Predictor generation method and device, electronic equipment and storage medium

Abstract

The invention relates to a predictor generation method and device, electronic equipment and a storage medium, and is applied to the technical field of computers, and the method comprises the steps: obtaining a model matrix in an original state space model and matrix parameters of the model matrix; the model matrix comprises an output matrix and a state matrix of a controlled object, and the matrix parameters comprise the number of rows of the output matrix; based on the matrix parameters, a model matrix is expanded to obtain an expanded matrix, the expanded matrix comprises an expanded state matrix obtained by expanding the state matrix, the number of first columns, newly added relative to the state matrix, of the expanded state matrix is consistent with the number of rows of the output matrix, and the expanded matrix meets a Hautus observability condition; and establishing a predictor based on the extension matrix.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to a method, apparatus, electronic device, and storage medium for generating an estimator. Background Art

[0002] In process control, in each control cycle of Model Predictive Control (MPC), the MPC controller performs dynamic prediction within the prediction horizon (PH) according to the input and output constraint conditions and the prediction model, and repeats the execution of new optimization. In the actual production scenarios on site, there are always situations of disturbances, noises, and inaccurate models, resulting in a deviation between the actual process on site and the estimation of the prediction model. In order to achieve unbiased tracking control of MPC under the above complex working conditions, an estimator is usually used to estimate the deviation caused by disturbances, noises, and model mismatches in real time, and these effects are eliminated through dynamic compensation.

[0003] The design of the estimator is to ensure that the deviation generated by each controlled variable (CV) under complex working conditions can be effectively eliminated.

[0004] In the related art, for the estimator of unbiased MPC control, it is usually required that each controlled variable (CV) corresponds to a manipulated variable (MV) one by one. However, the coping method adopted is to simply expand the input matrix of the original state space model into the state matrix. However, in the case of relatively complex (such as multi-input multi-output of controlled variables), if a certain MV has a corresponding gain relationship with multiple CVs, the disturbance estimation states of these CVs will interfere with each other, resulting in prediction errors, and even causing the control action of the MV to reverse, resulting in control failure. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, and storage medium for generating an estimator, which is used to solve the problem in the prior art that when using the estimator generated by expanding the input matrix of the original state space model into the state matrix, the disturbance estimation states of CVs interfere with each other, resulting in prediction errors, and even causing the control action of the MV to reverse, resulting in control failure.

[0006] According to the first aspect of the embodiments of this application, a method for generating an estimator is provided, including:

[0007] Obtain the model matrix in the original state space model and the matrix parameters of the model matrix; the model matrix includes an output matrix and the state matrix of the controlled object, and the matrix parameters include the number of rows of the output matrix;

[0008] The model matrix is extended based on the matrix parameters to obtain an extended matrix, the extended matrix includes an extended state matrix obtained by extending the state matrix, the number of the first columns newly added to the extended state matrix relative to the state matrix is consistent with the number of rows of the output matrix, and the extended matrix satisfies the Hautus observability condition;

[0009] An estimator is established based on the extended matrix.

[0010] Optionally, the extended matrix further includes an extended output matrix obtained by extending the output matrix, and the number of the second columns newly added to the extended output matrix relative to the output matrix is consistent with the number of rows of the output matrix;

[0011] The model matrix further includes: an input matrix;

[0012] The method further includes:

[0013] The number of rows by which the extended input matrix is extended relative to the input matrix is consistent with the number of rows of the output matrix, and the extended input matrix is a matrix obtained by extending the input matrix.

[0014] Optionally, extending the model matrix based on the matrix parameters to obtain an extended matrix that satisfies the Hautus observability condition includes:

[0015] The following extension process: extending the intermediate output matrix according to the first preset extension method to obtain the current output matrix; extending the intermediate state matrix according to the second preset extension method to obtain the initial state matrix; configuring perturbation gains for the elements of the last column of the initial state matrix to obtain the current state matrix, so that the state matrix, the output matrix, the current state matrix, and the current output matrix satisfy the Hautus observability condition, the initial value of the intermediate state matrix is the state matrix; the initial value of the intermediate output matrix is the output matrix;

[0016] Updating the intermediate output matrix to the current output matrix and updating the intermediate state matrix to the current state matrix, and executing the extension process again until the number of columns newly added to the current state matrix relative to the state matrix is consistent with the number of rows of the output matrix, and determining the current output matrix and the current state matrix as the extended matrix.

