Method, device and equipment for temperature adaptive compensation of an inductive synchronizer

Abstract

The application relates to the technical field of inductive synchro technology, and discloses an inductive synchro temperature self-adaptive compensation method, device and equipment, wherein the method comprises the following steps: determining initial linear coefficients of an initial linear model; acquiring a current environment temperature and a current measurement angle of the inductive synchro, and determining initial iteration parameters of the initial linear model according to the current environment temperature and the current measurement angle; iteratively updating the initial linear coefficients according to the initial iteration parameters to obtain target linear coefficients, wherein data format conversion is performed in each iteration updating process, and the next iteration is performed according to the conversion result; updating the initial linear model based on the target linear coefficients to obtain an updated target linear model, and determining a measurement angle compensation value of the inductive synchro according to the target linear model. The application can realize high-precision and self-adaptive real-time temperature compensation of the inductive synchro in a hot vacuum environment, and simultaneously adapt to the hardware design and reliability requirements of an aircraft.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of inductive synchro, and particularly to an inductive synchro temperature adaptive compensation method, device and equipment. BACKGROUND

[0002] As a high-precision angle measurement device, the inductive synchro is widely used in remote sensing observation systems of spacecraft such as high-orbit satellites. However, in the vacuum environment of the spacecraft, extreme temperature changes will change the electromagnetic coupling gap between the stator and the rotor of the inductive synchro, causing the measurement results of the inductive synchro to deviate, and therefore the measurement results of the inductive synchro need to be compensated.

[0003] In related technologies, the commonly used static compensation schemes such as linear fitting and table lookup cannot track the dynamic changes of temperature in the hot vacuum environment in real time, and in the scene of aerospace and other harsh scenes with high requirements for hardware resources and reliability, the hardware adaptability and anti-radiation interference capability of the related technologies are also difficult to meet the design requirements of the aircraft. SUMMARY

[0004] The present application provides an inductive synchro temperature adaptive compensation method, device and equipment, which solves the technical problem that related technologies cannot track the dynamic changes of temperature in the hot vacuum environment in real time and are difficult to meet the design requirements of the aircraft, and realizes high-precision and adaptive real-time temperature compensation of the inductive synchro in the hot vacuum environment, while adapting to the hardware design and reliability requirements of the aircraft.

[0005] In order to achieve the above purpose, the main technical scheme adopted by the present application includes:

[0006] In a first aspect, the present application provides an inductive synchro temperature adaptive compensation method, which comprises:

[0007] determining an initial linear coefficient of an initial linear model;

[0008] obtaining a current environment temperature and a current measurement angle of the inductive synchro, and determining an initial iteration parameter of the initial linear model according to the current environment temperature and the current measurement angle;

[0009] iteratively updating the initial linear coefficient according to the initial iteration parameter to obtain a target linear coefficient, wherein data format conversion is performed in each iteration update process, and the next iteration is performed according to the conversion result;

[0010] updating the initial linear model based on the target linear coefficient to obtain an updated target linear model, and determining a measurement angle compensation value of the inductive synchro according to the target linear model.

[0011] The temperature self-adaptive compensation method of the inductive synchro proposed in the embodiments of the present application uses the recursive least squares method to iteratively update the linear coefficient between the ambient temperature and the measured angle, so that the model can timely and accurately track the change of the measured angle caused by the temperature change of the inductive synchro and perform compensation. In addition, the data fixed-point format conversion is performed on the corresponding iteration parameters in each iteration update process, which significantly reduces the resource occupation rate of floating point operation, overcomes the disadvantage that the traditional adaptive algorithm is difficult to be applied to the aviation level field programmable gate array (FPGA) system, and makes the iteration calculation process adapt to the aircraft hardware design, so as to balance the iteration algorithm accuracy and the reasonable use of FPGA resources, thereby realizing high-precision adaptive compensation of the inductive synchro.

[0012] Optionally, in some embodiments of the present application, the method is applied to an FPGA system, and the data fixed-point format of the conversion result matches the number of data buses of the FPGA system.

