Simulation method, simulation device and storage medium for a multiple input / output system

Abstract

The application provides a simulation method, a simulation device and a storage medium of a multiple-input and multiple-output system. The method comprises the following steps: obtaining a first target state space model and a second target state space model; performing controllability analysis and observability analysis on the first target state space model by using a Kalman decomposition method based on a first predetermined value, to obtain a first controllable and observable state space model; performing controllability analysis and observability analysis by using the Kalman decomposition method based on a second predetermined value, to obtain a second controllable and observable state space model; merging the first controllable and observable state space model and the second controllable and observable state space model, to obtain a minimum realization state space model; and simulating the multiple-input and multiple-output system by using the minimum realization state space model. The method solves the problem that the simulation method of the multiple-input and multiple-output system in the prior art cannot consider both the accuracy and the timeliness.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of multi-input and output system, in particular to a multi-input and output system simulation method, a multi-input and output system simulation device, a storage medium, a processor and an electronic device. BACKGROUND

[0002] In the existing multi-input and output system simulation method, the Kalman state decomposition method is used to determine the minimum realization state space model of the multi-input and output system. In this process, the Kalman state decomposition method needs to be used for multiple controllability analysis and multiple observability analysis. If the number of controllability analysis is very large and the number of observability analysis is very large, a large amount of time and computing power will be consumed, which cannot guarantee the timeliness. If the number of controllability analysis is very small and the number of observability analysis is very small, the accuracy of the minimum realization state space model of the multi-input and output system cannot be guaranteed. That is, the existing multi-input and output system simulation method cannot balance the accuracy and timeliness.

[0003] At present, there is no solution to the above problems. SUMMARY

[0004] The main purpose of the present application is to provide a multi-input and output system simulation method, a multi-input and output system simulation device, a storage medium, a processor and an electronic device, which at least solve the problem that the existing multi-input and output system simulation method cannot balance the accuracy and timeliness.

[0005] In order to achieve the above object, according to one aspect of the present application, there is provided a simulation method of a multiple-input and multiple-output system, the method comprising: obtaining a first target state space model and a second target state space model, the first target state space model being a state space model of a first target subsystem, the second target state space model being a state space model of a second target subsystem, the multiple-input and multiple-output system comprising the first target subsystem and the second target subsystem, the first target subsystem being an integral part of the multiple-input and multiple-output system, the second target subsystem being a non-integral part of the multiple-input and multiple-output system; based on a first predetermined value, performing controllability analysis and observability analysis on the first target state space model by using a Kalman decomposition method to obtain a first controllable and observable state space model, and based on a second predetermined value, performing controllability analysis and observability analysis by using the Kalman decomposition method to obtain a second controllable and observable state space model, the first predetermined value being used to control the number of times of controllability analysis and the number of times of observability analysis of the first target state space model, the first predetermined value being negatively correlated with the number of times of controllability analysis of the first target state space model, the first predetermined value being negatively correlated with the number of times of observability analysis of the first target state space model, the second predetermined value being used to control the number of times of controllability analysis and the number of times of observability analysis of the second target state space model, the second predetermined value being negatively correlated with the number of times of controllability analysis of the second target state space model, the second predetermined value being negatively correlated with the number of times of observability analysis of the second target state space model, the first predetermined value being smaller than the second predetermined value; merging the first controllable and observable state space model and the second controllable and observable state space model to obtain a minimum realization state space model; and simulating the multiple-input and multiple-output system by using the minimum realization state space model.

[0006] Optionally, the controllable state space model comprises a first input matrix, a first control matrix and a first output matrix, based on a first predetermined value, controllability analysis and observability analysis are performed on the first target state space model by using a Kalman decomposition method to obtain a first controllable and observable state space model, comprising: a first analysis step, controllability analysis is performed on the first target state space model by using the Kalman decomposition method to obtain a controllable state space model; a second analysis step, observability analysis is performed on the controllable state space model by using the Kalman decomposition method to obtain the first controllable and observable state space model; a first acquisition step, a first target value and a second target value are acquired, the first target value is the number of row vectors of the first input matrix, and the second target value is the MacMillan degree of the first controllable and observable state space model; a first adjustment step, in the case that the absolute value of the difference between the first target value and the second target value is greater than a first preset value, the first predetermined value is reduced; and a first repetition step, the first analysis step, the second analysis step, the first acquisition step and the first adjustment step are repeated at least once until the absolute value of the difference between the first target value and the second target value is less than or equal to the first preset value.

[0007] Optionally, the first target state space model comprises a second input matrix, a second control matrix and a second output matrix, and the controllable state space model comprises a third input matrix, a third control matrix and a third output matrix, controllability analysis is performed on the first target state space model by using the Kalman decomposition method to obtain a controllable state space model, comprising: a second acquisition step, matrix decomposition is performed on an Nth control matrix to obtain an Nth unitary matrix, N≥1, and the first control matrix is a first control matrix; a first compression step, compression processing is performed on the Nth control matrix by using the Nth unitary matrix to obtain an N+1th control matrix and an N+1th zero matrix; a first construction step, an Nth transformation matrix is constructed by using the Nth unitary matrix and an Nth unit matrix, and the number of row vectors of the Nth transformation matrix is the same as that of row vectors of the original input matrix; a first increment step, in the case that the difference between the rank of the N+1th control matrix and the rank of the Nth control matrix is greater than or equal to the first predetermined value, the value of N is increased by 1; a second compression step, compression processing is performed on an Nth input matrix by using the Nth unitary matrix to obtain an N+1th input matrix, and the first input matrix is the second input matrix; a second repetition step, the first acquisition step, the first compression step, the first construction step, the first increment step and the second compression step are repeated until the difference between the rank of the N+1th control matrix and the rank of the Nth control matrix is less than an Nth first preset value, the rank of the N+1th control matrix is equal to 0 or the number of row vectors of the N+1th zero matrix is equal to 0; and a first determination step, the first controllable and observable state space model is determined according to the Nth transformation matrix. determining a controllable transformation matrix, wherein T ∑ is the controllable transformation matrix, T i is the i-th transformation matrix; a second determining step, determining the first input matrix according to A P ∑ A1T ∑ H , determining the first control matrix according to B P ∑ B1T P ∑ H , determining the first output matrix, wherein A1 is the second input matrix, A P is the third input matrix, T ∑ H is the conjugate transpose matrix of A P , B1 is the second control matrix, B P is the third control matrix, C1 is the second output matrix, C P is the third output matrix.

