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

Abstract

The invention provides a fault detection method and a fault detection device for a multi-robot interconnection system, and the method comprises the steps: constructing a difference value between a second target model and a third target model for fault observation based on a first target model of system modeling, and determining the difference value as a first estimation error, remodeling the third target model based on the first estimation error to obtain a fourth target model; determining a first target variable based on the nonlinear interconnection item of the model, and remodeling the third target model by the first target variable to obtain a fifth target model; constructing a nonlinear inequality matrix on the basis of the model, and solving the nonlinear inequality matrix to obtain the gain of the target fault observer; and updating the fifth target model based on the gain of the target fault observer, and determining that the multi-robot interconnection system has a fault under the condition that the output value of the fault judgment function is greater than a first threshold value. According to the method, the problem of high fault detection calculation complexity of the multi-robot interconnection system in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of fault detection technology, and in particular to a fault detection method, a fault detection device, a computer-readable storage medium and a multi-robot interconnection system. Background Art

[0002] The actual robot system has an increasingly high demand for safety, and it is necessary to conduct research on the fault detection method of the robot control system so that faults in the robot system can be detected in a timely and accurate manner. In addition, with the development of automatic control technology, a single robot control system can no longer meet actual needs, and multiple robots are required to be interconnected. Interconnection is an important feature of a multi-robot system, which refers to the effective cooperation between multiple robots rather than a simple stacking. Therefore, it is of great significance to study the fault detection problem of a multi-robot interconnected system. From the perspective of the interconnection relationship in the robot control system, the structure of the robot group is mainly divided into centralized and distributed. So far, a large number of effective centralized fault detection methods have been proposed in existing invention patents. However, it should be noted that in actual multi-robot systems, due to the limitations of computing power and communication bandwidth, fault detection in a centralized architecture is usually difficult.

[0003] Therefore, in order to solve the above problems, the "decentralized control" method is proposed in the prior art, and then a large number of fault detection schemes are developed based on the decentralized fault detection method. It is worth noting that most of the research on decentralized fault detection methods focuses on linear multi-robot interconnected systems or Lipschitz nonlinear multi-robot interconnected systems, while the decentralized fault detection method of more general nonlinear multi-robot interconnected systems needs further research.

[0004] It should be noted that in the prior art, the interconnection satisfies the Lipschitz condition and processes the related interconnection terms through the linear matrix inequality technology. The above processing method imposes additional restrictions on the Lyapunov matrix, resulting in a large number of linear matrix inequalities in the design conditions, resulting in high computational complexity and low monitoring efficiency. Summary of the invention

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

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present 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, wherein the first target model includes multiple second target models, and the second target model is the interconnected nonlinear system model corresponding to each of the robots; constructing a fuzzy fault observer model based on the second target model to obtain a third target model; determining the difference between the second target model and the third target model as a first estimated error, and re-modeling the second target model based on the first estimated error and the third target model to obtain a fourth target model; determining the fault in the model based on the fourth target model and the third target model. A nonlinear interconnected term is determined, and a first target variable is determined based on the boundary limit of the nonlinear interconnected term, and the first estimation error and residual signal of the third target model are remodeled based on the first target variable to obtain a fifth target model; based on the fifth target model, verification is performed through a Lyapunov function, and when the verification passes, multiple nonlinear inequality matrices are constructed based on the fifth model and the second model, and the nonlinear inequality is solved to obtain a target fault observer gain; based on the target fault observer gain, the fifth target model is updated to obtain a sixth target model, and a fault judgment function is constructed based on the sixth target model, and when the output value of the fault judgment function is greater than a first threshold, it is determined that there is a fault in the multi-robot interconnection system.

[0007] Optionally, a multi-robot interconnected system is modeled based on an interconnected nonlinear system model to obtain a first target model, wherein the first target model includes a plurality of second target models, including: modeling each of the robots in the multi-robot interconnected system and representing them in a tight set including an origin to obtain a seventh target model: Among them, x i (t) is the state of the robot i, For x i The derivative of (t), i = 1, 2, ..., N, N is the number of robots included in the multi-robot interconnection system, j = 1, 2, ..., r i , r i is the total number of fuzzy rules for robot i, υ(t) is the antecedent variable, w i (t) and f i (t) are 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 、Eij and C i is the system matrix, y i (t) The measured output of the robot i, g ni (x n (t)) is the nonlinear interconnection term, g ni (x n (t)) satisfies ‖g ni (x n (t))‖≤α ni ‖x n (t)‖,α ni is the unknown constant term, h ij ( i (t)) is the membership function, h ij ( i (t)) satisfies h ij ( i (t))≥0 and The system matrix in the seventh target model Decompose to get the first matrix and the second matrix Among them, A i0 is the constant term of the i-th row, A ij To remove A i0 Part other than: the seventh target is represented based on the first matrix and the second matrix to obtain the second target model: Among them, A i0 satisfy A ih satisfy B ih satisfy E ih satisfy

[0008] Optionally, constructing a fuzzy fault observer model based on the second target model to obtain a third target model includes: 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), is the estimated value of υ(t), r i (t) is the residual signal, L ij is the gain of the fault detection observer, g ni (x n(t)) processing function.