[0013] The data fixed-point format conversion of the embodiments of the present application enables the corresponding iteration parameters and linear coefficients in each iteration update process to be directly calculated on the FPGA system, effectively avoiding the floating point overflow problem that may occur due to the mismatch between the data fixed-point format and the data bus of the FPGA system, reducing the calculation resource occupation of the iteration update calculation in the spaceflight level FPGA system, and greatly improving the iteration update efficiency.

[0014] Optionally, in some embodiments of the present application, the initial linear model represents a negative correlation mapping relationship between the error of the current measured angle of the inductive synchro and the current ambient temperature, wherein the reciprocal of the current ambient temperature is an input variable of the initial linear model.

[0015] After obtaining the current ambient temperature of the inductive synchro, the method further comprises:

[0016] Approximate calculation is performed on the reciprocal of the current ambient temperature based on the Newton iteration method, so as to represent the input variable of the initial linear model according to the approximate calculation result.

[0017] The embodiments of the present application use the Newton iteration method to approximately calculate the reciprocal of the current ambient temperature, and represent the input variable of the model with the result, so as to avoid complex division operation of the FPGA system, thereby significantly reducing the calculation complexity and further improving the iteration update calculation efficiency.

[0018] Optionally, in some embodiments of the present application, the initial iteration parameters of the initial linear model are determined according to the current ambient temperature and the current measured angle, comprising:

[0019] determining a regression vector according to the current environment temperature and a preset constant term;

[0020] determining an error observation value according to the current measurement angle and a reference angle;

[0021] constructing a covariance matrix according to a preset initial value;

[0022] taking the regression vector, the error observation value and the covariance matrix as the initial iteration parameters;

[0023] The reference angle is pre-stored in a reference database.

[0024] Optionally, in some embodiments of the present application, the reference database is determined by:

[0025] determining a preset reference temperature, and respectively measuring each sampling point of the inductive synchro at the reference temperature to obtain the reference angle corresponding to each sampling point;

[0026] associating the reference temperature, the sampling points and the corresponding reference angles and storing them into the reference database.

[0027] The embodiments of the present application utilize the reference temperature and the corresponding reference angle to provide a unified reference for the measurement of the inductive synchro, thereby enabling real-time and accurate adaptive compensation of the measurement results of the inductive synchro under different environment temperatures.

[0028] Optionally, in some embodiments of the present application, the initial linear coefficients are iteratively updated to obtain target linear coefficients according to the initial iteration parameters, wherein data format conversion is performed in each iteration update process, and the next iteration is performed according to the conversion result, including:

[0029] determining a raw residual vector corresponding to the current iteration according to the error observation value, the regression vector and the initial linear coefficients, and performing data fixed-point format conversion on the raw residual vector based on a first preset fixed-point conversion rule to obtain a converted residual vector;

[0030] determining a raw gain vector corresponding to the current iteration according to the regression vector and the covariance matrix, and performing data fixed-point format conversion on the raw gain vector based on a second preset fixed-point conversion rule to obtain a converted gain vector;

[0031] updating the initial linear coefficients and the initial iteration parameters according to the converted gain vector and the converted residual vector based on a recursive least square method to obtain updated linear coefficients and updated iteration parameters;

[0032] The updated linear coefficient and the updated iteration parameter are subjected to data fixed-point format conversion based on a third preset fixed-point conversion rule to obtain a converted updated linear coefficient and a converted updated iteration parameter for next iteration, and the converted updated linear coefficient is taken as the target linear coefficient when a preset iteration stop condition is met.

[0033] The embodiments of the present application utilize error observation values, regression vectors, initial linear coefficients and covariance matrices to determine original gain vectors and original residual vectors in the iteration process based on the recursive least square method, and perform fixed-point truncation on the original gain vectors and the original residual vectors based on corresponding first and second preset fixed-point conversion rules respectively to solve the floating point overflow in the iteration update calculation process, which ensures that the updated iteration parameter obtained by iteration update can match the hardware of the FPGA system, effectively reduces the occupation of the FPGA system calculation resources by the iteration update calculation, and significantly improves the iteration update calculation efficiency, thereby facilitating the real-time adaptive compensation of the induction synchronizer.