[0008] Alternatively, the controllable state space model comprises a third input matrix, a third control matrix and a third output matrix, the controllable state space model is subjected to an observability analysis by using a Kalman decomposition method to obtain the first controllable and observable state space model, comprising: a fourth obtaining step of performing matrix decomposition on an M-th output matrix to obtain an M-th unitary matrix, M≥1, and the first output matrix is the third output matrix; a third compression step of compressing the M-th output matrix by using the M-th unitary matrix to obtain an M+1-th output matrix and an M+1-th zero matrix; a second constructing step of constructing an M-th transformation matrix by using the M-th unitary matrix and an M-th unit matrix, and the number of row vectors of the M-th transformation matrix is the same as the number of row vectors of the controllable input matrix; a second incrementing step of, in the case that the difference between the rank of the M+1-th output matrix and the rank of the M-th output matrix is greater than or equal to an M-th first predetermined value, setting M+1, the difference between the M-th first predetermined value and an M-1-th first predetermined value is 1; a fourth compression step of compressing an M-th input matrix by using the M-th unitary matrix to obtain an M+1-th input matrix, and the first input matrix is the controllable input matrix; a fourth repeating step of repeating the fourth obtaining step, the third compression step, the second constructing step, the second incrementing step and the fourth compression step until the difference between the rank of the M+1-th output matrix and the rank of the M-th output matrix is less than the M-th first predetermined value, the rank of the M+1-th output matrix is equal to 0 or the number of row vectors of the M+1-th zero matrix is equal to 0; a third determining step of determining the first controllable and observable state space model according to​​​ determining a controllable and observable transformation matrix, wherein G ∑ is the controllable and observable transformation matrix, G n is the ith of the Mth transformation matrix; a fourth determining step, determining the controllable and observable input matrix according to B PQ = G ∑ H A P G ∑ , determining the controllable and observable input matrix according to B PQ = G ∑ H B P determining the controllable and observable control matrix according to C PQ = C P G ∑ , determining the controllable and observable output matrix, wherein A P is the controllable input matrix, G ∑ H is the conjugate transpose matrix of the controllable and observable transformation matrix, A PQ is the controllable and observable input matrix, B P is the controllable control matrix, B PQ is the controllable and observable control matrix, C P is the controllable output matrix, C PQ is the controllable and observable output matrix.

[0009] According to another aspect of the present application, there is provided an analog device of a multiple-input and multiple-output system, the device comprising: an obtaining unit configured to obtain a first target state space model and a second target state space model, the first target state space model being a state space model of a first target subsystem, the second target state space model being a state space model of a second target subsystem, the multiple-input and multiple-output system comprising the first target subsystem and the second target subsystem, the first target subsystem being an integral part of the multiple-input and multiple-output system, the second target subsystem being a non-integral part of the multiple-input and multiple-output system; an analyzing unit configured to perform controllability analysis and observability analysis on the first target state space model based on a first predetermined value using a Kalman decomposition method to obtain a first controllable and observable state space model, and perform controllability analysis and observability analysis on the second target state space model based on a second predetermined value using the Kalman decomposition method to obtain a second controllable and observable state space model, the first predetermined value being used to control a number of times of the controllability analysis and a number of times of the observability analysis of the first target state space model, the first predetermined value being negatively correlated with the number of times of the controllability analysis of the first target state space model, the first predetermined value being negatively correlated with the number of times of the observability analysis of the first target state space model, the second predetermined value being used to control a number of times of the controllability analysis and a number of times of the observability analysis of the second target state space model, the second predetermined value being negatively correlated with the number of times of the controllability analysis of the second target state space model, the second predetermined value being negatively correlated with the number of times of the observability analysis of the second target state space model, the first predetermined value being less than the second predetermined value; a merging unit configured to merge the first controllable and observable state space model and the second controllable and observable state space model to obtain a minimal realization state space model; and an analog unit configured to analog the multiple-input and multiple-output system using the minimal realization state space model.

[0010] According to yet another aspect of the present application, there is provided a computer readable storage medium, the computer readable storage medium comprising a stored program, wherein the computer readable storage medium is caused to perform any of the analog methods of a multiple-input and multiple-output system when the program is run.

[0011] According to still another aspect of the present application, there is provided a processor configured to run a program, wherein the processor is caused to perform any of the analog methods of a multiple-input and multiple-output system when the program is run.

[0012] According to an aspect of the present application, an electronic device 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, and the one or more programs comprise a simulation method for performing any one of the multi-input and output systems.

[0013] According to the technical solution of the present application, the multi-input and output system is divided into a first target state space model (integral part of the multi-input and output system) and a second target state space model (non-integral part of the multi-input and output system), the first target state space model is subjected to controllability analysis of a large number of times and observability analysis of a large number of times to ensure the accuracy of the finally obtained minimum realization state space model, and the second target state space model is subjected to controllability analysis of a small number of times and observability analysis of a small number of times to improve the timeliness, thereby solving the problem that the simulation method of the multi-input and output system in the prior art cannot balance the accuracy and timeliness. BRIEF DESCRIPTION OF DRAWINGS

[0014] The drawings accompanying the specification of the present application form a part thereof and serve to provide further understanding of the present application, the illustrative embodiments of the present application and its description serve to explain the present application, and do not constitute improper limitations on the present application. In the drawings:

[0015] Figure 1 A hardware structure block diagram of a mobile terminal for performing a simulation method of a multi-input and output system according to an embodiment of the present application is shown;

[0016] Figure 2 A flowchart of a simulation method of a multi-input and output system according to an embodiment of the present application is shown;

[0017] Figure 3 A structure block diagram of a simulation device of a multi-input and output system according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0018] It should be noted that the embodiments and features in the present application can be combined with each other without conflict. The technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0019] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should belong to the protection scope of the present application.

[0020] It should be noted that the terms "first", "second", and the like in the description and in the claims of the present application and in the above drawings are intended to distinguish between similar objects and not necessarily in a particular order or a particular sequence. It should be understood that the data thus used in the description can be interchanged, where appropriate, to describe the embodiments of the present application described herein. Furthermore, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, a method, a system, a product, or an apparatus that comprises a list of steps or units is not necessarily limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to such processes, methods, products, or apparatuses.

[0021] As introduced in the background, the simulation method of the multi-input and output system in the prior art cannot balance the accuracy and timeliness. To solve the problem that the simulation method of the multi-input and output system in the prior art cannot balance the accuracy and timeliness, the embodiments of the present application provide a simulation method of a multi-input and output system, a simulation device of a multi-input and output system, a storage medium, a processor and an electronic device.

[0022] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application.

[0023] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or similar computing devices. Taking the case of running on a mobile terminal, Figure 1 is a hardware structure block diagram of a mobile terminal of a simulation method of a multi-input and output system according to the embodiments of the present application. As shown in Figure 1 , the mobile terminal can include one or more (only one is shown in the figure) processors 102 (the processor 102 can include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the above mobile terminal can further include a transmission device 106 for communication function and an input and output device 108. Those skilled in the art can understand that Figure 1 the structure shown in the figure is only schematic, which does not limit the structure of the above mobile terminal. For example, the mobile terminal can include more or less components than those shown in Figure 1 , or have a different configuration from that shown in Figure 1 . Figure 1

[0024] ​The memory 104 can be used to store computer programs, such as software programs of application software and modules, such as a computer program corresponding to the device information display method of the embodiments of the present application. The processor 102 executes various functional applications and data processing, i.e., implements the above method, by running the computer programs stored in the memory 104. The memory 104 can include a high-speed random access memory, and can further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include memories disposed remotely with respect to the processor 102, which can be connected to the mobile terminal through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. The transmission device 106 is used to receive or send data via a network. The specific examples of the above network can include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet in a wireless manner.

[0025] In the embodiments, a simulation method of a multi-input and output 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 herein can be executed in an order different from that shown.

[0026] Figure 2 is a flowchart of the simulation method of the multi-input and output system according to the embodiments of the present application. As shown in Figure 2 the method includes the following steps:

[0027] In step S201, a first target state space model and a second target state space model are obtained.

[0028] The first target state space model is a state space model of a first target subsystem, and the second target state space model is a state space model of a second target subsystem. The multi-input and output system includes the first target subsystem and the second target subsystem. The first target subsystem is an integral part of the multi-input and output system, and the second target subsystem is a non-integral part of the multi-input and output system.