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

[0010]

[0011] Optionally, determining a nonlinear interconnected term in a model based on the fourth target model and the third target model, determining a first target variable based on a boundary limit of the nonlinear interconnected term, and remodeling the first estimation error and residual signal of the third target model based on the first target variable to obtain a fifth target model includes: determining a corresponding first boundary limit based on the nonlinear interconnected term: Among them, η n >0 and η n is a constant term; determining the first target variable based on the first boundary limit The first boundary limit is expressed based on the first target variable, and a second boundary limit is obtained: Reconstruct the processing function based on the second boundary limit Among them, P i represents a Lyapunov matrix, and satisfies P i >0, and is a uniformly continuous bounded function that satisfies An eighth target model is constructed based on the processing function and the first target variable, and the eighth target model is used to update a second target variable, where the second target variable is an estimated value of the first target variable: Among them, r n is a constant, and r n >0, is the second target variable; and the difference between the first target variable and the second target variable is determined as the third target variable: in, is the third target variable, and the third target variable is based on Update; based on the actuator failure f i (t) and the residual signal ri (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, verifying the fifth target model through a Lyapunov function, constructing multiple nonlinear inequality matrices based on the fifth model and the second model if the verification passes, solving the nonlinear inequality to obtain a target fault observer gain, including: i When (t) = 0, based on the Lyapunov matrix P i Construct the fifth target model disturbance suppression performance H ∞ Lyapunov function Get the first function; in the disturbance w i When (t) = 0, based on the Lyapunov matrix P i Construct the fifth target model disturbance suppression performance H ∞ The Lyapunov function of : A second function is obtained; when the output values ​​of the first function and the second function are both greater than a second threshold, it is determined that the verification is passed; when the verification is passed, 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 δ 1 >0, scalar δ 2 >0, scalar θ 1 > 0, * is a symmetric matrix term, I is the unit matrix; solve it through the Matlab linear matrix solver to obtain the Lyapunov matrix P 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 , and obtain the target fault observer gain.

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

[0015]

[0016] According to another aspect of the present application, a fault detection device for a multi-robot interconnected system is provided, the device comprising: a first modeling unit, for modeling the multi-robot interconnected system based on an interconnected nonlinear system model to obtain a first target model, wherein the first target model comprises a plurality of second target models, wherein the second target models are the interconnected nonlinear system models corresponding to each of the robots; a second modeling unit, for constructing a fuzzy fault observer model based on the second target model to obtain a third target model; a third modeling unit, for determining a difference between the second target model and the third target model as a first estimation error, and re-modeling 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, for constructing a fuzzy fault observer model based on the fourth target model and the third target model. The invention relates to a method for determining a nonlinear interconnected term in a model by a type, and determining a first target variable based on the boundary limit of the nonlinear interconnected term, remodeling the first estimation error and residual signal of the third target model based on the first target variable to obtain a fifth target model; a first calculation unit is used to verify the fifth target model through a Lyapunov function, and if the verification passes, a plurality of nonlinear inequality matrices are constructed based on the fifth model and the second model, and the nonlinear inequality is solved to obtain a target fault observer gain; a 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 construct a fault judgment function based on the sixth target model, and if the output value of the fault judgment function is greater than a first threshold, it is determined that there is a fault in the multi-robot interconnection system.

[0017] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0018] According to another aspect of the present 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 are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the methods described.

[0019] Applying the technical solution of the present application, in the fault detection method of the multi-robot interconnected system, first, the multi-robot interconnected system is modeled based on the interconnected nonlinear system model to obtain a first target model, wherein the first target model includes multiple second target models, and the second target model is the interconnected nonlinear system model corresponding to each of the above robots; then, a fuzzy fault observer model is constructed based on the second target model to obtain a third target model; then, 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 re-modeled based on the first estimation error and the third target model to obtain a fourth target model; then, the nonlinearity in the model is determined based on the fourth target model and the third target model. The invention discloses a method for solving the problem of fault observer gain in a multi-robot interconnected system. The invention discloses a method for solving the problem of fault observer gain in a multi-robot interconnected system. The method ... BRIEF DESCRIPTION OF THE DRAWINGS

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

[0021] Figure 2A schematic flow chart of a fault detection method for a multi-robot interconnected system provided according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0022] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work 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 and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] As introduced in the background technology, the interconnection in the prior art satisfies the Lipschitz condition and processes related interconnection terms through linear matrix inequality technology. The above processing method imposes additional restrictions on the Lyapunov matrix, resulting in a large number of linear matrix inequalities in the design conditions, resulting in high computational complexity and low monitoring efficiency. In order to solve the problem of high computational complexity of fault detection in multi-robot interconnection systems in the prior art, an embodiment of the present application provides a fault detection method, a fault detection device, a computer-readable storage medium and a multi-robot interconnection system for a multi-robot interconnection system.