[0034] Optionally, in some embodiments of the present application, the method further comprises storing the converted updated linear coefficient and the converted updated iteration parameter through registers in each iteration update process;

[0035] Among the converted updated linear coefficient and the converted updated iteration parameter, key data are pre-selected, and each key data corresponds to a plurality of registers with redundant design for backup.

[0036] The embodiments of the present application store key updated iteration parameters in registers with redundant design, and read correct parameters through majority voting when the key updated iteration parameters are needed to participate in iteration calculation, thereby effectively protecting the reading of the key updated iteration parameters from the influence of single event upset (SEU) in the space environment, ensuring the data integrity and accuracy of the converted updated iteration parameter participating in the next iteration each time, and making the adaptive compensation of the induction synchronizer meet the reliability requirements of space level.

[0037] Optionally, in some embodiments of the present application, the reference database further pre-stores an angle mean value of the reference angle;

[0038] The target compensation value of the measured angle of the induction synchronizer is determined according to the target linear model, comprising:

[0039] An initial compensation value corresponding to the current environment temperature is determined according to the target linear model;

[0040] The target compensation value is determined according to the difference between the initial compensation value and the angle mean value.

[0041] The embodiment of the present application first obtains an initial compensation value corresponding to the current environment temperature according to a target linear model, and then determines a target compensation value through the difference between the initial compensation value and the average angle, so as to ensure that the adaptive compensation of each channel of the induction synchro is mapped to the same reference temperature, effectively eliminates the compensation error caused by the temperature drift characteristics of the channel, and further improves the accuracy of adaptive compensation.

[0042] In a second aspect, the embodiment of the present application provides an induction synchro temperature adaptive compensation device, which comprises:

[0043] A modeling module is configured to determine initial linear coefficients of an initial linear model, and obtain a current environment temperature and a current measurement angle of the induction synchro, and determine initial iteration parameters of the initial linear model according to the current environment temperature and the current measurement angle;

[0044] An iteration module is configured to iteratively update the initial linear coefficients to obtain target linear coefficients according to the initial iteration parameters, wherein data format conversion is performed in each iteration update process, and the next iteration is performed according to the conversion result;

[0045] A compensation module is configured to update the initial linear model based on the target linear coefficients to obtain an updated target linear model, and determine a measurement angle compensation value of the induction synchro according to the target linear model.

[0046] The induction synchro temperature adaptive compensation device provided by the embodiment of the present application iteratively updates the linear coefficients between the environment temperature and the measurement angle by using the recursive least square method, so that the model can timely and accurately track the measurement angle change of the induction synchro caused by temperature change and perform compensation. In addition, the corresponding iteration parameters are subjected to data fixed-point format conversion in each iteration update process, which significantly reduces the resource occupancy rate of floating point operation, overcomes the disadvantage that the traditional adaptive algorithm is difficult to be applied to the aviation level FPGA system, and makes the iteration calculation process adaptable to the aircraft hardware design, so as to balance the iteration algorithm accuracy and the reasonable use of FPGA resources, thereby realizing high-precision adaptive compensation of the induction synchro.

[0047] In a third aspect, the embodiment of the present application provides a computer device, which comprises:

[0048] A memory and a processor, which are communicatively connected with each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the induction synchro temperature adaptive compensation method provided in the above embodiment.

[0049] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, having stored thereon computer instructions for causing a computer to execute the inductive synchronizer temperature adaptive compensation method described in the above embodiments.

[0050] In a fifth aspect, an embodiment of the present application provides a computer program product, comprising computer instructions for causing a computer to execute the inductive synchronizer temperature adaptive compensation method described in the above embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the specific embodiments or prior art in the present application, the drawings needed to be used in the specific embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0052] Figure 1 One of the flowcharts of the inductive synchronizer temperature adaptive compensation method provided by an embodiment of the present application;

[0053] Figure 2 The second flowchart of the inductive synchronizer temperature adaptive compensation method provided by an embodiment of the present application;

[0054] Figure 3 The third flowchart of the inductive synchronizer temperature adaptive compensation method provided by an embodiment of the present application;

[0055] Figure 4 The structural schematic diagram of the inductive synchronizer temperature adaptive compensation device provided by an embodiment of the present application;

[0056] Figure 5 The structural schematic diagram of the computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some 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 those skilled in the art without creative labor fall within the scope of protection of the present application.

