0 satisfy the above inequalities, then the robot interconnection system can achieve stability and relatively ideal fault monitoring performance.

[0116] Step S2055: Solve the matrix using the Matlab linear matrix solver to obtain the Lyapunov matrix P. i And the above matrix K ij ;

[0117] Specifically, based on the above inequalities, the gain of the fuzzy fault observer is solved, i.e., according to matrix K. ij =P i L ij Determine matrix L ij =P i -1 K ij It can be seen that for the above Lyapunov matrix P i And the above matrix K ij Continue twisting to find the solution.

[0118] Step S2056, the above Lyapunov matrix P i And the above matrix K ij Substitute into the fourth matrix L ij =P i -1 K ij The gain of the target fault observer is obtained.

[0119] Specifically, the Lyapunov matrix P mentioned above i And the above matrix K ij Substitute into the fourth matrix L ij =P i -1 K ij The gain of the target fault observer is obtained.

[0120] To achieve fault detection, in one optional implementation, step S206 includes:

[0121] Step S2061: Construct a fault determination function based on the sixth objective model described above:

[0122] Specifically, based on the sixth objective model mentioned above, and according to the evaluation agency function, the above fault determination function is constructed:

[0123] Step S2062, when the output value J(t) of the above fault determination function is greater than the first threshold J th In this case, it was determined that the aforementioned multi-robot interconnection system was faulty, among which,

[0124] Specifically, based on the aforementioned fault determination function, its threshold is set as follows: Where supremum represents the supremum. When J(t) > J th The time indicates a fault has occurred, J(t)≤J th This indicates that no malfunction has occurred.

[0125] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0126] This application also provides a fault detection device for a multi-robot interconnection system. It should be noted that the fault detection device for a multi-robot interconnection system in this application can be used to execute the fault detection method for a multi-robot interconnection system provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0127] The following describes the fault detection device for the multi-robot interconnection system provided in the embodiments of this application.

[0128] Figure 2 This is a structural block diagram of a fault detection device for a multi-robot interconnection system according to an embodiment of this application. Figure 2 As shown, the device includes:

[0129] The first modeling unit 10 is used to model the multi-robot interconnection system based on the interconnection nonlinear system model to obtain a first target model. The first target model includes multiple second target models, and the second target models are the interconnection nonlinear system models corresponding to each robot.

[0130] The second modeling unit 20 is used to construct a fuzzy fault observer model based on the above-mentioned second objective model to obtain the third objective model;

[0131] The third modeling unit 30 is used to determine the difference between the second target model and the third target model as the first estimation error, and to remodel the second target model based on the first estimation error and the third target model to obtain the fourth target model.

[0132] The fourth modeling unit 40 is used to determine the nonlinear interconnection term in the model based on the fourth objective model and the third objective model, and to determine the first objective variable based on the boundary constraints of the nonlinear interconnection term. Based on the first objective variable, the first estimation error and residual signal of the third objective model are remodeled to obtain the fifth objective model.

[0133] The first computing unit 50 is used to verify the fifth target model based on the Lyapunov function. If the verification is successful, multiple nonlinear inequality matrices are constructed based on the fifth model and the second model. The nonlinear inequalities are solved to obtain the target fault observer gain.

[0134] The second calculation unit 60 is used to update the fifth target model based on the gain of the target fault observer to obtain the sixth target model, and to construct a fault determination function based on the sixth target model. If the output value of the fault determination function is greater than the first threshold, the multi-robot interconnection system is determined to have a fault.