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

[0027] In this embodiment, a fault detection method for a multi-robot interconnected system running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of 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 can be executed in an order different from that shown here.

[0028] Figure 1 FIG. 1 is a flow chart of a fault detection method for a multi-robot interconnected system according to an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0029] Step S201, modeling a multi-robot interconnected system based on an interconnected nonlinear system model to obtain a first target model, wherein the first target model includes a plurality of second target models, and the second target models are the interconnected nonlinear system models corresponding to the respective robots;

[0030] Specifically, a dynamic mathematical model of the multi-robot interconnected system is established to obtain the first target model. In the above process, the multi-robot interconnected system is modeled as a complex interconnected nonlinear system with uncertainty. It can be understood that the first target model can be expressed as a large interconnected fuzzy system S composed of multiple subsystems in a compact set θ xi It is accurately represented in, that is, represented by multiple of the above-mentioned second target models.

[0031] Step S202, constructing a fuzzy fault observer model based on the second target model to obtain a third target model;

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

[0033] Step S203, 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;

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

[0035] Step S204, determining a nonlinear interconnected term in the model based on the fourth target model and the third target model, determining a first target variable based on the boundary limit of the nonlinear interconnected term, and remodeling the first estimation error and residual signal of the third target model based on the first target variable to obtain a fifth target model;

[0036] Specifically, the unknown variables are redefined based on the nonlinear interconnected terms in the fourth target model, and adaptively updated based on the unknown variables, and the third target model is remodeled to obtain the fifth target model.

[0037] Step S205, verifying the fifth target model through the Lyapunov function, constructing multiple nonlinear inequality matrices based on the fifth model and the second model if the verification is passed, solving the nonlinear inequalities, and obtaining the target fault observer gain;

[0038] Specifically, based on the fifth target model, a nonlinear inequality matrix is ​​directly constructed and solved to obtain the fault observer gain. Compared with the prior art, the present application does not need to impose additional inequality restrictions on the Lyapunov matrix and can directly solve it, thereby omitting a large number of nonlinear inequalities and reducing the computational complexity.

[0039] Step S206: Based on the target fault observer gain, the fifth target model is updated to obtain a sixth target model, and a fault judgment function is constructed based on the sixth target model. When the output value of the fault judgment function is greater than a first threshold, it is determined that a fault exists in the multi-robot interconnection system.

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

[0041] Through this embodiment, first, a multi-robot interconnected system is modeled based on an interconnected nonlinear system model to obtain a first target model, wherein the first target model includes multiple second target models, and the second target model is the interconnected nonlinear system model corresponding to each of the robots; then, a fuzzy fault observer model is constructed based on the second target model to obtain a third target model; then, a 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; then, a nonlinear interconnected term in the model is determined based on the fourth target model and the third target model, and a fuzzy fault observer model is constructed based on the nonlinear error. The boundary restriction of the interconnected term determines the first target variable, and the first estimation error and residual signal of the third target model are remodeled based on the first target variable to obtain the fifth target model; then, the fifth target model is verified by the Lyapunov function, and multiple nonlinear inequality matrices are constructed based on the fifth model and the second model when the verification is passed, and the nonlinear inequality is solved to obtain 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, and a fault judgment function is constructed based on the sixth target model. When the output value of the fault judgment function is greater than the first threshold, it is determined that the multi-robot interconnected system has a fault. The present application introduces an adaptive mechanism, which extracts specific variables through the boundary restriction of the nonlinear interconnected term on the basis of modeling, reconstructs the processing function of the nonlinear interconnected term, and directly solves the fault observer gain through the reconstructed processing function. Compared with the method of scaling the nonlinear interconnected term to generate an inequality matrix in the prior art, the present application reduces the number of inequality matrices and solves the problem of high computational complexity of fault detection of the multi-robot interconnected system in the prior art.

[0042] In order to construct the second target model, in an optional implementation, the step S201 includes:

[0043] Step S2011, modeling each of the above robots in the multi-robot interconnected system, and representing them in a tight set including the origin, to obtain a seventh target model:

[0044]

[0045] Among them, x i (t) is the state of the robot i, For x i The derivative of (t), i = 1, 2, ..., N, N is the number of the above-mentioned robots included in the above-mentioned multi-robot interconnection system, j = 1, 2, ..., r i , ri is the total number of fuzzy rules for robot i, υ(t) is the antecedent variable, w i (t) and f i (t) are 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 is the system matrix, y i (t) The measured output of the above robot i, g ni (x n (t)) is the above nonlinear interconnection term, g ni (x n (t)) satisfies ‖g ni (x n (t))‖≤α ni ‖x n (t)‖,α ni is the unknown constant term, h ij ( i (t)) is the membership function, h ij ( i (t)) satisfies h ij ( i (t))≥0 and

[0046] Specifically, the initial model of the i-th second target model in the first target model can be expressed as:

[0047]

[0048] And the above second target model satisfies the above restrictions.