[0058] The inductive synchro causes the measurement result to deviate due to thermal expansion characteristics. In order to compensate for the deviation of the measurement result, the related art often uses a static compensation scheme such as linear fitting and table lookup. However, the compensation precision of linear fitting is difficult to meet the precision requirement of the space-level inductive synchro, and can only be adapted to the normal temperature environment with small temperature range variation. The compensation response of the table lookup method has a lag, and the inductive synchro in the thermal vacuum environment faces more drastic changes in the environmental temperature, for example, a temperature sudden change of 5℃ per minute occurs when the satellite enters or exits the shadow area, and therefore cannot track the dynamic changes of the temperature in the thermal vacuum environment in real time.

[0059] In addition, the look-up table (LUT) and digital signal processor (DSP) resources of the space-level anti-fuse FPGA are limited, and the traditional adaptive algorithm needs to use floating point operation, which has too high resource occupation and is difficult to deploy on the space-level anti-fuse FPGA with limited resources.

[0060] Therefore, how to realize real-time adaptive compensation for the measurement deviation of the inductive synchro in the thermal vacuum environment is a problem to be solved at present.

[0061] According to the embodiment of the present application, an inductive synchro temperature adaptive compensation method embodiment 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 group 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.

[0062] Figure 1 is a flowchart of the inductive synchro temperature adaptive compensation method according to the embodiment of the present application, as shown in Figure 1 The flowchart includes the following steps:

[0063] Step S1, determining an initial linear coefficient of an initial linear model.

[0064] Specifically, the initial linear model represents the linear relationship between the error of the current measurement angle and the current environmental temperature, and therefore the initial linear coefficient includes the slope and the intercept.

[0065] Step S3, obtaining the current environmental temperature and the current measurement angle of the inductive synchro, and determining the initial iteration parameter of the initial linear model according to the current environmental temperature and the current measurement angle.

[0066] Specifically, the current ambient temperature is acquired by a temperature sensor arranged near the induction synchronizer, and a current measured angle output by the induction synchronizer at the current ambient temperature is acquired. It should be noted that in the embodiments of the present application, the angles output by the coarse channel and the fine channel of the induction synchronizer are measured respectively and then compensated adaptively, and therefore the above-mentioned current measured angle refers to the angle output by the coarse channel or the fine channel.

[0067] The embodiments of the present application update and estimate the slope and the intercept of the initial linear model in real time by using the Recursive Least Squares (RLS). The RLS is an algorithm capable of updating the estimated value constantly according to new measurement data. In order to perform the RLS iterative calculation, the embodiments of the present application determine part of the initial iterative parameters participating in the RLS iterative calculation according to the acquired current ambient temperature and current measured angle, and set the initial values of the slope, the intercept of the initial linear model and the covariance matrix participating in the RLS iterative calculation to determine another part of the initial iterative parameters. The RLS iterative calculation is started with these initial iterative parameters to realize the real-time update and estimation of the slope and the intercept.

[0068] In step S5, the initial linear coefficient is updated iteratively according to the initial iterative parameters to obtain a target linear coefficient, wherein the data format conversion is performed in each iterative update process, and the next iteration is performed according to the conversion result.

[0069] Specifically, the embodiments of the present application first update the initial iterative parameters based on the RLS, and then perform the next RLS calculation based on the updated iterative parameters, until the preset iteration stopping condition is met, so as to obtain the target linear coefficient according to the last iterative update. Preferably, in the case that the error of the results of the adjacent two iterative updates is less than a preset threshold, it is determined that the preset iteration stopping condition is met.

[0070] In addition, in the iteration process, in order to enable the iterative calculation algorithm to be applied to the space-level FPGA system, the embodiments of the present application perform the data fixed-point format conversion on the corresponding iterative parameters in each iterative update process, so as to avoid the floating-point overflow in the iterative calculation process and affect the subsequent iterative calculation.

[0071] In step S7, the initial linear model is updated based on the target linear coefficient to obtain an updated target linear model, and the measurement angle compensation value of the induction synchronizer is determined according to the target linear model.