[0029] Step S202, based on a first predetermined value, the controllability analysis and observability analysis of the first target state space model is carried out by using Kalman decomposition method, and a first controllable and observable state space model is obtained, based on a second predetermined value, the controllability analysis and observability analysis is carried out by using Kalman decomposition method, and a second controllable and observable state space model is obtained;

[0030] The first predetermined value is used to control the number of times of controllability analysis and the number of times of observability analysis of the first target state space model, the first predetermined value is negatively correlated with the number of times of controllability analysis of the first target state space model, the first predetermined value is negatively correlated with the number of times of observability analysis of the first target state space model, the second predetermined value is used to control the number of times of controllability analysis and the number of times of observability analysis of the second target state space model, the second predetermined value is negatively correlated with the number of times of controllability analysis of the second target state space model, and the second predetermined value is negatively correlated with the number of times of observability analysis of the second target state space model, and the first predetermined value is less than the second predetermined value.

[0031] Step S203, the first controllable and observable state space model and the second controllable and observable state space model are combined to obtain a minimum realization state space model.

[0032] Step S204, the minimum realization state space model is used to simulate the multi-input and multi-output system.

[0033] Through the above embodiment, the multi-input and multi-output system is divided into a first target state space model (an integral part of the multi-input and multi-output system) and a second target state space model (a non-integral part of the multi-input and multi-output system), the first target state space model is subjected to a large number of controllability analysis and a large number of observability analysis to ensure the accuracy of the minimum realization state space model, and the second target state space model is subjected to a small number of controllability analysis and a small number of observability analysis to improve the timeliness, thereby solving the problem that the simulation method of the multi-input and multi-output system in the prior art cannot consider both accuracy and timeliness.

[0034] In an optional embodiment, the controllable state space model includes a first input matrix, a first control matrix and a first output matrix, and the controllability analysis and observability analysis of the first target state space model in step S202 based on the first predetermined value can be realized by using Kalman decomposition method.

[0035] Step S2021, a first analysis step, analyzes the controllability of the first target state space model by using the Kalman decomposition method to obtain a controllable state space model;

[0036] Step S2022, a second analysis step, analyzes the observability of the controllable state space model by using the Kalman decomposition method to obtain the first controllable and observable state space model.

[0037] Specifically, since the minimal realization state space model must have both controllability and observability, the controllability of the first target state space model (the integral part of the multi-input and output system) is analyzed by using the Kalman decomposition method to obtain a controllable and observable state space model, and then the observability of the controllable state space model is analyzed by using the Kalman decomposition method to obtain a controllable and observable state space model.

[0038] It should be noted that the first target state space model can also be analyzed for observability first, and then for controllability.

[0039] It should be noted that the process of analyzing the controllability and observability of the second target state space model by using the Kalman decomposition method to obtain the second controllable and observable state space model is the same as the process of analyzing the controllability and observability of the first target state space model by using the Kalman decomposition method to obtain the first controllable and observable state space model.

[0040] The controllable state space model includes a controllable input matrix, a controllable control matrix, and a controllable output matrix. In an optional embodiment, the step S2021 can be implemented as:

[0041] For a non-minimal realization system (A, B, C, D), first separate the integral part (A1, B1, C1, D1) and the non-integral part (A2, B2, C2, D2). Wherein A1 represents the state transition matrix, B1 represents the input matrix, C1 represents the output matrix, and D1 represents the direct transfer matrix. If there is no direct transfer relationship between the input and output of the system, the matrix is zero. Determine a unitary matrix U1 of B1 based on the first predetermined value selected initially, which compresses B1 matrix to obtain two parts, the first τ1 lines are 0 (and a very small value close to zero), and the last ρ1 lines are equal to the rank of B1. Use the same unitary matrix U1 to compress A1 and record the transformation matrix of this compression as U1. If B1 is neither row full rank nor zero rank, perform the second compression. Determine a unitary matrix U2 of B2 based on the first predetermined value selected, and use the unitary matrix to compress B2 matrix to obtain The A2 matrix is compressed to obtain The transformation matrix of this compression is recorded as The B1 is continuously checked until the B1 satisfies the row full rank or zero rank after compression, and the compression is stopped. The total transformation matrix is obtained by multiplying the transformation matrix of each time The total transformation matrix is used The original system is transformed, The lower right corner block is the controllable state transition matrix, denoted as A c The dimension is determined by all accumulated p, and the total transformation matrix is used The lower right corner block of the total transformation matrix is obtained The lower right corner block of the total transformation matrix is obtained c The lower right corner block of the total transformation matrix is obtained The lower right corner block of the total transformation matrix is obtained The lower right corner block of the total transformation matrix is obtained c .

[0042] For example The matrix dimension is (m, n), and the lower right corner matrix refers to the sigma(p) rows and sigma(p) columns from the last element.

[0043] The lower block of the matrix, that is, from the last row of the matrix, the sigma(p) rows are calculated from the top, and the block composed of all elements from this row is the lower block matrix of The right side block of the matrix, that is, from the rightmost column of the matrix, the sigma(p) columns are counted from left, and the block composed of all elements from this column is the right side block matrix of

[0044] The right side block of the matrix, that is, from the rightmost column of the matrix, the sigma(p) columns are counted from left, and the block composed of all elements from this column is the right side block matrix of The right side block of the matrix, that is, from the rightmost column of the matrix, the sigma(p) columns are counted from left, and the block composed of all elements from this column is the right side block matrix of

[0045] Step S2023, a first acquisition step, acquiring a first target value and a second target value, the first target value being the number of row vectors of the controllable input matrix, and the second target value being the MacMillan degree of the controllable state space model;

[0046] Step S2024, a first adjustment step, in the case that the absolute value of the difference between the first target value and the second target value is greater than a first preset value, reducing the first preset value;

[0047] Step S2025, a first repetition step, repeating the first analysis step, the second analysis step, the first acquisition step and the first adjustment step at least once until the absolute value of the difference between the first target value and the second target value is less than or equal to the first preset value. ​​

[0048] In addition, after step S2024 is performed, an error processing is added: setting a maximum iteration number, and if a result that is close enough to the MacMillan degree cannot be obtained after the first predetermined value is adjusted for the maximum iteration number, taking the last calculated result as the first target minimal realization model output.

[0049] In addition, if steps S2023, S2024 and S2025 only perform controllability decomposition, the MacMillan degree is not compared at this time. Only the result after the original model is completely decomposed in controllability and observability is used for comparison with the MacMillan degree. If the result matrix dimension is not much different from the MacMillan degree, the compression is recognized; otherwise, the first predetermined value is modified, and the decomposition in controllability and observability is performed again.

[0050] The first target state space model includes a second input matrix, a second control matrix and a second output matrix, the controllable state space model includes a third input matrix, a third control matrix and a third output matrix, the controllability of the first target state space model is analyzed by using the Kalman decomposition method, and the controllable state space model is obtained, including:

[0051] The second acquisition step performs matrix decomposition on the Nth control matrix to obtain an Nth unitary matrix, N≥1, and the first control matrix is the original control matrix.

[0052] Specifically, the matrix decomposition can use a singular value decomposition method or a QR decomposition method.