[0135] In this embodiment, the first modeling unit models the multi-robot interconnection system based on an interconnected nonlinear system model to obtain a first target model. This first target model includes multiple second target models, each corresponding to a different interconnected nonlinear system model for each robot. The second modeling unit constructs a fuzzy fault observer model based on the second target models to obtain a third target model. The third modeling unit determines the difference between the second and third target models as a first estimation error, and remodels the second target model based on the first estimation error and the third target model to obtain a fourth target model. The fourth modeling unit determines the nonlinear interconnection terms in the model based on the fourth target model and the third target model, and based on the aforementioned... The boundary constraints of the nonlinear interconnection terms determine the first target variable. Based on the first target variable, the first estimation error and residual signal of the third target model are remodeled to obtain the fifth target model. The first computing unit verifies the fifth target model using the Lyapunov function. If the verification is successful, multiple nonlinear inequality matrices are constructed based on the fifth model and the second model. The nonlinear inequalities are solved to obtain the target fault observer gain. The second computing unit updates the fifth target model based on the target fault observer gain to obtain the sixth target model. A fault determination function is constructed based on the sixth target model. If the output value of the fault determination function is greater than a first threshold, the multi-robot interconnection system is determined to have a fault. This application introduces an adaptive mechanism. Based on the modeling, specific variables are extracted by the boundary constraints of the nonlinear interconnection terms. The processing function of the nonlinear interconnection terms is reconstructed. The fault observer gain can be directly solved using the reconstructed processing function. Compared with the prior art of scaling the nonlinear interconnection terms to generate inequality matrices, this application reduces the number of inequality matrices and solves the problem of high computational complexity for fault detection in multi-robot interconnection systems in the prior art.

[0136] To construct the aforementioned second target model, in one optional implementation, the aforementioned first modeling unit includes:

[0137] The first modeling module is used to model each of the robots in the aforementioned multi-robot interconnection system and represent them in a compact set including the origin, thus obtaining the seventh target model:

[0138]

[0139] Where, x i (t) represents the state of robot i mentioned above. For x i The derivative of (t), i = 1, 2, ..., N, where N is the number of robots included in the above multi-robot interconnection system, j = 1, 2, ..., ri r i Let t be the total number of fuzzy rules for robot i, υ(t) be the antecedent variable, and w be the total number of fuzzy rules for robot i. i (t) and f i (t) represents the disturbance and actuator failure of robot i, respectively, and w i (t) and f i (t) Energy is bounded, satisfying and B ij E ij E ij and C i Let y be the system matrix. i (t) The measurement output of robot i above, g ni (x n (t) represents the aforementioned nonlinear interconnection term, g ni (x n (t) satisfies ||g ni (x n (t))‖≤α ni ||x n (t)‖,α ni h is an unknown constant term. ij (υ i (t) is the membership function, h ij (υ i (t) satisfies h ij (υ i (t)≥0 and

[0140] The decomposition module is used to decompose the system matrix in the seventh objective model mentioned above. Decomposition yields the first matrix and the second matrix. Among them, A i0 Let A be the constant term in the i-th row. ij To exclude A i0 Other parts:

[0141] The second modeling module is used to represent the seventh objective based on the first and second matrices mentioned above, thereby obtaining the second objective model:

[0142]

[0143] Among them, A i0 satisfy A ih satisfy B ih satisfy E ih satisfy

[0144] To construct the aforementioned third objective model, in one optional implementation, the second modeling unit includes:

[0145] The third modeling module is used to construct the fuzzy fault observer model based on the second target model:

[0146]

[0147] in, satisfy satisfy For x i The estimated value of (t), For y i The estimated value of (t), r is an estimate of υ(t). i (t) is the residual signal, L ij The gain of the fault detection observer, For g ni (x n The processing function for (t)).

[0148] To construct the aforementioned processing function, in one optional implementation, the third modeling unit includes:

[0149] The first determining module is used to determine the difference between the second target model and the third target model as the first estimation error: Where e i (t) represents the first estimation error mentioned above;

[0150] The fourth modeling module is used to remodel the second objective model based on the first estimation error and the third objective model to obtain the fourth objective model:

[0151]

[0152] To construct the aforementioned processing function, in one optional implementation, the fourth modeling unit includes:

[0153] The second determining module is used to determine the corresponding first boundary constraint based on the aforementioned nonlinear interconnection term:

[0154] Where, η n >0 and η n For constant terms;

[0155] The third determining module is used to determine the first target variable based on the first boundary constraint.