[0072] Specifically, the target linear coefficient is substituted into the initial linear model to replace the original initial linear coefficient, so as to update the initial linear model, and generate a target linear model which can more accurately describe the relationship between the angle fluctuation of the inductive synchro measured and the ambient temperature. Then, the current ambient temperature is input into the target linear model, and the compensation value of the inductive synchro measured angle is determined according to the measured angle fluctuation output by the model, so as to correct the measured angle deviation of the inductive synchro caused by the change of the ambient temperature, and improve the measurement accuracy.

[0073] The inductive synchro temperature adaptive compensation method provided in the embodiments of the present application uses the recursive least square method to iteratively update the linear coefficient between the ambient temperature and the measured angle, so that the model can timely and accurately track the change of the measured angle of the inductive synchro caused by the change of the temperature and perform compensation. In addition, the data fixed-point format conversion is performed on the corresponding iterative parameters in each iterative update process, which significantly reduces the resource occupation rate of floating point operation, overcomes the disadvantage that the traditional adaptive algorithm is difficult to be applied to the aviation level FPGA system, and makes the iterative calculation process adaptable to the aircraft hardware design, so as to balance the accuracy of the iterative algorithm and the reasonable use of FPGA resources, thereby realizing high-precision adaptive compensation of the inductive synchro.

[0074] Further, in some embodiments of the present application, the method is applied to an FPGA system, and the data fixed-point format of the conversion result matches the number of data buses of the FPGA system.

[0075] Specifically, the FPGA system is composed of a large number of logic units and data paths, and the data bus is a key channel for transmitting data, and the number of data buses determines the number of data bits that can be transmitted in parallel at a time. In the embodiments of the present application, the initial linear coefficient and the initial iterative parameter are respectively preset with corresponding data fixed-point formats, so as to match the number of data buses of the FPGA system. For example, if the number of data buses of the FPGA system is 16 bits, the preset data fixed-point format needs to satisfy the total data bit number of 16 bits. In each iterative update process, the data format conversion is performed, and each conversion result also needs to satisfy the total data bit number of 16 bits.

[0076] The data fixed-point format conversion in the embodiments of the present application enables the corresponding iterative parameters in each iterative update process to be directly applied to the FPGA system for calculation, effectively avoids the floating point overflow problem that may occur due to the mismatch between the data fixed-point format and the data bus of the FPGA system, reduces the calculation resource occupation of the iterative update calculation in the space level FPGA system, and greatly improves the iterative update efficiency.

[0077] In some embodiments of the present application, the initial linear model represents a negative correlation between the fluctuation of the current measured angle of the inductive synchro and the ambient temperature. That is, as the current ambient temperature rises, the fluctuation of the current measured angle of the inductive synchro tends to decrease.

[0078] The following formula (1) shows the initial linear model, where the reciprocal of the current ambient temperature is the input variable of the initial linear model, and the fluctuation of the measured angle of the inductive synchro is the output variable of the initial linear model:

[0079]

[0080] In the formula, T is the current ambient temperature, is the error of the current measured angle of the inductive synchro at the current ambient temperature, is the slope, and is the intercept.

[0081] It should be noted that in some embodiments of the present application, the reference angle of the inductive synchro at the reference temperature is determined in advance, and the difference between the current measured angle and the reference angle is taken as the error of the current measured angle.

[0082] The reference temperature and the reference angle are pre-stored in a reference database, and further, in some embodiments of the present application, the reference database is determined by the following method:

[0083] A preset reference temperature is determined, and each sampling point of the inductive synchro is measured at the reference temperature to obtain the reference angle corresponding to each sampling point.

[0084] The reference temperature, the sampling points, and the corresponding reference angles are associated and stored in the reference database.

[0085] Specifically, in the embodiments of the present application, 25℃ is taken as the reference temperature, that is, the sampling points of the inductive synchro are measured at room temperature, so that the reference angle output by the inductive synchro at room temperature is taken as the reference for subsequent determination of the fluctuation of the current measured angle. The reference angle includes the coarse channel reference angle and the fine channel reference angle output by the coarse channel and the fine channel of the inductive synchro at each sampling point at the reference temperature.