[0049] Step S2012: convert the system matrix in the seventh target model Decompose to get the first matrix and the second matrix Among them, A i0 is the constant term of the i-th row, A ij To remove A i0 Other parts:

[0050] Specifically, in order to facilitate the subsequent Lyapunov matrix representation and calculation, this application sets the above system matrix Decompose into Among them, A i0 is the constant term of the i-th row, A ij To remove Ai0 outside the part.

[0051] Step S2013, representing the seventh target based on the first matrix and the second matrix to obtain the second target model:

[0052]

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

[0054] Specifically, based on the above decomposed system matrix, the above seventh target model is re-expressed to obtain the above second target model:

[0055]

[0056] In order to construct the third target model, in an optional implementation, the 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), is the estimated value of υ(t), r i (t) is the residual signal, L ij is the gain of the fault detection observer, g ni (x n (t)) processing function.

[0060] Specifically, for the i-th first target model, the third target model constructed is:

[0061]

[0062] It can be understood that the above processing function is only a definition here, which is used to process unknown nonlinear interconnected terms, and the gain of the above fault observer is only a definition, and no specific solution formula is given here.

[0063] In order to construct the above processing function, in an optional implementation, the above step S203 includes:

[0064] Step S2031: determine the difference between the second target model and the third target model as the first estimation error: where e i (t) is the first estimation error mentioned above;

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

[0066] Step S2032: remodel the second target model based on the first estimation error and the third target model to obtain a fourth target model:

[0067]

[0068] Then, the i-th interconnected augmented robot system is remodeled according to the i-th second target model and the third target model:

[0069]

[0070] In order to construct the above processing function, in an optional implementation, the above step S204 includes:

[0071] Step S2041, determining the corresponding first boundary limit based on the above nonlinear interconnection term:

[0072] Among them, η n >0 and η n is a constant term;

[0073] Specifically, based on the representation of the fourth objective model, restrictions can be made on the nonlinear interconnected terms:

[0074]

[0075] Among them, η n is a constant term and η n >0, that is, η n Any constant has no effect on the subsequent solution of the model.

[0076] Step S2042, determining the first target variable based on the first boundary restriction

[0077] Specifically, based on the fourth target model and the first boundary restriction, a new unknown variable is introduced: That is the first target variable mentioned above.

[0078] Step S2043, based on the first target variable representing the first boundary limit, a second boundary limit is obtained:

[0079]

[0080] Specifically, based on the definition of the first objective variable, the first boundary restriction is re-expressed as:

[0081]

[0082] Step S2044: reconstruct the processing function based on the second boundary restriction

[0083]

[0084] Among them, P i represents a Lyapunov matrix, and satisfies P i >0, and is a uniformly continuous bounded function that satisfies

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

[0086]

[0087] Step S2045: construct an eighth target model based on the processing function and the first target variable. The eighth target model is used to update the second target variable. The second target variable is an estimated value of the first target variable:

[0088]

[0089] Among them, r n is a constant, and r n >0, is the second target variable mentioned above;

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

[0091] Step S2046: determine the difference between the first target variable and the second target variable as the third target variable:

[0092] in, is the third target variable, and the third target variable is based on renew;

[0093] Specifically, the corresponding estimation error defined based on the first objective variable and the second objective variable is: Then construct the adaptive update formula of the above estimation error

[0094] It can be understood 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 interconnected terms through nonlinear inequalities as in the prior art.

[0095] Step S2047, based on the above actuator fault f i (t) and the above 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 fuzzy fault observer model is reconstructed as in That is, a matrix of length m.

[0097] Step S2048, construct the third matrix

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

[0099] Step S2049: remodel the third target model based on the processing function, the third matrix and the fault estimation error to obtain the fifth target model:

[0100]

[0101] Specifically, based on the fault estimation error and the third matrix, the third target model is remodeled to obtain the fifth target model of the i-th target in the new multi-robot interconnected system:

[0102]

[0103] In order to obtain the target fault observer gain, in an optional implementation, the step S205 includes:

[0104] Step S2051, when the actuator fails i When (t) = 0, based on the above Lyapunov matrix P i Construct the disturbance suppression performance H of the fifth target model mentioned above ∞ Lyapunov function Get the first function;

[0105] Specifically, in the above actuator fault f i When (t) = 0, the H of the Lyapunov function analysis system is established. ∞ Disturbance rejection performance, Lyapunov function V i (t) are as follows:

[0106]

[0107] Step S2052, in the above disturbance w i When (t) = 0, based on the above Lyapunov matrix P i Construct the disturbance suppression performance H of the fifth target model mentioned above ∞ The Lyapunov function of : Get the second function;

[0108] Specifically, in the above perturbation w i When (t) = 0, the H of the Lyapunov function analysis system is established. ∞ Disturbance rejection performance, Lyapunov function V fi (t) are as follows:

[0109]

[0110] In the above embodiment, the performance of the target fuzzy fault observer is verified through the above two verifications. When the verification is passed, the gain is further solved to obtain the final target fuzzy fault observer.