[0156] The fifth modeling module is used to obtain the second boundary constraint based on the first objective variable representing the first boundary constraint:

[0157]

[0158] The sixth modeling module is used to reconstruct the aforementioned processing function based on the second boundary constraint.

[0159] Among them, P i Let P be a Lyapunov matrix, and satisfy P i >0, and It is a uniformly continuous bounded function, and satisfies

[0160] The seventh modeling module is used to construct an eighth objective model based on the aforementioned processing function and the first objective variable. This eighth objective model is used to update the second objective variable, which is an estimate of the first objective variable.

[0161]

[0162] Where, r n It is a constant, and r n >0, The second objective variable mentioned above;

[0163] The fourth determination module is used to determine the difference between the first target variable and the second target variable as the third target variable:

[0164] in, The third objective variable is defined above, and the third objective variable is defined according to... renew;

[0165] The fifth determining module is used to determine the actuator fault based on the above-mentioned fault f. i (t) and the aforementioned residual signal r i (t) Determine the fault estimation error of the above fuzzy fault observer model. in,

[0166] The eighth modeling module is used to construct the third matrix.

[0167] The ninth modeling module is used to remodel the third objective model based on the above processing function, the third matrix, and the fault estimation error, to obtain the fifth objective model:

[0168]

[0169] To obtain the aforementioned target fault observer gain, in one optional implementation, the first computing unit includes:

[0170] The first calculation module is used to handle the aforementioned actuator failure f. i When (t) = 0, based on the Lyapunov matrix P mentioned above i The perturbation suppression performance H of the fifth objective model described above is constructed. ∞ Lyapunov function The first function is obtained;

[0171] The second calculation module is used to calculate the above-mentioned disturbance w. i When (t) = 0, based on the Lyapunov matrix P mentioned above i The perturbation suppression performance H of the fifth objective model described above is constructed. ∞ Lyapunov function: The second function is obtained;

[0172] The sixth determining module is used to determine that the verification is successful if the output values ​​of both the first function and the second function are greater than the second threshold.

[0173] The tenth modeling module, if the validation passes, is used to construct a nonlinear inequality matrix based on the fifth and second models described above:

[0174]

[0175] Among them, P i >0, matrix K ij =P i L ij Scalar δ1 > 0, scalar δ2 > 0, scalar θ1 > 0, * is a symmetric matrix term, and I is the identity matrix;

[0176] The first calculation module is used to solve for the Lyapunov matrix P mentioned above using the Matlab linear matrix solver. i And the above matrix K ij ;

[0177] The second calculation module is used to process the Lyapunov matrix P mentioned above. i And the above matrix K ij Substitute into the fourth matrix L ij =P i -1 K ij The gain of the target fault observer is obtained.

[0178] To achieve fault detection, in one optional implementation, the second computing unit includes:

[0179] The third calculation module is used to construct the fault determination function based on the sixth objective model mentioned above:

[0180] The seventh determination module is used when the output value J(t) of the above fault determination function is greater than the first threshold J. th In this case, it was determined that the aforementioned multi-robot interconnection system was faulty, among which,

[0181] The fault detection device of the aforementioned multi-robot interconnection system includes a processor and a memory. The first modeling unit, second modeling unit, third modeling unit, fourth modeling unit, first calculation unit, and second calculation unit are all stored as program units in the memory. The processor executes these program units stored in the memory to achieve the corresponding functions. All of the above modules are located in the same processor; alternatively, the modules may be located in different processors in any combination.

[0182] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and the computational efficiency of prediction can be improved by adjusting kernel parameters.

[0183] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0184] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the fault detection method of the multi-robot interconnection system.

[0185] This invention provides a processor for running a program, wherein the program executes the fault detection method of the multi-robot interconnection system.

[0186] This invention provides a multi-robot interconnection system, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements at least the steps of a fault detection method for the multi-robot interconnection system.