[0017] Optionally, the first preset extension method includes:

[0018] C1 = [C total O ny，1 ];

[0019] The second preset extension method includes:

[0020]

[0021] Among them, A1 represents the initial state matrix obtained by this expansion, C1 represents the current output matrix obtained by this expansion, A total represents the intermediate state matrix, C total represents the intermediate output matrix, O nx,1 represents a zero matrix with nx rows and one column, O ny,1 represents a zero matrix with ny rows and 1 column, nx represents the number of rows of A, ny represents the number of rows of the output matrix C, and nd represents the number of columns of this expansion.

[0022] Optionally, configure a perturbation gain for the elements in the last column of the initial state matrix to obtain the current state matrix, so that the state matrix, output matrix, current state matrix, and current output matrix satisfy the Hautus observability condition, including:

[0023] Set the element in the last column of the nth row of the initial state matrix to the perturbation gain, and the initial value of n is 1;

[0024] Judge whether the state matrix, output matrix, current state matrix, and current output matrix satisfy the Hautus observability condition;

[0025] If not, clear the element in the last column of the nth row of the initial state matrix, update n to n + 1, and execute again the operation of setting the element in the last column of the nth row of the initial state matrix to the perturbation gain until it is satisfied.

[0026] Optionally, judge whether the state matrix, output matrix, current state matrix, and current output matrix satisfy the Hautus observability condition, including:

[0027] Judge whether the following equation holds:

[0028]

[0029] Among them, A represents the state matrix, C represents the output matrix, I represents the identity matrix, and Gd represents the perturbation state matrix in the extended state matrix.

[0030] Optionally, it further includes: expanding the input matrix B in the following manner:

[0031]

[0032] Among them, B1 represents the current input matrix, and the initial value of B total is the input matrix B, and O nd,nu represents a zero matrix with nd rows and nu columns, where nu is the number of columns of B.

[0033] According to the second aspect of the embodiments of the present application, a generation device for an estimator is provided, including:

[0034] An acquisition unit, configured to acquire a model matrix in an original state - space model and matrix parameters of the model matrix; the model matrix includes an output matrix and a state matrix of a controlled object, and the matrix parameters include the number of rows of the output matrix;

[0035] An expansion unit, configured to expand the model matrix based on the matrix parameters to obtain an expansion matrix, the expansion matrix satisfies the Hautus observability condition, the expansion matrix includes an expanded state matrix obtained by expanding the state matrix, the number of the first columns newly added to the expanded state matrix relative to the state matrix is the same as the number of rows of the output matrix, and the expansion matrix satisfies the Hautus observability condition;

[0036] A generation unit, configured to establish an estimator based on the expansion matrix.

[0037] According to the third aspect of the embodiments of the present application, there is provided an electronic device, including a memory and a processor;

[0038] The memory is connected to the processor and is used for storing programs;

[0039] The processor is configured to implement the anti - abnormal sound method as described in the first aspect by running the program in the memory.

[0040] According to the fourth aspect of the embodiments of the present application, there is provided a storage medium, on which a computer program is stored, and when the computer program is run by a processor, the anti - abnormal sound method as described in the first aspect is implemented.

[0041] According to the fifth aspect of the embodiments of the present application, there is provided a computer program product, including computer program instructions, and when the computer program instructions are run by a processor, the processor is caused to execute the anti - abnormal sound method as described in the first aspect.

[0042] The above - mentioned technical solutions provided by the embodiments of the present application have the following advantages compared with the prior art: In the method provided by the embodiments of the present application, by expanding the state matrix in the original state - space model, the number of columns of the expanded matrix is the same as the number of rows of the output matrix, so as to ensure that the parameters of each output matrix (i.e., the controlled variables) have corresponding disturbance states for compensation, so that the disturbance states of each controlled variable do not interfere with each other, and the prediction can be made more accurate. Moreover, the expansion matrix satisfies the Hautus observability condition, indicating that the disturbance states expanded for the state matrix do not affect the observability of the system, so as to correctly compensate for the disturbance during the dynamic process, and further achieve unbiased control. Description of the Drawings

[0043] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on the provided drawings.

[0044] Figure 1 It is a flowchart of a method for generating an estimator provided by an embodiment of the present application;

[0045] Figure 2 It is a flowchart of a method for generating an estimator provided by another embodiment of the present application;

[0046] Figure 3 It is a structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0048] Exemplary implementation environment

[0049] The method for generating an estimator according to the embodiment of the present application can be executed by an electronic device such as a terminal device or a server. The terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be an independent physical server, a server cluster composed of multiple physical servers, or a cloud server capable of performing cloud computing. This method can be implemented by the processor invoking the computer-readable program instructions stored in the memory. The present application takes the method for generating an estimator being executed by the server as an example for explanation, but is not limited thereto.