[0053] The method uses the SVD decomposition method to obtain the unitary matrix, and in the process of SVD decomposition, a plurality of singular values from large to small are obtained. The method sets the size of the singular value as a threshold, and the singular value smaller than the set threshold is discarded (i.e., regarded as 0). The larger the threshold is, the more singular values are discarded, and the smaller the dimension of the final minimal realization system is, but there may be a large deviation from the original system. Therefore, a small threshold is used for the integral system to make the result as accurate as possible, and a large threshold is used for the non-integral system to increase the minimal realization dimension reduction effect.

[0054] Specifically, B N =U N ΣV N H , wherein Σ is an n×n matrix, The first ρ diagonal elements of Σ are singular values arranged from large to small, and Σ p =diag{σ1,...,σ ρ}, B N is the Nth control matrix, U N is the Nth unitary matrix.

[0055] The first compression step is configured to compress the Nth control matrix by using the Nth unitary matrix to obtain an (N+1)th control matrix and an (N+1)th zero matrix.

[0056] Specifically, wherein U N is the Nth unitary matrix, U N H is the conjugate transpose matrix of the Nth unitary matrix, B N is the Nth control matrix, B N+1 is the (N+1)th control matrix, and O N+1 is the (N+1)th zero matrix, ρ1 is the rank of the (N+1)th control matrix, and τ1 is the number of row vectors of the (N+1)th zero matrix. B N is the smaller one between the number of rows and the number of columns of the matrix, and is recorded as Bmin, and B N The rank of the last Bmin rows of the matrix is recorded as rho1, and rho1 is the compression amount in this loop, and the number of rows of B N is the dimension of the part to be compressed in the next loop after rho1 is subtracted from the number of rows of B. The value of rho needs to be accumulated (for example, recorded as rho_all), and the value of rho is added to rho_all after each compression, and rho_all finally represents the dimension of the controllable part.

[0057] The first construction step is configured to construct an Nth transformation matrix by using the Nth unitary matrix and an Nth identity matrix, and the number of row vectors of the Nth transformation matrix is the same as the number of row vectors of the original input matrix.

[0058] Specifically, wherein U N is the Nth unitary matrix, I N is the Nth identity matrix, and T N is the Nth transformation matrix.

[0059] The first increment step is configured to, in the case that the difference between the rank of the (N+1)th control matrix and the rank of the Nth control matrix is greater than or equal to the Nth first predetermined value, increment N by 1, and the difference between the Nth first predetermined value and the (N-1)th first predetermined value is 1.

[0060] The second compression step is configured to compress the Nth input matrix by using the Nth unitary matrix to obtain an (N+1)th input matrix, and the first input matrix is the original input matrix.

[0061] Specifically, wherein U N is the Nth unitary matrix, U N H is the conjugate transpose matrix of the Nth unitary matrix, B NFor the Nth control matrix, B N+1 For the (N+1)th control matrix, A N Let A be the Nth input matrix. N+1 ρ1 is the rank of the (N+1)th input matrix, ρ1 is the rank of the (N+1)th control matrix, and τ1 is the number of row vectors in the (N+1)th input matrix.

[0062] The second repetition step involves repeating the second acquisition step, the first compression step, the first construction step, the first increment step, and the second compression step until the difference between the rank of the (N+1)th control matrix and the rank of the Nth control matrix is ​​less than the Nth first predetermined value, the rank of the (N+1)th control matrix is ​​equal to 0, or the number of row vectors of the (N+1)th zero matrix is ​​equal to 0.

[0063] Specifically, after several compressions, the B matrix (control matrix) will have two possible outcomes: 1) The rank of the B matrix is ​​zero, meaning that no more controllable parts that can be further decomposed are generated, i.e., rho is zero; 2) The rank of the B matrix is ​​equal to the number of rows (i.e., full row rank, rho equals the number of rows in the B matrix). Both of these scenarios are possible, and the loop exits when either condition is met.

[0064] The first step is to determine the procedure based on... Determine the controllable transformation matrix, where T ∑ Let T be the controllable transformation matrix mentioned above. i Let i be the i-th transformation matrix;

[0065] The second determination step, based on A P =T ∑ A1T ∑ H Determine the above controllable input matrix, based on B P =T ∑ B1 determines the above controllable matrix, based on C. P =C1T ∑ H Determine the controllable output matrix as described above, where A1 is the original input matrix, A P B1 is the controllable input matrix mentioned above, and B1 is the original control matrix mentioned above. P C1 is the controllable matrix mentioned above, and C2 is the original output matrix mentioned above. P The above is the controllable output matrix.

[0066] The aforementioned first controllable and observable state-space model includes a controllable and observable input matrix, a controllable and observable control matrix, and a controllable and observable output matrix. In an optional implementation, step S2022 can be implemented as follows:

[0067] The decomposed controllable part (Ac, Bc, Cc) of the original integral system (A1, B1, C1) is taken out from step S2021, and a controllability decomposition is performed again: Ac, Bc and Cc are transposed respectively and denoted as Ac', Bc' and Cc', Ac' corresponds to A1, Cc' corresponds to B1 and Bc' corresponds to C1 according to S2021, and one round of compression is performed, and finally the total transformation matrix T can be obtained o . Using the transformation of the controllable system, the lower right corner block is the controllable and observable state transition matrix, denoted as A co , the dimension of which is determined by all the accumulated ρ, using the lower block of is the controllable and observable input matrix, denoted as B co , using the right block of is the controllable and observable output matrix, denoted as C co .

[0068] Step S20221, third acquisition step, acquires a third target value and a fourth target value, the third target value is the number of row vectors of the controllable and observable input matrix, and the fourth target value is the MacMillan degree of the first controllable and observable state space model;

[0069] Step S20222, second adjustment step, in the case that the absolute value of the difference between the third target value and the fourth target value is greater than a second preset value, the first predetermined value is adjusted;

[0070] Step S20223, second analysis step, the controllability of the first target state space model is analyzed by using the Kalman decomposition method, and the first controllable and observable state space model is obtained; this step further analyzes the controllable state space model obtained to obtain the first controllable and observable state space model (minimum realization model).

[0071] Step S20224, third repetition step, the third acquisition step, the second adjustment step and the second analysis step are repeated at least once until the absolute value of the difference between the third target value and the fourth target value is less than or equal to the second preset value.

[0072] After the minimum realization model is obtained in step S20223, it is checked whether the rank of the minimum realization model is sufficient to approximate the MacMillan degree (defined as whether the difference between the two is less than a set scalar); if it is sufficient to approximate, it is considered that the minimum realization model obtained in step S20223 meets the requirements; otherwise, the third acquisition step, the second adjustment step and the second analysis step are repeatedly executed once to adjust the first predetermined value to determine whether the minimum realization model obtained in step S20223 meets the requirements.​

[0073] The controllable state space model includes a third input matrix, a third control matrix and a third output matrix, and a Kalman decomposition method is used to analyze the observability of the controllable state space model to obtain the first controllable and observable state space model, which includes:

[0074] A fourth acquisition step is performed to decompose the Nth output matrix to obtain an Nth unitary matrix, N≥1, and the first output matrix is the controllable output matrix;

[0075] A third compression step is performed to compress the Nth output matrix using the Nth unitary matrix to obtain an N+1th output matrix and an N+1th zero matrix;

[0076] A second construction step is performed to construct an Nth transformation matrix using the Nth unitary matrix and an Nth identity matrix, and the number of row vectors of the Nth transformation matrix is the same as the number of row vectors of the controllable input matrix;