[0187] This application also provides a computer program product that, when executed on a data processing device, is adapted to perform the steps of initializing a fault detection method for a system with at least a multi-robot interconnection.

[0188] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0189] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0190] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0191] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0192] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0193] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0194] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0195] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0196] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0197] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0198] 1) The fault detection method for a multi-robot interconnected system of this application firstly models the multi-robot interconnected system based on an interconnected nonlinear system model to obtain a first target model. The first target model includes multiple second target models, each of which is an interconnected nonlinear system model corresponding to each robot. Then, a fuzzy fault observer model is constructed based on the second target models to obtain a third target model. Next, the difference between the second target model and the third target model is determined as a first estimation error. Based on the first estimation error and the third target model, the second target model is remodeled to obtain a fourth target model. Finally, based on the fourth target model and the third target model, the nonlinear interconnection terms in the model are determined. Based on the boundary constraints of the aforementioned nonlinear interconnection terms, a first target variable is determined. Based on this first target variable, the first estimation error and residual signal of the aforementioned third target model are remodeled to obtain a fifth target model. Then, the fifth target model is verified using a Lyapunov function. If the verification is successful, multiple nonlinear inequality matrices are constructed based on the fifth and second models. These nonlinear inequalities are solved to obtain the target fault observer gain. Finally, the fifth target model is updated based on the target fault observer gain to obtain a sixth target model. A fault determination function is constructed based on the sixth target model. If the output value of the fault determination function is greater than a first threshold, a fault is determined in the multi-robot interconnection system. This application introduces an adaptive mechanism. Based on the modeling, specific variables are extracted through boundary constraints of the nonlinear interconnection terms, and the processing function of the nonlinear interconnection terms is reconstructed. The reconstructed processing function can directly solve for the fault observer gain. Compared with the existing method of scaling nonlinear interconnection terms to generate inequality matrices, this application reduces the number of inequality matrices and solves the problem of high computational complexity in fault detection of multi-robot interconnection systems in the prior art.

[0199] 2) The fault detection device for the multi-robot interconnection system of this application comprises: a first modeling unit modeling the multi-robot interconnection system based on an interconnection nonlinear system model to obtain a first target model, wherein the first target model includes multiple second target models, each of which is an interconnection nonlinear system model corresponding to each robot; a second modeling unit constructing a fuzzy fault observer model based on the second target models to obtain a third target model; a third modeling unit determining the difference between the second target model and the third target model as a first estimation error, and remodeling the second target model based on the first estimation error and the third target model to obtain a fourth target model; and a fourth modeling unit determining the nonlinear interconnection in the model based on the fourth target model and the third target model. The first objective variable is determined by boundary constraints of the aforementioned nonlinear interconnection terms. Based on the first objective variable, the first estimation error and residual signal of the aforementioned third objective model are remodeled to obtain the fifth objective model. The first calculation unit verifies the fifth objective model using a Lyapunov function. If the verification is successful, multiple nonlinear inequality matrices are constructed based on the fifth and second models, and the nonlinear inequalities are solved to obtain the target fault observer gain. The second calculation unit updates the fifth objective model based on the target fault observer gain to obtain the sixth objective model, and constructs a fault determination function based on the sixth objective model. If the output value of the fault determination function is greater than a first threshold, the multi-robot interconnection system is determined to have a fault. This application introduces an adaptive mechanism. Based on the modeling, specific variables are extracted by boundary constraints of the nonlinear interconnection terms, and the processing function of the nonlinear interconnection terms is reconstructed. The fault observer gain can be directly solved using the reconstructed processing function. Compared with the prior art method of scaling nonlinear interconnection terms to generate inequality matrices, this application reduces the number of inequality matrices and solves the problem of high computational complexity for fault detection in multi-robot interconnection systems in the prior art.