[0050] Exemplary method

[0051] Before elaborating on the embodiments of the present invention in detail, the relevant background involved in the embodiments of the present invention will be further described.

[0052] In model predictive control, the predictor is the core to achieve unbiased tracking. Its function is to estimate in real time the deviations caused by disturbances, noises, and model mismatches, and eliminate these effects through dynamic compensation. The design of traditional predictors is relatively cumbersome and needs to be designed for different process objects to ensure that the deviations generated by each controlled variable (CV) under complex working conditions can be effectively eliminated. The original state-space model is as follows:

[0053] x(t + 1) = Ax(t) + Bu(t); (1)

[0054] y(t) = Cx(t); (2)

[0055] Where: x represents the state quantity of the object, with a dimension of nx×1; t represents time / cycle number; u represents the input vector, with a dimension of nu×1; y represents the output vector, with a dimension of ny×1; A represents the state matrix of the controlled object, with a dimension of nx×nx; B represents the input matrix, with a dimension of nx×nu; C represents the output matrix, with a dimension of ny×nx.

[0056] The extended state-space model considering disturbances can be expressed in the forms of equations (3) and (4) below, where determines the influence of disturbances on the system.

[0057]

[0058] Where, d represents the disturbance of the system, with a dimension of nd×1, Gd represents the disturbance state matrix in the extended state matrix, O represents the zero matrix, and I represents the identity matrix.

[0059] According to the Hautus observability condition, the extended state-space model is observable if and only if: the original state-space model is observable, and the matrix is column full rank. A reasonable predictor needs to meet the above conditions to correctly compensate for disturbances during the dynamic process, incorporate unmeasurable disturbances into the system dynamic description, and thus achieve unbiased control.

[0060] Based on the above, it can be seen that the setting of Gd is very important for the generation of the predictor. In related technologies, directly using the input matrix B as Gd may result in not meeting the Hautus observability condition of being column full rank.

[0061] To avoid the related problems caused by directly using the input matrix as the disturbance matrix Gd in the prior art, the present application provides the following predictor generation method.

[0062] Please refer to Figure 1 , in an exemplary embodiment, a method for generating a predictor is provided, including:

[0063] Step 101: Obtain the model matrix in the original state space model and the matrix parameters of the model matrix; the model matrix includes an output matrix and a state matrix of the controlled object, and the matrix parameters include the number of rows of the output matrix.

[0064] In some embodiments, the original state space model is the above-mentioned equations (1) and (2). This model carries the corresponding model matrix and matrix parameters. Among them, the model matrix includes an output matrix, a state matrix of the controlled object, and an input matrix. The matrix parameters of each model matrix can also be directly obtained from this original state space model. The matrix parameters include the dimensions of each model matrix.

[0065] Step 102: Expand the model matrix based on the matrix parameters to obtain an expanded matrix. The expanded matrix includes an expanded state matrix obtained by expanding the state matrix. The number of the first new columns added to the expanded state matrix compared with the state matrix is the same as the number of rows of the output matrix, and the expanded matrix satisfies the Hautus observability condition.

[0066] In some embodiments, to avoid the related problems caused by simply setting the input disturbance matrix Gd as the input matrix B of the original state space model, in this step, the model matrix is expanded using the matrix parameters. Among them, for the expansion of the state matrix, the number of the first new columns added needs to be the same as the number of rows of the output matrix. In this way, each CV of the output matrix can have a corresponding disturbance state for compensation, and the disturbance states of each controlled variable will not interfere with each other, which can make the prediction more accurate. And the expanded matrix satisfies the Hautus observability condition, indicating that the disturbance state expanded for the state matrix does not affect the system observability, so as to correctly compensate the disturbance during the dynamic process, and then achieve unbiased control.

[0067] In an optional embodiment, the expanded matrix further includes an expanded output matrix obtained by expanding the output matrix, and the number of the second new columns added to the expanded output matrix compared with the output matrix is the same as the number of rows of the output matrix;

[0068] The model matrix further includes: an input matrix; the number of rows expanded by the expanded input matrix compared with the input matrix is the same as the number of rows of the output matrix, and the expanded input matrix is a matrix obtained by expanding the input matrix.

[0069] Among them, the purpose of expanding the output matrix and the input matrix is to ensure that the matrix dimensions of the expanded state space are correct, and at this time, the task of automatically generating the estimator is completed.