[0077] A second increment step is performed to increase N+1 by 1 when the difference between the rank of the N+1th output matrix and the rank of the Nth output matrix is greater than or equal to the Nth first predetermined value, and the difference between the Nth first predetermined value and the N-1th first predetermined value is 1;

[0078] A fourth compression step is performed to compress the Nth input matrix using the Nth unitary matrix to obtain an N+1th input matrix, and the first input matrix is the controllable input matrix;

[0079] A fourth repetition step is performed to repeat the fourth acquisition step, the third compression step, the second construction step, the second increment step and the fourth compression step until the difference between the rank of the N+1th output matrix and the rank of the Nth output matrix is less than the Nth first predetermined value, the rank of the N+1th output matrix is equal to 0, or the number of row vectors of the N+1th zero matrix is equal to 0;

[0080] A third determination step is performed to determine a controllable and observable transformation matrix according to ∑ The controllable and observable transformation matrix is G n The i-th Nth transformation matrix is G

[0081] A fourth determination step is performed to determine a controllable and observable input matrix according to PQ ∑ H P ∑ PQ ∑ ​​​​​​H B P determining the controllable and observable control matrix according to C PQ = C P G ∑ determining the controllable and observable output matrix according to G P is the controllable input matrix, G ∑ H is the controllable and observable transformation matrix, A PQ is the controllable and observable input matrix, B P is the controllable control matrix, B PQ is the controllable and observable control matrix, C P is the controllable output matrix, C PQ is the controllable and observable output matrix.

[0082] After that, the same processing needs to be done to the second target state space model (i.e. the non-integral part of the original system):

[0083] 1. Design a second threshold, based on the second threshold, perform controllability decomposition on the second target state space model to obtain a second target controllable state space model.

[0084] 2. Based on the second threshold, perform observability decomposition on the second target controllable state space model to obtain a second target controllable and observable state space model (second model minimum realization model).

[0085] 3. Compare the rank of the second target controllable and observable state space model, and the MacMillan degree of the second target state space model, if they are close enough, then the second target minimum realization state space model obtained in this processing is recognized, otherwise adjust the second threshold, and execute the above step 1 again.

[0086] 4. Error handling: set the maximum number of iterations, if the second threshold is adjusted several times and still cannot obtain a result close enough to the MacMillan degree, then take the last calculation result as the output of the second target minimum realization model.

[0087] 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 the 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.

[0088] The embodiment of the present application further provides a simulation device of a multiple-input and multiple-output system. It should be noted that the simulation device of the multiple-input and multiple-output system of the embodiment of the present application can be used to execute the simulation method for the multiple-input and multiple-output system provided by the embodiment of the present application. The device is used to realize the above-mentioned embodiment and preferred embodiment, and the description has been made and will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the device described in the following embodiment is preferably realized in software, the realization of hardware or the combination of software and hardware is also possible and is conceived.

[0089] The simulation device of the multiple-input and multiple-output system provided by the embodiment of the present application is introduced below.

[0090] Figure 3 is a schematic diagram of the simulation device of the multiple-input and multiple-output system according to the embodiment of the present application. As shown in Figure 3 , the device comprises:

[0091] The acquisition unit 10 is configured to acquire a first target state space model and a second target state space model.

[0092] The first target state space model is a state space model of a first target subsystem, and the second target state space model is a state space model of a second target subsystem. The multiple-input and multiple-output system comprises the first target subsystem and the second target subsystem. The first target subsystem is an integral part of the multiple-input and multiple-output system, and the second target subsystem is a non-integral part of the multiple-input and multiple-output system.

[0093] The analysis unit 20 is configured to perform controllability analysis and observability analysis on the first target state space model based on a first predetermined value by using a Kalman decomposition method to obtain a first controllable and observable state space model, and perform controllability analysis and observability analysis based on a second predetermined value by using the Kalman decomposition method to obtain a second controllable and observable state space model.

[0094] The first predetermined value is used to control the number of controllability analysis and the number of observability analysis of the first target state space model, the first predetermined value is negatively correlated with the number of controllability analysis of the first target state space model, the first predetermined value is negatively correlated with the number of observability analysis of the first target state space model, the second predetermined value is used to control the number of controllability analysis and the number of observability analysis of the second target state space model, the second predetermined value is negatively correlated with the number of controllability analysis of the second target state space model, and the second predetermined value is negatively correlated with the number of observability analysis of the second target state space model, and the first predetermined value is less than the second predetermined value.

[0095] The merging unit 30 is configured to merge the first controllable and observable state space model and the second controllable and observable state space model to obtain a minimum realization state space model.

[0096] The simulation unit 40 is configured to simulate the multi-input and multi-output system by using the minimum realization state space model.

[0097] According to the above embodiment, the multi-input and multi-output system is divided into a first target state space model (an integral part of the multi-input and multi-output system) and a second target state space model (a non-integral part of the multi-input and multi-output system), the first target state space model is subjected to a large number of controllability analysis and a large number of observability analysis to ensure the accuracy of the minimum realization state space model, and the second target state space model is subjected to a small number of controllability analysis and a small number of observability analysis to improve the timeliness, thereby solving the problem that the simulation method of the multi-input and multi-output system in the prior art cannot balance the accuracy and timeliness.

[0098] In an optional embodiment, the analysis unit comprises:

[0099] The first analysis subunit is configured to perform controllability analysis on the first target state space model by using a Kalman decomposition method based on the first predetermined value to obtain a controllable state space model.

[0100] The second analysis subunit is configured to perform observability analysis on the controllable state space model by using a Kalman decomposition method based on the first predetermined value to obtain the first controllable and observable state space model.

[0101] Specifically, since the minimum realization state space model must have both controllability and observability, for the first target state space model (integral part of the multi-input and multi-output system), the Kalman decomposition method is used to analyze the controllability of the first target state space model (integral part of the multi-input and multi-output system) to obtain a controllable and observable state space model, and then the Kalman decomposition method is used to analyze the observability of the controllable state space model to obtain a controllable and observable state space model.

[0102] It should be noted that the observability analysis can be performed first, and then the controllability analysis.

[0103] It should be noted that the process of using the Kalman decomposition method to analyze the controllability and observability of the above-mentioned second target state space model to obtain a second controllable and observable state space model is the same as the process of using the Kalman decomposition method to analyze the controllability and observability of the above-mentioned first target state space model to obtain a first controllable and observable state space model.

[0104] The controllable state space model includes a controllable input matrix, a controllable control matrix, and a controllable output matrix. In an optional implementation, the first analysis subunit includes:

[0105] The first obtaining module is configured to perform the first obtaining step to obtain a first target value and a second target value. The first target value is the number of row vectors of the controllable input matrix, and the second target value is the MacMillan degree of the controllable state space model.

[0106] The first adjusting module is configured to perform the first adjusting step to adjust the first predetermined value when the absolute value of the difference between the first target value and the second target value is greater than a first preset value.

[0107] The first analysis module is configured to perform the first analysis step to analyze the controllability of the first target state space model by using the Kalman decomposition method to obtain the controllable state space model.

[0108] The first repeating module is configured to perform the first repeating step to repeat the first obtaining step, the first adjusting step, and the first analysis step at least once until the absolute value of the difference between the first target value and the second target value is less than or equal to the first preset value.