[0111] Step S2053, when the output values ​​of the first function and the second function are both greater than the second threshold, determining that the verification is passed;

[0112] Step S2054: if the verification is passed, construct a nonlinear inequality matrix based on the fifth model and the second model:

[0113]

[0114] Among them, P i >0, matrix K ij =P i L ij , scalar δ 1 >0, scalar δ2 >0, scalar θ 1 >0, * is a symmetric matrix item, I is the identity matrix;

[0115] Specifically, if there exists a matrix P i >0, matrix K ij =P i L ij , scalar δ 1 >0 and scalar δ 2 >0, and the given constant matrix M ij >0 and scalar θ 1 >0, and the above inequality is satisfied, the robot interconnection system can achieve stability and achieve ideal fault monitoring performance.

[0116] Step S2055, solve the problem by using Matlab linear matrix solver, and obtain the above Lyapunov matrix P i And the above matrix K ij ;

[0117] Specifically, based on the above inequality, the gain of the fuzzy fault observer is solved, that is, according to the matrix K ij =P i L ij , determine the matrix L ij =P i -1 K ij , we can see that for the above Lyapunov matrix P i And the above matrix K ij Keep twisting to find the solution.

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

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

[0120] In order to implement fault detection, in an optional implementation, the above step S206 includes:

[0121] Step S2061: construct a fault judgment function based on the sixth target model:

[0122] Specifically, based on the sixth target model, the fault judgment function is constructed according to the evaluation mechanism function:

[0123] Step S2062: when the output value J(t) of the fault judgment function is greater than the first threshold value J th In the case of , it is determined that the multi-robot interconnection system has a fault, where:

[0124] Specifically, according to the above fault judgment function, its threshold is set as Where sup represents the supremum. th When a fault occurs, J(t)≤J th Indicates that no fault has occurred.

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

[0126] The embodiment of the present application also provides a fault detection device for a multi-robot interconnected system. It should be noted that the fault detection device for a multi-robot interconnected system of the embodiment of the present application can be used to execute the fault detection method for a multi-robot interconnected system provided by the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0127] The following is an introduction to the fault detection device of the multi-robot interconnection system provided in an embodiment of the present application.

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

[0129] A first modeling unit 10 is used to model the multi-robot interconnected system based on an interconnected nonlinear system model to obtain a first target model, wherein the first target model includes a plurality of second target models, and the second target models are the interconnected nonlinear system models corresponding to the respective robots;

[0130] A second modeling unit 20 is used to construct a fuzzy fault observer model based on the second target model to obtain a third target model;

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

[0132] a fourth modeling unit 40, configured to determine a nonlinear interconnected term in a model based on the fourth target model and the third target model, determine a first target variable based on a boundary restriction of the nonlinear interconnected term, and remodel the first estimation error and residual signal of the third target model based on the first target variable to obtain a fifth target model;

[0133] A first calculation unit 50 is used to verify the fifth target model through the Lyapunov function, and if the verification is passed, a plurality of nonlinear inequality matrices are constructed based on the fifth model and the second model, and the nonlinear inequality is solved to obtain a target fault observer gain;

[0134] The second calculation unit 60 is used to update the above-mentioned fifth target model based on the above-mentioned target fault observer gain to obtain a sixth target model, and to construct a fault judgment function based on the above-mentioned sixth target model. When the output value of the above-mentioned fault judgment function is greater than the first threshold, it is determined that there is a fault in the above-mentioned multi-robot interconnection system.

[0135] Through this embodiment, the first modeling unit models the multi-robot interconnected system based on the interconnected nonlinear system model to obtain a first target model, wherein the first target model includes multiple second target models, and the second target model is the interconnected nonlinear system model corresponding to each of the above robots; the second modeling unit constructs a fuzzy fault observer model based on the second target model to obtain a third target model; the third modeling unit determines the difference between the second target model and the third target model as a first estimation error, and re-models 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 interconnected terms in the model based on the fourth target model and the third target model, and re-models the second target model based on the fourth target model and the third target model to obtain a fourth target model. The first target variable is determined by the boundary restriction of the nonlinear interconnected term, and the first estimation error and residual signal of the third target model are remodeled based on the first target variable to obtain the fifth target model; the first calculation unit verifies the fifth target model through the Lyapunov function, and constructs multiple nonlinear inequality matrices based on the fifth model and the second model when the verification is passed, and solves the nonlinear inequality to obtain the target fault observer gain; the second calculation unit updates the fifth target model based on the target fault observer gain to obtain the sixth target model, and constructs a fault judgment function based on the sixth target model, and determines that the multi-robot interconnected system has a fault when the output value of the fault judgment function is greater than the first threshold. The present application introduces an adaptive mechanism, which extracts specific variables from the nonlinear interconnected term through the boundary restriction on the basis of modeling, reconstructs the processing function of the nonlinear interconnected term, and directly solves the fault observer gain through the reconstructed processing function. Compared with the method of scaling the nonlinear interconnected term to generate an inequality matrix in the prior art, the present application reduces the number of inequality matrices and solves the problem of high computational complexity of fault detection of the multi-robot interconnected system in the prior art.