[0070] In an optional embodiment, expanding the model matrix based on the matrix parameters to obtain an expanded matrix that satisfies the Hautus observability condition includes:

[0071] The following expansion process: expand the intermediate output matrix according to the first preset expansion method to obtain the current output matrix; expand the intermediate state matrix according to the second preset expansion method to obtain the initial state matrix; configure a perturbation gain for the elements in the last column of the initial state matrix to obtain the current state matrix, so that the state matrix, output matrix, current state matrix, and current output matrix satisfy the Hautus observability condition. The initial value of the intermediate state matrix is the state matrix; the initial value of the intermediate output matrix is the output matrix;

[0072] Update the intermediate output matrix to the current output matrix and update the intermediate state matrix to the current state matrix, and then execute the expansion process again until the number of newly added columns of the current state matrix relative to the state matrix is the same as the number of rows of the output matrix, and determine the current output matrix and the current state matrix as the expansion matrix.

[0073] In some embodiments, the state matrix and the output matrix are expanded in the above manner. Each time, a column is added to the end of the intermediate output matrix, and a perturbation gain that satisfies the Hautus observability condition is configured, so that the expanded content does not affect the system objectivity.

[0074] In an optional embodiment, the first preset expansion method includes:

[0075] C1 = [C total O ny，1 ; (5)

[0076] The second preset expansion method includes:

[0077]

[0078] where, A1 represents the initial state matrix obtained by this expansion, C1 represents the current output matrix obtained by this expansion, A total represents the intermediate state matrix, C total represents the intermediate output matrix, O nx,1 represents a zero matrix with nx rows and one column, O ny,1 represents a zero matrix with ny rows and 1 column, nx represents the number of rows of A, ny represents the number of rows of the output matrix C, and nd represents the number of columns of this expansion. Among them, the initial value of nd is 0, and nd is updated to nd + 1 each time an expansion is performed until nd = ny.

[0079] It can be seen from equation (5) that each time the intermediate output matrix is expanded, a column of zero matrix is added to the end of the matrix. It can be seen from equation (6) that each time the intermediate state matrix is expanded, a zero matrix is added to the last column and the last row of the matrix, and 1 is filled at the end of the added row or column.

[0080] In an optional embodiment, a perturbation gain is configured for the elements in the last column of the initial state matrix to obtain the current state matrix, so that the state matrix, the output matrix, the current state matrix, and the current output matrix satisfy the Hautus observability condition, including:

[0081] Set the element in the last column of the n-th row of the initial state matrix to the perturbation gain, where the initial value of n is 1;

[0082] Judge whether the state matrix, the output matrix, the current state matrix, and the current output matrix satisfy the Hautus observability condition;

[0083] If not, clear the element in the last column of the n-th row of the initial state matrix, update n to n + 1, and execute again the operation of setting the element in the last column of the n-th row of the initial state matrix to the perturbation gain until it is satisfied.

[0084] In some embodiments, after obtaining the initial state matrix, starting from its first row, the elements in the last column can be configured with the perturbation gain row by row, and the positions where the perturbation gain is configured are recorded. After configuring the perturbation gain, by judging whether the Hautus observability condition is satisfied, if not, clear the last column, configure the elements in the next row with the perturbation gain, and judge again until the extended state matrix, output matrix, current state matrix, and current output matrix satisfy the Hautus observability condition.

[0085] Among them, the value of the perturbation gain can be selected and set based on the actual situation. For example, the perturbation gain can be, but is not limited to, 1.

[0086] Further, judging whether the state matrix, the output matrix, the current state matrix, and the current output matrix satisfy the Hautus observability condition can be, but is not limited to, through the following method:

[0087]

[0088] Among them, A represents the state matrix, C represents the output matrix, and I represents the identity matrix.

[0089] By calculating the rank of the matrix in Equation (7), when the ranks of the three matrices satisfy the conditions in Equation (7), it is determined that A1 and C1 obtained by extension in the above embodiments satisfy the Hautus observability condition

[0090] In this embodiment, Gd is a matrix with nx + nd rows and ny columns, and the perturbation gain is configured. It can be understood that after the extension is completed, nd is equal to ny.

[0091] In an alternative embodiment, to ensure that the matrix dimensions of the expanded state space are correct, after expanding the AC, the input matrix B also needs to be expanded in the following manner:

[0092]

[0093] where B1 represents the current input matrix, and the initial value of B total is the input matrix B, and O nd,nu represents a zero matrix with nd rows and nu columns, where nu is the number of columns of B.

[0094] It can be understood that after the expansion of A1 and C1, nd is the number of the first column.