[0109] The first target state space model includes an original input matrix, an original control matrix, and an original output matrix. The controllable state space model includes a controllable input matrix, a controllable control matrix, and a controllable output matrix. Based on the first predetermined value, in an optional implementation, the first analysis module includes:

[0110] The first obtaining sub-module is configured to perform the second obtaining step, and perform matrix decomposition on the Nth control matrix to obtain an Nth unitary matrix, where N≥1, and the 1st control matrix is the original control matrix.

[0111] Specifically, B N = U N ΣV N H wherein Σ is an n×n matrix, the first ρ diagonal elements of Σ are singular values arranged in descending order, and Σ p = diag{σ1,...,σ ρ}, B N is the Nth control matrix, U N is the Nth unitary matrix.

[0112] The first compression sub-module is configured to perform the first compression step, and compress the Nth control matrix by using the Nth unitary matrix to obtain an N+1th control matrix and an N+1th zero matrix.

[0113] Specifically, wherein U N is the Nth unitary matrix, U N H is the conjugate transpose matrix of the Nth unitary matrix, B N is the Nth control matrix, B N+1 is the N+1th control matrix, O N+1 is the N+1th zero matrix, ρ1 is the rank of the N+1th control matrix, and τ0 is the number of row vectors of the N+1th zero matrix.

[0114] The first construction sub-module is configured to perform the first construction step, and construct an Nth transformation matrix by using the Nth unitary matrix and an Nth identity matrix, wherein the number of row vectors of the Nth transformation matrix is the same as the number of row vectors of the original input matrix.

[0115] Specifically, wherein U N is the Nth unitary matrix, I N is the Nth identity matrix, and T N is the Nth transformation matrix.

[0116] The first increment sub-module is configured to perform the first increment step, and in the case that the difference between the rank of the N+1th control matrix and the rank of the Nth control matrix is greater than or equal to the Nth first predetermined value, increment N+1, wherein the difference between the Nth first predetermined value and the N-1th first predetermined value is 1.

[0117] a second compression submodule, configured to perform a second compression step, compress an Nth input matrix by using the Nth unitary matrix to obtain an (N+1)th input matrix, wherein the first input matrix is the original input matrix;

[0118] Specifically, wherein U N is the Nth unitary matrix, U N H is a conjugate transpose matrix of the Nth unitary matrix, B N is the Nth control matrix, B N+1 is the (N+1)th control matrix, A N is the Nth input matrix, A N+1 is the (N+1)th input matrix, ρ1is a rank of the (N+1)th control matrix, and τ1is a number of row vectors of the (N+1)th input matrix.

[0119] a first repeating submodule, configured to perform a second repeating step, repeat the second obtaining step, the first compression step, the first constructing step, the first increasing step, and the second compression step until a difference between the rank of the (N+1)th control matrix and the rank of the Nth control matrix is less than the Nth first predetermined value, the rank of the (N+1)th control matrix is equal to 0, or a number of row vectors of the (N+1)th zero matrix is equal to 0;

[0120] Specifically, if the rank of the (N+1)th control matrix is equal to 0, it indicates that the (N+1)th control matrix has no compressed component, which means that the first target state space model has no controllable component that can be further divided, at this time, the loop is exited, and the number of row vectors of the (N+1)th zero matrix is equal to 0, which indicates that all components of the (N+1)th control matrix are divided into controllable components, and all components in the first target state space model are controllable components, at this time, the loop is exited.

[0121] a first determining submodule, configured to perform a first determining step, determine a controllable transformation matrix according to ∑ is the controllable transformation matrix, T i is the ith transformation matrix;

[0122] a second determining submodule, configured to perform a second determining step, determine the controllable input matrix according to A P ∑ H A1T ∑ , determine the controllable control matrix according to B P ∑ H B1determine the controllable control matrix according to C P Σ ​​​​Determine the controllable output matrix as described above, where A1 is the original input matrix, A P B1 is the controllable input matrix mentioned above, and B1 is the original control matrix mentioned above. P C1 is the controllable matrix mentioned above, and C2 is the original output matrix mentioned above. P The above is the controllable output matrix.

[0123] The aforementioned first controllable and observable state-space model includes a controllable and observable input matrix, a controllable and observable control matrix, and a controllable and observable output matrix. In an optional implementation, the aforementioned first analysis subunit includes:

[0124] The second acquisition module is used to execute the third acquisition step to acquire the third target value and the fourth target value. The third target value is the number of row vectors of the controllable and observable input matrix, and the fourth target value is the McMillan degree of the first controllable and observable state space model.

[0125] The second adjustment module is used to perform the second adjustment step, adjusting the first predetermined value when the absolute value of the difference between the third target value and the fourth target value is greater than the second preset value.

[0126] The second analysis module is used to perform the second analysis step, which uses the Kalman decomposition method to perform controllability analysis on the above-mentioned first target state space model to obtain the above-mentioned first controllable and observable state space model.

[0127] The second repeating module is used to perform the third repeating step, repeating the third acquisition step, the second adjustment step, and the second analysis step at least once, until the absolute value of the difference between the third target value and the fourth target value is less than or equal to the second preset value.

[0128] The aforementioned controllable state-space model includes a controllable input matrix, a controllable control matrix, and a controllable output matrix. The aforementioned first controllable and observable state-space model includes a controllable and observable input matrix, a controllable and observable control matrix, and a controllable and observable output matrix. In an optional implementation, the aforementioned second analysis module includes:

[0129] The second acquisition submodule is used to execute the fourth acquisition step, which performs matrix decomposition on the Nth output matrix to obtain the Nth unitary matrix, where N≥1, and the first output matrix is ​​the controllable output matrix mentioned above.

[0130] The third compression submodule is used to perform the third compression step, which uses the Nth unitary matrix to compress the Nth output matrix to obtain the (N+1)th output matrix and the (N+1)th zero matrix.

[0131] The second construction submodule is used to execute the second construction step, which uses the Nth unitary matrix and the Nth identity matrix to construct the Nth transformation matrix. The number of row vectors of the Nth transformation matrix is ​​the same as the number of row vectors of the controllable input matrix.

[0132] The second increment submodule is used to perform the second increment step, where if the difference between the rank of the (N+1)th output matrix and the rank of the Nth output matrix is ​​greater than or equal to the Nth first predetermined value, the difference between the N+1th first predetermined value and the (N-1)th first predetermined value is set to 1.

[0133] The fourth compression submodule is used to perform the fourth compression step, which uses the Nth unitary matrix to compress the Nth input matrix to obtain the (N+1)th input matrix. The first input matrix is ​​the controllable input matrix mentioned above.

[0134] The second repeating submodule is used to perform the fourth repeating step, repeating the above-mentioned fourth acquisition step, the above-mentioned third compression step, the above-mentioned second construction step, the above-mentioned second increment step and the above-mentioned fourth compression step until the difference between the rank of the above-mentioned (N+1)th output matrix and the rank of the above-mentioned Nth output matrix is ​​less than the Nth above-mentioned first predetermined value, the rank of the above-mentioned (N+1)th output matrix is ​​equal to 0, or the number of row vectors of the above-mentioned (N+1)th zero matrix is ​​equal to 0.

[0135] The third determination submodule is used to execute the third determination step, based on... Determine the controllable and observable transformation matrix, where G Σ Let G be the controllable and observable transformation matrix mentioned above. n This is the i-th transformation matrix mentioned above, which is the N-th transformation matrix.