[0136] In order to construct the second target model, in an optional implementation, the first modeling unit includes:

[0137] The first modeling module is used to model each of the above robots in the multi-robot interconnected system and represent them in a tight set including the origin to obtain a seventh target model:

[0138]

[0139] Among them, x i (t) is the state of the robot i, For x i The derivative of (t), i = 1, 2, ..., N, N is the number of the above-mentioned robots included in the above-mentioned multi-robot interconnection system, j = 1, 2, ..., ri , r i is the total number of fuzzy rules for robot i, υ(t) is the antecedent variable, w i (t) and f i (t) are 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 is the system matrix, y i (t) The measured output of the above robot i, g ni (x n (t)) is the above nonlinear interconnection term, g ni (x n (t)) satisfies ‖g ni (x n (t))‖≤α ni ‖x n (t)‖,α ni is the unknown constant term, h ij ( i (t)) is the membership function, h ij ( i (t)) satisfies h ij ( i (t))≥0 and

[0140] Decomposition module, used to convert the above system matrix in the seventh target model Decompose to get the first matrix and the second matrix Among them, A i0 is the constant term of the i-th row, A ij To remove A i0 Other parts:

[0141] The second modeling module is used to represent the seventh target based on the first matrix and the second matrix to obtain the second target model:

[0142]

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

[0144] In order to construct the third target model, in an optional implementation, the second modeling unit includes:

[0145] A 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), is the estimated value of υ(t), r i (t) is the residual signal, L ij is the gain of the fault detection observer, g ni (x n (t)) processing function.

[0148] In order to construct the above processing function, in an optional implementation, the above third modeling unit includes:

[0149] A 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) is the first estimation error mentioned above;

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

[0151]

[0152] In order to construct the above processing function, in an optional implementation, the fourth modeling unit includes:

[0153] The second determination module is used to determine the corresponding first boundary limit based on the above nonlinear interconnection term:

[0154] Among them, η n >0 and η n is a constant term;

[0155] A third determination module is used to determine the first target variable based on the first boundary restriction.

[0156] A fifth modeling module is used to express the first boundary constraint based on the first target variable to obtain a second boundary constraint:

[0157]

[0158] A sixth modeling module, for reconstructing the processing function based on the second boundary restriction

[0159] Among them, P i represents a Lyapunov matrix, and satisfies P i >0, and is a uniformly continuous bounded function that satisfies

[0160] A seventh modeling module is used to construct an eighth target model based on the processing function and the first target variable, wherein the eighth target model is used to update the second target variable, and the second target variable is an estimated value of the first target variable:

[0161]

[0162] Among them, r n is a constant, and r n >0, is the second target 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, is the third target variable, and the third target variable is based on renew;

[0165] The fifth determination module is used to determine the actuator fault f i (t) and the above 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] A ninth modeling module is used to remodel the third target model based on the processing function, the third matrix and the fault estimation error to obtain the fifth target model:

[0168]

[0169] In order to obtain the target fault observer gain, in an optional implementation, the first calculation unit includes:

[0170] The first calculation module is used for the above actuator fault f i When (t) = 0, based on the above Lyapunov matrix P i Construct the disturbance suppression performance H of the fifth target model mentioned above ∞ Lyapunov function Get the first function;

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

[0172] A sixth determination module, configured to determine that the verification is passed when the output values ​​of the first function and the second function are both greater than a second threshold;

[0173] The tenth modeling module is used to construct a nonlinear inequality matrix based on the fifth model and the second model when the verification is passed:

[0174]

[0175] Among them, P i >0, matrix K ij =P i L ij , scalar δ 1 >0, scalar δ 2 >0, scalar θ 1 >0, * is a symmetric matrix item, I is the identity matrix;

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

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

[0178] In order to implement fault detection, in an optional implementation manner, the second calculation unit includes:

[0179] The third calculation module is used to construct a fault judgment function based on the sixth target model:

[0180] The seventh determination module is used to determine the fault when the output value J(t) of the fault judgment function is greater than the first threshold value J th In the case of , it is determined that the multi-robot interconnection system has a fault, where:

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

[0182] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the calculation efficiency of the prediction can be improved by adjusting the kernel parameters.

[0183] The memory may include non-permanent memory in a computer-readable medium, 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] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the fault detection method of the multi-robot interconnection system.

[0185] An embodiment of the present invention provides a processor, which is used to run a program, wherein the fault detection method of the multi-robot interconnection system is executed when the program is running.

[0186] An embodiment of the present 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, at least the steps of a fault detection method for the multi-robot interconnection system are implemented.