[0095] In a specific embodiment, the process of expanding the model matrix can be as follows:

[0096] Step 1: Initialize the model matrix and parameters.

[0097] Obtain the matrices A, B, and C of the original state space model, and let the intermediate matrix for calculation be: A total = A, B total = B, C total = C, and record the total number of inputs and outputs, that is, the total input number nu and the total output number ny. Initialize the number of unmeasured disturbance states nd = 0, and the state counter n = 0.

[0098] Step 2: Construct the unmeasured disturbance state model.

[0099] For each CV (i.e., y in Equation (2) above), an unmeasured state quantity needs to be added. During operation, the Kalman filter can quickly estimate the unmeasured disturbance. The following operations are performed for each CV to add the corresponding unmeasured disturbance state.

[0100] Step 2.1: Increase the disturbance state count.

[0101] To determine the index position of the current disturbance state, it is necessary to perform the disturbance state count and count the expanded number of columns nd.

[0102] Judge whether nd is equal to ny at this time. If so, exit Step 2 and enter Step 3; otherwise, increase the disturbance state count by 1 to obtain the new state count: n = n + 1.

[0103] Step 2.2: Expand the state matrix and the output matrix

[0104] Construct the expanded state matrix A and output matrix C to obtain the initial state matrix A1 and the current output matrix C1:

[0105]

[0106] C1 = [C total O ny,1

[0107] Step 2.3: Set the disturbance state gain

[0108] In most cases, users can achieve a good estimate of the unmeasurable state without configuring the gain. However, in some specific scenarios, the impact of the disturbance on the CV may be significantly higher or lower than the disturbance model corresponding to the default gain. Therefore, users need to be allowed to configure the disturbance state.

[0109] Set the last element in the n-th row of matrix A1 to the user-configurable disturbance gain km, i.e.:

[0110] A1(n, end) = k m ;

[0111] where the default value of km is set to 1 to ensure that each CV has a corresponding disturbance state for compensation.

[0112] Step 2.4: Find the Gd matrix that makes the extended system observable

[0113] If the following conditions are met, combined with the Hautus observability condition, it means that adding a disturbance state at this position has no impact on the system observability, where I is the identity matrix of the corresponding dimension. At this time, the loop ends and step 2.5 is entered;

[0114]

[0115] Otherwise, it means that adding a new disturbance state at this position destroys the model observability, and a feasible position needs to be continued to be searched. Therefore, the last element in the n-th row of A1 needs to be cleared, i.e., A1(n, end) = 0, and let n = n + 1, then return to step 2.3.

[0116] Step 2.5: Update the model matrix

[0117] Assign the extended matrix to the intermediate state-space matrix, update the number of unmeasurable disturbance states, and return to step 2.1:

[0118] A total = A1

[0119] C total = C1

[0120] n d = n d + 1

[0121] Step 3: Extend the input matrix

[0122] ​Add nd rows to Btotal and set the elements of the newly added rows to 0 to ensure that the matrix dimension of the state space after expansion is correct. At this time, the automatic generation task of the estimator is completed:

[0123]

[0124] Step 103: Establish an estimator based on the expanded matrix.

[0125] In some embodiments, the expanded matrix provides a basis for the generation of the estimator, and the estimator can be uniquely determined by using the obtained expanded matrix. Among them, the expanded state matrix establishes a corresponding disturbance state for each CV, that is, for each CV, there is a unique corresponding column in Gd of the expanded state matrix as its disturbance state.

[0126] Compared with the existing technical solutions, the present invention has significant advantages in solving the key problems faced by MPC in practical engineering applications. Existing interference prediction methods usually need to be specially designed according to different systems, with complex operations and lack of generality. The improved solution proposed by the present invention can effectively overcome the problem of biased prediction caused by model mismatch and noise, provides an automated interference prediction method, simplifies the configuration work of engineers, and improves the reliability and application convenience of the system.

[0127] In the prior art, some interference prediction methods are only effective when the MV and CV are aligned and corresponding one by one. This limitation may lead to prediction errors or control failures in actual applications. The technical solution of the present invention ensures the observability of the expanded model by designing an interference estimator that can automatically generate and adapt to various system structures and input-output parameter combinations, avoids the problem of adaptability to the complex and changeable situations on the engineering site, and ensures the unbiasedness and stability of MPC control.

[0128] The interference estimator design method of the present invention can not only adapt to different types of controlled objects, but also reduce manual intervention and configuration work during the automation process, improving the efficiency of system deployment. This solution has high generality, can be applied in a variety of engineering scenarios, and significantly reduces the implementation difficulty.