[0136] The fourth determination submodule is used to execute the fourth determination step, based on A. PQ =G Σ H A P G Σ Determine the above controllable and observable input matrix, based on B PQ =G ∑ H B P Determine the controllable and observable control matrix as described above, based on C PQ =C P G ∑ Determine the controllable and observable output matrix as described above, where A P Given the controllable input matrix A, PQ For the above controllable and observable input matrix, B P For the above controllable control matrix, B PQ For the above controllable and observable control matrix, C PFor the above controllable output matrix, C PQ The above is the controllable and observable output matrix.

[0137] The aforementioned simulation device for a multiple input / output system includes a processor and a memory. The acquisition unit, analysis unit, merging unit, and simulation unit are all stored as program units in the memory, and the processor executes these program units to achieve the corresponding functions. All of the above modules reside in the same processor; alternatively, the modules may be located in different processors in any combination.

[0138] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can address the issue that existing simulation methods for multi-input / output systems cannot simultaneously achieve both accuracy and timeliness.

[0139] 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.

[0140] 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 simulation method of the multiple input / output system.

[0141] Specifically, simulation methods for multi-input / output systems include:

[0142] Step S201: Obtain the first target state space model and the second target state space model;

[0143] Wherein, the first target state space model is the state space model of the first target subsystem, the second target state space model is the state space model of the second target subsystem, the multi-input output system includes the first target subsystem and the second target subsystem, the first target subsystem is the integral part of the multi-input output system, and the second target subsystem is the non-integral part of the multi-input output system;

[0144] Step S202: Based on the first predetermined value, the controllability and observability of the first target state space model are analyzed using the Kalman decomposition method to obtain the first controllable and observable state space model. Based on the second predetermined value, the controllability and observability of the second controllable and observable state space model are analyzed using the Kalman decomposition method to obtain the second controllable and observable state space model.

[0145] Wherein, the first predetermined value is used to control the number of controllability analyses of the first target state space model and the number of observability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of controllability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of observability analyses of the first target state space model, the second predetermined value is used to control the number of controllability analyses of the second target state space model and the number of observability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of controllability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of observability analyses of the second target state space model, and the first predetermined value is less than the second predetermined value;

[0146] Step S203: Merge the first controllable and observable state space model and the second controllable and observable state space model to obtain the minimum realization state space model.

[0147] Step S204: Simulate the multi-input output system using the aforementioned minimum implementation state-space model.

[0148] This invention provides a processor for running a program, wherein the program executes the simulation method of the multiple input / output system.

[0149] This invention provides a device, 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 performs at least the following steps: Step S201, obtaining a first target state space model and a second target state space model;

[0150] Wherein, the first target state space model is the state space model of the first target subsystem, the second target state space model is the state space model of the second target subsystem, the multi-input output system includes the first target subsystem and the second target subsystem, the first target subsystem is the integral part of the multi-input output system, and the second target subsystem is the non-integral part of the multi-input output system;

[0151] Step S202: Based on the first predetermined value, the controllability and observability of the first target state space model are analyzed using the Kalman decomposition method to obtain the first controllable and observable state space model. Based on the second predetermined value, the controllability and observability of the second controllable and observable state space model are analyzed using the Kalman decomposition method to obtain the second controllable and observable state space model.

[0152] Wherein, the first predetermined value is used to control the number of controllability analyses of the first target state space model and the number of observability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of controllability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of observability analyses of the first target state space model, the second predetermined value is used to control the number of controllability analyses of the second target state space model and the number of observability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of controllability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of observability analyses of the second target state space model, and the first predetermined value is less than the second predetermined value;

[0153] Step S203: Merge the first controllable and observable state space model and the second controllable and observable state space model to obtain the minimum realization state space model.

[0154] Step S204: Simulate the multi-input output system using the aforementioned minimum implementation state-space model.

[0155] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0156] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0157] Step S201: Obtain the first target state space model and the second target state space model;

[0158] Wherein, the first target state space model is the state space model of the first target subsystem, the second target state space model is the state space model of the second target subsystem, the multi-input output system includes the first target subsystem and the second target subsystem, the first target subsystem is the integral part of the multi-input output system, and the second target subsystem is the non-integral part of the multi-input output system;

[0159] Step S202: Based on the first predetermined value, the controllability and observability of the first target state space model are analyzed using the Kalman decomposition method to obtain the first controllable and observable state space model. Based on the second predetermined value, the controllability and observability of the second controllable and observable state space model are analyzed using the Kalman decomposition method to obtain the second controllable and observable state space model.

[0160] Wherein, the first predetermined value is used to control the number of controllability analyses of the first target state space model and the number of observability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of controllability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of observability analyses of the first target state space model, the second predetermined value is used to control the number of controllability analyses of the second target state space model and the number of observability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of controllability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of observability analyses of the second target state space model, and the first predetermined value is less than the second predetermined value;

[0161] Step S203: Merge the first controllable and observable state space model and the second controllable and observable state space model to obtain the minimum realization state space model.

[0162] Step S204: Simulate the multi-input output system using the aforementioned minimum implementation state-space model.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0169] 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.

[0170] 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, magnetic magnetic 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.

[0171] 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.

[0172] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0173] 1) In the simulation method of the multi-input output system of this application, the multi-input output system is divided into a first target state space model (the integral part of the multi-input output system) and a second target state space model (the non-integral part of the multi-input output system). The first target state space model is subjected to a large number of controllability analyses and a large number of observability analyses to ensure the accuracy of the final minimum realization state space model. The second target state space model is subjected to a smaller number of controllability analyses and a smaller number of observability analyses to improve timeliness. This solves the problem that the simulation methods of multi-input output systems in the prior art cannot balance accuracy and timeliness.

[0174] 2) In the simulation device for the multi-input / output system of this application, the multi-input / output system is divided into a first target state space model (the integral part of the multi-input / output system) and a second target state space model (the non-integral part of the multi-input / output system). The first target state space model is subjected to a large number of controllability analyses and a large number of observability analyses to ensure the accuracy of the final minimum realization state space model. The second target state space model is subjected to a smaller number of controllability analyses and a smaller number of observability analyses to improve timeliness. This solves the problem that the simulation methods for multi-input / output systems in the prior art cannot balance accuracy and timeliness.

[0175] 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 simulation method for a multi-input output system, characterized in that, The method includes: Obtain a first target state space model and a second target state space model. The first target state space model is the state space model of the first target subsystem, and the second target state space model is the state space model of the second target subsystem. The multi-input output system includes the first target subsystem and the second target subsystem. The first target subsystem is the integral part of the multi-input output system, and the second target subsystem is the non-integral part of the multi-input output system. Based on a first predetermined value, the Kalman decomposition method is used to perform controllability and observability analysis on the first target state space model to obtain a first controllable and observable state space model. Based on a second predetermined value, the Kalman decomposition method is used to perform controllability and observability analysis to obtain a second controllable and observable state space model. The first predetermined value is used to control the number of controllability analyses and the number of observability analyses of the first target state space model. The first predetermined value is negatively correlated with the number of controllability analyses and the number of observability analyses of the first target state space model. The second predetermined value is used to control the number of controllability analyses and the number of observability analyses of the second target state space model. The second predetermined value is negatively correlated with the number of controllability analyses and the number of observability analyses of the second target state space model. The first predetermined value is less than the second predetermined value. The first controllable and observable state space model and the second controllable and observable state space model are merged to obtain the minimum realization state space model; The minimum implementation state-space model is used to simulate the multi-input output system.