[0187] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the steps of a fault detection method for a multi-robot interconnected system.

[0188] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0189] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0190] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0191] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0192] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions 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] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0195] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0196] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0197] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0198] 1) The fault detection method of the multi-robot interconnected system of the present application, first, the multi-robot interconnected system is modeled based on the interconnected 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 interconnected nonlinear system model corresponding to each of the robots; then, a fuzzy fault observer model is constructed based on the second target model to obtain a third target model; then, 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 re-modeled based on the first estimation error and the third target model to obtain a fourth target model; then, the nonlinear interconnected terms in the model are determined based on the fourth target model and the third target model. Based on the boundary restriction of the nonlinear interconnected term, the first target variable is determined, and the first estimation error and residual signal of the third target model are remodeled based on the first target variable to obtain the fifth target model; then, the fifth target model is verified by Lyapunov function, and multiple nonlinear inequality matrices are constructed based on the fifth model and the second model when the verification is passed, and the nonlinear inequality is solved to obtain 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, and a fault judgment function is constructed based on the sixth target model, and when the output value of the fault judgment function is greater than the first threshold, it is determined that the multi-robot interconnected system has a fault. The present application introduces an adaptive mechanism, and on the basis of modeling, the boundary restriction of the nonlinear interconnected term is used to extract specific variables, and the processing function of the nonlinear interconnected term is reconstructed. The fault observer gain can be directly solved by the reconstructed processing function. Compared with the method of scaling the nonlinear interconnected term to generate the inequality matrix in the prior art, the present application reduces the number of inequality matrices and solves the problem of high computational complexity of fault detection of the multi-robot interconnected system in the prior art.

[0199] 2) In the fault detection device for a multi-robot interconnected system of the present application, a first modeling unit models the multi-robot interconnected system based on an interconnected nonlinear system model to obtain a first target model, wherein the first target model includes multiple second target models, and the second target models are the interconnected nonlinear system models corresponding to each of the robots; a second modeling unit constructs a fuzzy fault observer model based on the second target model to obtain a third target model; a third modeling unit determines the difference between the second target model and the third target model as a first estimation error, and re-models the second target model based on the first estimation error and the third target model to obtain a fourth target model; a fourth modeling unit determines the nonlinear interactions in the model based on the fourth target model and the third target model. The invention discloses a method for determining a first target variable based on the boundary restriction of the nonlinear interconnected term, and remodeling the first estimation error and residual signal of the third target model based on the first target variable to obtain a fifth target model; the first calculation unit verifies the fifth target model through the Lyapunov function, and constructs multiple nonlinear inequality matrices based on the fifth model and the second model when the verification is passed, and solves the nonlinear inequality to obtain the target fault observer gain; the second calculation unit updates the fifth target model based on the target fault observer gain to obtain the sixth target model, and constructs a fault judgment function based on the sixth target model, and determines that the multi-robot interconnected system has a fault when the output value of the fault judgment function is greater than the first threshold. The present application introduces an adaptive mechanism, and extracts specific variables through the boundary restriction of the nonlinear interconnected term on the basis of modeling, and reconstructs the processing function of the nonlinear interconnected term. The fault observer gain can be directly solved through the reconstructed processing function. Compared with the method of scaling the nonlinear interconnected term to generate an inequality matrix in the prior art, the present application reduces the number of inequality matrices and solves the problem of high computational complexity of fault detection of the multi-robot interconnected system in the prior art.

[0200] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A fault detection method for a multi-robot interconnected system, characterized in that: include: Modeling a multi-robot interconnected system based on an interconnected nonlinear system model to obtain a first target model, wherein the first target model includes a plurality of second target models, and the second target models are the interconnected nonlinear system models corresponding to each of the robots; Building a fuzzy fault observer model based on the second target model to obtain a third target model; Determine a difference between the second target model and the third target model as a first estimation error, and remodel the second target model based on the first estimation error and the third target model to obtain a fourth target model; Determine a nonlinear interconnected term in a model based on the fourth target model and the third target model, determine a first target variable based on boundary restrictions of the nonlinear interconnected term, and remodel the first estimation error and residual signal of the third target model based on the first target variable to obtain a fifth target model; Verifying the fifth target model through a Lyapunov function, constructing a plurality of nonlinear inequality matrices based on the fifth target model and the second target model if the verification passes, solving the nonlinear inequalities, and obtaining a target fault observer gain; The fifth target model is updated based on the target fault observer gain to obtain a sixth target model, and a fault judgment function is constructed based on the sixth target model. When the output value of the fault judgment function is greater than a first threshold, it is determined that a fault exists in the multi-robot interconnection system.