[0129] The present invention proposes a design method that can automatically generate estimator parameters suitable for different controlled systems, and solves the problem of biased prediction caused by model mismatch and noise in model predictive control. This method can ensure that different structures and types of controlled systems can achieve unbiased prediction when implementing MPC control and ensure the observability of the expanded model.

[0130] A method for automatically generating estimator parameters, according to the characteristics of different controlled objects, automatically searches for suitable positions of newly added unmeasurable state configurations, constructs an estimator, while ensuring the observability of the extended state space and avoiding the complexity of manual configuration. This method is applicable to different engineering sites and variable control objects, and has high generality and flexibility. At the same time, other similar methods for iteratively generating estimators based on observability criteria are also covered within the scope of protection of this invention.

[0131] Exemplary device

[0132] Correspondingly, an embodiment of the present application further provides a device for generating an estimator, including:

[0133] An acquisition unit, configured to acquire the model matrix and the matrix parameters of the model matrix in the original state space model; the model matrix includes an output matrix and the state matrix of the controlled object, and the matrix parameters include the number of rows of the output matrix;

[0134] An expansion unit, configured to expand the model matrix based on the matrix parameters to obtain an expansion matrix, the expansion matrix satisfies the Hautus observability condition, the expansion matrix includes an extended state matrix obtained by expanding the state matrix, and the number of the first columns newly added to the extended state matrix relative to the state matrix is the same as the number of rows of the output matrix, and the expansion matrix satisfies the Hautus observability condition;

[0135] A generation unit, configured to establish an estimator based on the expansion matrix.

[0136] The device for generating an estimator provided in this embodiment belongs to the same inventive concept as the method for generating an estimator provided in the above embodiments of the present application, can execute the methods provided in any of the above embodiments of the present application, and has the corresponding functional modules and beneficial effects for executing the methods. For technical details not described in detail in this embodiment, reference can be made to the specific processing content of the method for generating an estimator provided in the above embodiments of the present application, which will not be elaborated here.

[0137] The functions implemented by the above acquisition unit, expansion unit and generation unit can be realized by the same or different processors respectively, which is not limited in the embodiments of the present application.

[0138] It should be understood that the acquisition unit, expansion unit, and generation unit in the above device can be implemented in the form of a processor invoking software. For example, the device includes a processor, the processor is connected to a memory, instructions are stored in the memory, and the processor invokes the instructions stored in the memory to implement any of the above methods or the functions of each unit of the device. The processor can be a general-purpose processor, such as a CPU or a microprocessor, etc., and the memory can be a memory inside the device or a memory outside the device. Alternatively, the units in the device can be implemented in the form of hardware circuits. By designing the hardware circuits, the functions of some or all of the units can be implemented. The hardware circuits can be understood as one or more processors. For example, in one implementation, the hardware circuit is an ASIC, and by designing the logical relationship of the components in the circuit, the functions of some or all of the above units are implemented. Again, for example, in another implementation, the hardware circuit can be implemented by a PLD. Taking FPGA as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured through a configuration file, so as to implement the functions of some or all of the above units. All the units of the above device can be all implemented in the form of a processor invoking software, or all implemented in the form of hardware circuits, or some implemented in the form of a processor invoking software and the remaining part implemented in the form of hardware circuits.

[0139] In the embodiments of the present application, the processor is a circuit with the ability to process signals. In one implementation, the processor can be a circuit with the ability to read and execute instructions, such as a CPU, a microprocessor, a GPU, or a DSP, etc. In another implementation, the processor can implement certain functions through the logical relationship of hardware circuits, and the logical relationship of the hardware circuits is fixed or can be reconstructed. For example, the processor is a hardware circuit implemented by an ASIC or a PLD, such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the configuration of the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as an NPU, a TPU, a DPU, etc.

[0140] It can be seen that each unit in the above device can be one or more processors (or processing circuits) configured to implement the above method. For example: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0141] In addition, all or part of the units in the above device can be integrated together or can be implemented independently. In one implementation, these units are integrated together and implemented in the form of a System on Chip (SOC). The SOC may include at least one processor for implementing any of the above methods or the functions of the units of the device. The types of the at least one processor may be different. For example, it may include a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0142] Exemplary electronic device

[0143] Another embodiment of the present application further provides an electronic device. As shown in Figure 3 the device includes:

[0144] a memory 300 and a processor 310;

[0145] wherein, the memory 300 is connected to the processor 310 and is used for storing programs;

[0146] the processor 310 is used for implementing the method for generating the estimator disclosed in any of the above embodiments by running the program stored in the memory 300.