[0200] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A fault detection method for a multi-robot interconnection system, characterized in that, include: A first target model is obtained by modeling a multi-robot interconnection system based on an interconnection nonlinear system model. The first target model includes multiple second target models, and each second target model is the interconnection nonlinear system model corresponding to each robot. Based on the second objective model, a fuzzy fault observer model is constructed to obtain the third objective model; The difference between the second target model and the third target model is determined as the first estimation error, and the second target model is remodeled based on the first estimation error and the third target model to obtain the fourth target model; Based on the fourth objective model and the third objective model, the nonlinear interconnection term in the model is determined, and the first objective variable is determined based on the boundary constraints of the nonlinear interconnection term. Based on the first objective variable, the first estimation error and residual signal of the third objective model are remodeled to obtain the fifth objective model. The fifth objective model is verified using the Lyapunov function. If the verification is successful, multiple nonlinear inequality matrices are constructed based on the fifth objective model and the second objective model. The nonlinear inequalities are solved to obtain the target fault observer gain. The fifth target model is updated based on the gain of the target fault observer to obtain the sixth target model, and a fault determination function is constructed based on the sixth target model. If the output value of the fault determination function is greater than the first threshold, it is determined that the multi-robot interconnection system has a fault.

2. The method according to claim 1, characterized in that, A first objective model is obtained by modeling a multi-robot interconnected system based on an interconnected nonlinear system model. This first objective model includes multiple second objective models, including: Each robot in the multi-robot interconnection system is modeled and represented in a compact set including the origin to obtain the seventh target model: Where, x i (t) represents the state of robot i. For x i The derivative of (t), i = 1, 2, ..., N, where N is the number of robots included in the multi-robot interconnection system, j = 1, 2, ..., r i r i Let t be the total number of fuzzy rules for robot i, υ(t) be the antecedent variable, and w be the total number of fuzzy rules for robot i. i (t) and f i (t) represents the disturbance and actuator failure of robot i, respectively, and w i (t) and f i (t) Energy is bounded, satisfying and B ij E ij E ij and C i Let y be the system matrix. i (t) The measurement output of robot i, g ni (x n (t) represents the nonlinear interconnection term, g ni (x n (t) satisfies ||g ni (x n (t))‖≤α ni ||x n (t)‖,α ni h is an unknown constant term. ij (υ i (t) is the membership function, h ij (υ i (t) satisfies h ij (υ i (t)≥0 and The system matrix in the seventh objective model Decomposition yields the first matrix and the second matrix. Among them, A i0 Let A be the constant term in the i-th row. ij To exclude A i0 Other parts: Based on the first matrix and the second matrix, the seventh objective is represented to obtain the second objective model: Among them, A i0 satisfy A ih satisfy B ih satisfy E ih satisfy 3. The method according to claim 2, characterized in that, Based on the second objective model, a fuzzy fault observer model is constructed to obtain the third objective model, which includes: The fuzzy fault observer model is constructed based on the second objective model: in, satisfy satisfy For x i The estimated value of (t), For y i The estimated value of (t), r is an estimate of υ(t). i (t) is the residual signal, L ij The gain of the fault detection observer, For g ni (x n The processing function for (t)).

4. The method according to claim 3, characterized in that, The difference between the second target model and the third target model is determined as the first estimation error. Based on the first estimation error and the third target model, the second target model is remodeled to obtain the fourth target model, which includes: The difference between the second target model and the third target model is determined as the first estimation error: Where e i (t) represents the first estimation error; Based on the first estimation error and the third objective model, the second objective model is remodeled to obtain the fourth objective model:

5. The method according to claim 4, characterized in that, Based on the fourth objective model and the third objective model, nonlinear interconnection terms are determined in the model, and a first objective variable is determined based on the boundary constraints of the nonlinear interconnection terms. Based on the first objective variable, the first estimation error and residual signal of the third objective model are remodeled to obtain a fifth objective model, including: The corresponding first boundary constraint is determined based on the nonlinear interconnection term: Where, η n >0 and η n For constant terms; The first target variable is determined based on the first boundary constraint. Based on the first objective variable representing the first boundary constraint, the second boundary constraint is obtained: The processing function is reconstructed based on the second boundary constraint. Among them, P i Let P be a Lyapunov matrix, and satisfy P i >0, and It is a uniformly continuous bounded function, and satisfies An eighth objective model is constructed based on the processing function and the first objective variable. The eighth objective model is used to update the second objective variable, which is an estimate of the first objective variable. Where, r n It is a constant, and r n >0, The second target variable; The difference between the first target variable and the second target variable is determined as the third target variable: in, The third target variable is defined as follows: renew; Based on the actuator fault f i (t) and the residual signal r i (t) Determine the fault estimation error of the fuzzy fault observer model. in, Constructing the third matrix The third target model is remodeled based on the processing function, the third matrix, and the fault estimation error to obtain the fifth target model:

6. The method according to claim 5, characterized in that, The fifth objective model is validated using a Lyapunov function. If the validation passes, multiple nonlinear inequality matrices are constructed based on the fifth and second objective models. Solving these nonlinear inequalities yields the target fault observer gain, including: In the actuator fault f i When (t) = 0, based on the Lyapunov matrix P i Constructing the perturbation suppression performance H of the fifth target model ∞ Lyapunov function The first function is obtained; In the disturbance w i When (t) = 0, based on the Lyapunov matrix P i Constructing the perturbation suppression performance H of the fifth target model ∞ Lyapunov function: The second function is obtained; If the output values ​​of both the first function and the second function are greater than the second threshold, the verification is deemed successful. If the verification passes, construct a nonlinear inequality matrix based on the fifth objective model and the second objective model: Among them, P i >0, matrix K ij =P i L ij Scalar δ1 > 0, scalar δ2 > 0, scalar θ1 > 0, * is a symmetric matrix term, and I is the identity matrix; The Lyapunov matrix P is obtained by solving the problem using the Matlab linear matrix solver. i and the matrix K ij ; The Lyapunov matrix P i and the matrix K ij Substitute into the fourth matrix L ij =P i -1 K ij The gain of the target fault observer is obtained.

7. The method according to claim 6, characterized in that, A fault determination function is constructed based on the sixth target model. If the output value of the fault determination function is greater than a first threshold, a fault is determined in the multi-robot interconnection system, including: Construct a fault determination function based on the sixth objective model: When the output value J(t) of the fault determination function is greater than the first threshold J th In the case of a fault in the multi-robot interconnection system, wherein, 8. A fault detection device for a multi-robot interconnection system, characterized in that, The device includes: The first modeling unit is used to model the multi-robot interconnection system based on the interconnection nonlinear system model to obtain a first target model. The first target model includes multiple second target models, and the second target model is the interconnection nonlinear system model corresponding to each robot. The second modeling unit is used to construct a fuzzy fault observer model based on the second target model to obtain the third target model; The third modeling unit is used to determine the difference between the second target model and the third target model as the first estimation error, and to remodel the second target model based on the first estimation error and the third target model to obtain the fourth target model; The fourth modeling unit is used to determine the nonlinear interconnection term in the model based on the fourth target model and the third target model, and to determine the first target variable based on the boundary constraints of the nonlinear interconnection term. Based on the first target variable, the first estimation error and residual signal of the third target model are remodeled to obtain the fifth target model. The first computing unit is used to verify the fifth target model using the Lyapunov function. If the verification is successful, it constructs multiple nonlinear inequality matrices based on the fifth target model and the second target model, solves the nonlinear inequalities, and obtains the target fault observer gain. The second calculation unit is used to update the fifth target model based on the target fault observer gain to obtain a sixth target model, and to construct a fault determination function based on the sixth target model. If the output value of the fault determination function is greater than a first threshold, the multi-robot interconnection system is determined to have a fault.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 7.

10. A multi-robot interconnection system, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising methods for performing any one of claims 1 to 7.

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