2. The method according to claim 1, characterized in that Modeling the multi-robot interconnected system based on the interconnected nonlinear system model to obtain a first target model, wherein the first target model includes a plurality of second target models, including: Each robot in the multi-robot interconnected system is modeled and represented in a tight set including the origin to obtain the seventh target model: Among them, x i (t) is the state of the robot i, For x i The derivative of (t), i = 1, 2, ..., N, N is the number of robots included in the multi-robot interconnection system, j = 1, 2, ..., r i , r i is the total number of fuzzy rules for robot i, υ(t) is the antecedent variable, w i (t) and f i (t) are 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 is the system matrix, y i (t) The measured output of the robot i, g ni (x n (t)) is the nonlinear interconnection term, g ni (x n (t)) satisfies ‖g ni (x n (t))‖≤α ni ‖x n (t)‖,α ni is the unknown constant term, h ij ( i (t)) is the membership function, h ij ( i (t)) satisfies h ij ( i (t))≥0 and The system matrix in the seventh target model Decompose to get the first matrix and the second matrix Among them, A i0 is the constant term of the i-th row, A ij To remove A i0 Other parts: The seventh target is represented based on the first matrix and the second matrix to obtain the second target 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 A fuzzy fault observer model is constructed based on the second target model to obtain a third target model, including: Construct 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), is the estimated value of υ(t), r i (t) is the residual signal, L ij is the gain of the fault detection observer, g ni (x n (t)) processing function.

4. The method according to claim 3, characterized in that Determining a 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, including: The difference between the second target model and the third target model is determined as the first estimation error: where e i (t) is the first estimated error; The second target model is remodeled based on the first estimation error and the third target model to obtain a fourth target model:

5. The method according to claim 4, characterized in that Determining nonlinear interconnected terms in a model based on the fourth target model and the third target model, determining a first target variable based on boundary restrictions of the nonlinear interconnected terms, and remodeling the first estimation error and residual signal of the third target model based on the first target variable to obtain a fifth target model, including: Determine the corresponding first boundary limit based on the nonlinear interconnection term: Among them, η n >0 and η n is a constant term; Determine the first target variable based on the first boundary constraint The first boundary limit is expressed based on the first target variable, and a second boundary limit is obtained: Reconstruct the processing function based on the second boundary limit Among them, P i represents a Lyapunov matrix, and satisfies P i >0, and is a uniformly continuous bounded function that satisfies An eighth target model is constructed based on the processing function and the first target variable, and the eighth target model is used to update a second target variable, where the second target variable is an estimated value of the first target variable: Among them, r n is a constant, and r n >0, is the second target variable; The difference between the first target variable and the second target variable is determined as the third target variable: in, is the third target variable, and the third target variable is based on 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 Verification is performed based on the fifth target model through the Lyapunov function, and if the verification is passed, multiple nonlinear inequality matrices are constructed based on the fifth target model and the second target model, and the nonlinear inequality is solved to obtain the target fault observer gain, including: In the case of the actuator fault f i When (t) = 0, based on the Lyapunov matrix P i Construct the fifth target model disturbance suppression performance H ∞ Lyapunov function Get the first function; In the disturbance w i When (t) = 0, based on the Lyapunov matrix P i Construct the fifth target model disturbance suppression performance H ∞ The Lyapunov function of : Get the second function; When the output values ​​of the first function and the second function are both greater than a second threshold, determining that the verification is passed; If the verification is successful, a nonlinear inequality matrix is ​​constructed based on the fifth target model and the second target 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 item, I is the identity matrix; Solve it through Matlab linear matrix solver to get the Lyapunov matrix P 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 , and obtain the target fault observer gain.

7. The method according to claim 6, characterized in that and constructing a fault determination function based on the sixth target model, and determining that a fault exists in the multi-robot interconnection system when an output value of the fault determination function is greater than a first threshold, including: Construct a fault judgment function based on the sixth target model: When the fault judgment function output value J(t) is greater than the first threshold value J th In the case of, it is determined that the multi-robot interconnection system has a fault, wherein, 8. A fault detection device for a multi-robot interconnected system, characterized in that: The device comprises: A first modeling unit is used to model the multi-robot interconnected system based on an interconnected nonlinear system model to obtain a first target model, wherein the first target model includes a plurality of second target models, and the second target model is the interconnected nonlinear system model corresponding to each of the robots; A second modeling unit is used to construct a fuzzy fault observer model based on the second target model to obtain a third target model; a third modeling unit, configured to determine a difference between the second target model and the third target model as a first estimation error, and remodel the second target model based on the first estimation error and the third target model to obtain a fourth target model; a fourth modeling unit, configured to determine a nonlinear interconnected term in a model based on the fourth target model and the third target model, determine a first target variable based on a boundary restriction of the nonlinear interconnected term, and remodel the first estimation error and residual signal of the third target model based on the first target variable to obtain a fifth target model; A first calculation unit is used to verify the fifth target model through a Lyapunov function, and if the verification is passed, a plurality of nonlinear inequality matrices are constructed based on the fifth target model and the second target model, and the nonlinear inequality is solved to obtain a 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 judgment function based on the sixth target model, and to determine that a fault exists in the multi-robot interconnection system when an output value of the fault judgment function is greater than a first threshold.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute 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 are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of claims 1 to 7.

Images