[0147] Specifically, the above electronic device may further include: a bus, a communication interface 320, an input device 330, and an output device 340.

[0148] The processor 310, the memory 300, the communication interface 320, the input device 330, and the output device 340 are interconnected through the bus. Among them:

[0149] The bus may include a path for transmitting information between various components of the computer system.

[0150] The processor 310 may be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or may be an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present invention. It may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0151] The processor 310 may include a main processor and may also include a baseband chip, a modem, etc.

[0152] The program for implementing the technical solution of the present invention is stored in the memory 300, and the operating system and other key services can also be stored. Specifically, the program may include program codes, and the program codes include computer operation instructions. More specifically, the memory 300 may include a read-only memory (ROM), other types of static storage devices that can store static information and instructions, a random access memory (RAM), other types of dynamic storage devices that can store information and instructions, a disk memory, a flash memory, etc.

[0153] The input device 330 may include devices for receiving data and information input by a user, such as a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor, etc.

[0154] The output device 340 may include devices for allowing information to be output to a user, such as a display screen, a printer, a speaker, etc.

[0155] The communication interface 320 may include devices of any transceiver type for communicating with other devices or communication networks, such as an Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc.

[0156] The processor 310 executes the program stored in the memory 300 and calls other devices, and can be used to implement each step of the generation method of any one of the estimators provided in the above embodiments of the present application.

[0157] Exemplary computer program product and storage medium

[0158] In addition to the above methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions. When the computer program instructions are run by a processor, the processor is caused to execute the steps in the generation method of the estimator according to various embodiments of the present application described in any of the above embodiments of this specification.

[0159] The computer program product can be written in any combination of one or more programming languages for executing the program codes for operating the embodiments of the present application. The programming languages include object-oriented programming languages, such as Java, C++, etc., and also include conventional procedural programming languages, such as the "C" language or similar programming languages. The program codes can be executed completely on a user computing device, partially on a user device, executed as an independent software package, partially on a user computing device and partially on a remote computing device, or completely executed on a remote computing device or server.

[0160] In addition, an embodiment of the present application may also be a storage medium storing a computer program, and the computer program is executed by a processor to perform the steps in the method for generating an estimator according to various embodiments of the present application described in any of the above embodiments of the present specification. Specifically, the following steps may be implemented:

[0161] Obtain a model matrix in an original state space model and matrix parameters of the model matrix; the model matrix includes an output matrix and a state matrix of a controlled object, and the matrix parameters include the number of rows of the output matrix;

[0162] Based on the matrix parameters, expand the model matrix to obtain an expanded matrix, the expanded matrix includes an expanded state matrix obtained by expanding the state matrix, the number of new first columns added to the expanded state matrix relative to the state matrix is the same as the number of rows of the output matrix, and the expanded matrix satisfies the Hautus observability condition;

[0163] Based on the expanded matrix, establish an estimator.

[0164] For the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0165] It should be noted that each embodiment in this specification is described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0166] The steps in the methods of the embodiments of the present application can be adjusted, combined, and deleted according to actual needs. The technical features recorded in each embodiment can be replaced or combined.

[0167] The modules and sub-modules in the devices and terminals in the embodiments of the present application can be combined, divided, and deleted according to actual needs.

[0168] In several embodiments provided by the present application, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the terminal embodiments described above are merely illustrative. For example, the division of modules or sub-modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple sub-modules or modules can be combined or integrated into another module, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection to each other can be an indirect coupling or communication connection through some interfaces, devices, or modules, and can be in electrical, mechanical, or other forms.

[0169] The modules or sub-modules described as separate components may or may not be physically separated. The components as modules or sub-modules may or may not be physical modules or sub-modules. That is, they can be located in one place or distributed to multiple network modules or sub-modules. Some or all of the modules or sub-modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0170] In addition, in each embodiment of the present application, each functional module or sub-module can be integrated in a processing module, or each module or sub-module can exist physically alone, or two or more modules or sub-modules can be integrated in one module. The above-mentioned integrated modules or sub-modules can be implemented in the form of hardware or in the form of software functional modules or sub-modules.

[0171] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or the combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0172] The steps of the methods or algorithms described in combination with the embodiments disclosed in this article can be directly implemented by hardware, software units executed by a processor, or the combination of the two. The software units can be placed in a random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0173] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising said element.