2. The method according to claim 1, characterized in that, The controllable state-space model includes a first input matrix, a first control matrix, and a first output matrix. Based on a first predetermined value, the Kalman decomposition method is used to perform controllability and observability analysis on the first target state-space model, resulting in a first controllable and observable state-space model, including: The first analysis step involves using the Kalman decomposition method to perform a controllability analysis on the first target state-space model, thereby obtaining a controllable state-space model. The second analysis step involves using the Kalman decomposition method to perform observability analysis on the controllable state space model to obtain the first controllable and observable state space model. The first acquisition step involves acquiring a first target value and a second target value. The first target value is the number of row vectors in the first input matrix, and the second target value is the McMillan degree of the first controllable and observable state-space model. The first adjustment step is to reduce the first predetermined value when the absolute value of the difference between the first target value and the second target value is greater than the first preset value. The first repetition step involves repeating the first analysis step, the second analysis step, the first acquisition step, and the first adjustment step at least once, until the absolute value of the difference between the first target value and the second target value is less than or equal to the first preset value.

3. The method according to claim 2, characterized in that, The first target state-space model includes a second input matrix, a second control matrix, and a second output matrix. The controllable state-space model includes a third input matrix, a third control matrix, and a third output matrix. The controllability of the first target state-space model is analyzed using the Kalman decomposition method to obtain a controllable state-space model, including: The second acquisition step is to perform matrix decomposition on the Nth control matrix to obtain the Nth unitary matrix, where N≥1, and the first control matrix is ​​the first control matrix; In the first compression step, the Nth unitary matrix is ​​used to compress the Nth control matrix to obtain the (N+1)th control matrix and the (N+1)th zero matrix. The first construction step involves using the Nth unitary matrix and the Nth identity matrix to construct the Nth transformation matrix, wherein the number of row vectors in the Nth transformation matrix is ​​the same as the number of row vectors in the original input matrix. In the first increment step, if the difference between the rank of the (N+1)th control matrix and the rank of the Nth control matrix is ​​greater than or equal to the first predetermined value, the value of N is increased by 1. In the second compression step, the Nth unitary matrix is ​​used to compress the Nth input matrix to obtain the (N+1)th input matrix, and the first input matrix is ​​the second input matrix. The second repetition step involves repeating the first acquisition step, the first compression step, the first construction step, the first increment step, and the second compression step until the difference between the rank of the (N+1)th control matrix and the rank of the Nth control matrix is ​​less than the Nth first predetermined value, the rank of the (N+1)th control matrix is ​​equal to 0, or the number of row vectors of the (N+1)th zero matrix is ​​equal to 0. The first step is to determine the procedure based on... Determine the controllable transformation matrix, where T ∑ Let T be the controllable transformation matrix. i Let i be the i-th transformation matrix; The second determination step, based on A P =T ∑ A1T ∑ H Determine the first input matrix, based on B P =T ∑ B1 determines the first control matrix, according to C P =C1T ∑ H Determine the first output matrix, where A1 is the second input matrix, A P Let T be the third input matrix. ∑ H For A P The conjugate transpose of B1 is the second control matrix, B P C1 is the third control matrix, C2 is the second output matrix, and C3 is the third control matrix. P This is the third output matrix.

4. The method according to claim 2, characterized in that, The controllable state-space model includes a third input matrix, a third control matrix, and a third output matrix. Observability analysis of the controllable state-space model is performed using the Kalman decomposition method to obtain a first controllable and observable state-space model, including: The fourth step is to perform matrix decomposition on the Mth output matrix to obtain the Mth unitary matrix, where M≥1, and the 1st output matrix is ​​the third output matrix. The third compression step involves using the Mth unitary matrix to compress the Mth output matrix, resulting in the (M+1)th output matrix and the (M+1)th zero matrix. The second construction step involves using the Mth unitary matrix and the Mth identity matrix to construct the Mth transformation matrix, wherein the number of row vectors in the Mth transformation matrix is ​​the same as the number of row vectors in the controllable input matrix. In the second increment step, if the difference between the rank of the (M+1)th output matrix and the rank of the Mth output matrix is ​​greater than or equal to the Mth first predetermined value, then M+1, the difference between the Mth first predetermined value and the (M-1)th first predetermined value is set to 1. In the fourth compression step, the Mth unitary matrix is ​​used to compress the Mth input matrix to obtain the (M+1)th input matrix, where the first input matrix is ​​the controllable input matrix. The fourth repetition step involves repeating the fourth acquisition step, the third compression step, the second construction step, the second increment step, and the fourth compression step until the difference between the rank of the (M+1)th output matrix and the rank of the Mth output matrix is ​​less than the first predetermined value, the rank of the (M+1)th output matrix is ​​equal to 0, or the number of row vectors in the (M+1)th zero matrix is ​​equal to 0. The third step is to determine the procedure based on... Determine the controllable and observable transformation matrix, where G ∑ Let G be the controllable and observable transformation matrix. n Let be the i-th transformation matrix and the M-th transformation matrix. The fourth step is to determine the procedure based on A. PQ =G ∑ H A P G ∑ Determine the controllable and observable input matrix, based on B PQ =G ∑ H B P Determine the controllable and observable control matrix, based on C PQ =C P G ∑ Determine the controllable and observable output matrix, where A P Let G be the controllable input matrix. ∑ H Let A be the conjugate transpose of the controllable and observable transformation matrix. PQ Let B be the controllable and observable input matrix. P For the controllable control matrix, B PQ Let C be the controllable and observable control matrix. P For the controllable output matrix, C PQ Let be the controllable and observable output matrix.

5. An analog device for a multi-input / output system, characterized in that, The device includes: An acquisition unit is used to acquire a first target state space model and a second target state space model. The first target state space model is the state space model of a first target subsystem, and the second target state space model is the state space model of a second target subsystem. The multi-input output system includes the first target subsystem and the second target subsystem. The first target subsystem is the integral part of the multi-input output system, and the second target subsystem is the non-integral part of the multi-input output system. An analysis unit is configured to perform controllability and observability analysis on the first target state space model using the Kalman decomposition method based on a first predetermined value to obtain a first controllable and observable state space model; and to perform controllability and observability analysis using the Kalman decomposition method based on a second predetermined value to obtain a second controllable and observable state space model. The first predetermined value is used to control the number of controllability analyses and the number of observability analyses of the first target state space model. The first predetermined value is negatively correlated with the number of controllability analyses and the number of observability analyses of the first target state space model. The second predetermined value is used to control the number of controllability analyses and the number of observability analyses of the second target state space model. The second predetermined value is negatively correlated with the number of controllability analyses and the number of observability analyses of the second target state space model. The first predetermined value is less than the second predetermined value. The merging unit is used to merge the first controllable and observable state space model and the second controllable and observable state space model to obtain the minimum realization state space model. A simulation unit is used to simulate the multi-input output system using the minimum implementation state-space model.

6. 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 simulation method of the multiple input / output system according to any one of claims 1 to 4.

7. A processor, characterized in that, The processor is used to run a program, wherein the program executes the simulation method of the multi-input output system according to any one of claims 1 to 4.

8. An electronic device, 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 including a method for performing a simulation of a multi-input / output system according to any one of claims 1 to 4.

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