[0174] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for generating an estimator, characterized in that: include: Obtaining a model matrix and matrix parameters of the model matrix in the original state space model; The model matrix includes an output matrix and a state matrix of the controlled object, and the matrix parameters include the number of rows of the output matrix; Expanding the model matrix based on the matrix parameters to obtain an extended matrix, the extended matrix including an extended state matrix obtained by expanding the state matrix, the number of first columns newly added to the extended state matrix relative to the state matrix is consistent with the number of rows of the output matrix, and the extended matrix satisfies a Hautus observability condition; An estimator is established based on the expanded matrix.

2. The method according to claim 1, characterized in that The extended matrix further includes an extended output matrix obtained by expanding the output matrix, and the number of second columns newly added to the extended output matrix relative to the output matrix is consistent with the number of rows of the output matrix; The model matrix also includes: input matrix; The method further comprises: The number of rows of the expanded input matrix relative to the input matrix is consistent with the number of rows of the output matrix. The expanded input matrix is a matrix obtained by expanding the input matrix.

3. The method according to claim 1 or 2, characterized in that The model matrix is expanded based on the matrix parameters to obtain an extended matrix, where the extended matrix satisfies the Hautus observability condition, including: The following expansion process: expanding the intermediate output matrix according to a first preset expansion method to obtain a current output matrix; expanding the intermediate state matrix according to a second preset expansion method to obtain an initial state matrix; configuring a perturbation gain for the last column element of the initial state matrix to obtain a current state matrix, so that the state matrix, the output matrix, the current state matrix, and the current output matrix meet the Hautus observability condition, and the initial value of the intermediate state matrix is the state matrix; the initial value of the intermediate output matrix is the output matrix; The intermediate output matrix is updated to the current output matrix and the intermediate state matrix is updated to the current state matrix, and the expansion process is performed again until the number of columns newly added to the current state matrix relative to the state matrix is consistent with the number of rows of the output matrix, and the current output matrix and the current state matrix are determined to be the expanded matrices.

4. The method according to claim 3, characterized in that The first preset extension method includes: C1=[C total O ny，1 ]; The second preset extension method includes: Among them, A1 represents the initial state matrix obtained by this expansion, C1 represents the current output matrix obtained by this expansion, and A total represents the intermediate state matrix, C total represents the intermediate output matrix, O nx,1 represents a zero matrix with nx rows and one column, O ny,1 Represents a zero matrix with ny rows and 1 column, nx represents the number of rows in A, ny represents the number of rows in the output matrix C, and nd represents the number of columns in this expansion.

5. The method according to claim 3, characterized in that Configure the perturbation gain for the last column element of the initial state matrix to obtain the current state matrix so that the state matrix, output matrix, current state matrix, and current output matrix meet the Hautus observability conditions, including: Set the element in the last column of the nth row of the initial state matrix to the perturbation gain, and the initial value of n is 1; Determine whether the state matrix, output matrix, current state matrix, and current output matrix meet the Hautus observability conditions; If not satisfied, the element in the last column of the nth row of the initial state matrix is cleared to zero, and n is updated to n+1, and the step of setting the element in the last column of the nth row of the initial state matrix to the disturbance gain is performed again until it is satisfied.

6. The method according to claim 5, characterized in that Determine whether the state matrix, output matrix, current state matrix, and current output matrix meet the Hautus observability conditions, including: Determine whether the following equation is true: Where A represents the state matrix, C represents the output matrix, I represents the identity matrix, and Gd represents the perturbation state matrix in the expanded state matrix.

7. The method according to claim 3, characterized in that Also includes: expanding the input matrix B as follows: Among them, B1 represents the current input matrix, B total The initial value of is the input matrix B, O nd,nu represents a zero matrix with nd rows and nu columns, where nu is the number of columns in B.

8. A device for generating an estimator, characterized in that: include: An acquisition unit, configured to acquire a model matrix in an original state space model and matrix parameters of the model matrix; The model matrix includes an output matrix and a state matrix of the controlled object, and the matrix parameters include the number of rows of the output matrix; an expansion unit, configured to expand the model matrix based on the matrix parameters to obtain an extended matrix, wherein the extended matrix satisfies a Hautus observability condition, the extended matrix includes an extended state matrix obtained by expanding the state matrix, the number of first columns newly added to the extended state matrix relative to the state matrix is consistent with the number of rows of the output matrix, and the extended matrix satisfies a Hautus observability condition; A generating unit is configured to establish an estimator based on the extended matrix.

9. An electronic device, characterized in that: including memory and processor; The memory is connected to the processor and is used to store programs; The processor is configured to implement the method for generating an estimator according to any one of claims 1 to 7 by running the program in the memory.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the method for generating an estimator according to any one of claims 1 to 7 is implemented.

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