[0086] For the coarse channel, 65535 sampling points are set in the full range interval, and each sampling point is measured repeatedly 100 times, so that the coarse channel reference angle is obtained , , . The coarse channel reference angle ​​The reference angle is stored in a Block Random Access Memory (BRAM) in the FPGA system and occupies 1 kB of storage space of the BRAM.

[0087] For the fine channel, the fine channel measurement period is divided into j phase intervals as sampling points, and 100 times of measurement are repeated at each sampling point, so as to obtain the fine channel reference angle , , The fine channel reference angle The reference angle is stored in a BRAM in the FPGA system and occupies 720B of storage space of the BRAM.

[0088] The embodiment of the application utilizes the reference temperature and the corresponding reference angle to provide a unified reference for the measurement of the inductive synchronizer, so that the measurement results of the inductive synchronizer under different environmental temperatures can be adaptively compensated in real time and accurately.

[0089] Further, after obtaining the current environmental temperature of the inductive synchronizer, the method further comprises:

[0090] The reciprocal of the current environmental temperature is calculated based on the Newton iteration method, so as to represent the input variable of the initial linear model according to the calculation result.

[0091] Specifically, in order to realize the reciprocal operation of the current environmental temperature, the reciprocal of the current environmental temperature is calculated by using the Newton iteration method through the following formula (2):

[0092] ;

[0093] In the formula, is the approximate result of the reciprocal of the current environmental temperature corresponding to the n+1th Newton iteration, is the approximate result of the reciprocal of the current environmental temperature corresponding to the nth Newton iteration, and x0 is a preset initial value when n=0.

[0094] The derivation process of the above formula (2) is as follows:

[0095] In the embodiment of the application, the equation to be solved is determined according to the to-be-solved The solving equation is shown in the following formula (3):

[0096] (3);

[0097] The derivative of formula (3) is obtained as formula (4):

[0098] (4);

[0099] The core formula of Newton iteration method is shown in formula (5), and the above formula (2) can be obtained by substituting and into formula (5):

[0100] - (5) ;

[0101] Through multiple Newton iterations, the reciprocal of the current environment temperature will gradually approach the real value, and when the difference between the results of adjacent two iterations and is less than a pre-set precision threshold, it is considered that sufficient approximation precision is reached, and at this time is the approximation value of the reciprocal of the current environment temperature.

[0102] In this way, compared with directly using the current environment temperature T as the input of the initial linear model, complex division operations are avoided in subsequent RLS iteration calculation. Because the implementation of division operation on FPGA coefficient is relatively complex and consumes more resources, the Newton iteration method in the embodiments of the present application is mainly implemented through multiplication and addition and subtraction operations, which are easier to implement in the FPGA system, and significantly reduce the calculation complexity.

[0103] The embodiments of the present application use Newton iteration method to approximately calculate the reciprocal of the current environment temperature, and use the result as the input variable of the model, so as to avoid complex division operation of the FPGA system, thereby significantly reducing the calculation complexity and further improving the iteration update calculation efficiency.

[0104] In some embodiments of the present application, the above initial iteration parameters include a regression vector, an error observation value and a covariance matrix, and the specific determination method of these initial iteration parameters includes the following flow:

[0105] The regression vector is determined according to the current environment temperature and a pre-set constant term, and specifically, the regression vector is shown in the following formula (6):

[0106] (6) ;

[0107] In the formula, is the regression vector corresponding to the kth iteration, is the absolute value of the current environment temperature corresponding to the kth iteration, is a reference temperature, and the current environment temperature , and the above pre-set constant term is 1.

[0108] Wherein, the kth iteration corresponds to the kth moment, that is, T k is the absolute value of the current ambient temperature at the kth moment, which is collected by a platinum resistance set on the inductive synchronizer in real time and converted into a digital quantity by a 16-bit digital-to-analog converter (ADC) of the FPGA. T ref is the reference temperature of 25 DEG C. The difference between the absolute value of the current ambient temperature at the kth moment and the reference temperature is calculated to obtain the current ambient temperature T, that is, the first element of the regression vector, and the data format of the first element is set to Q16.0. The reciprocal of the current ambient temperature T is calculated by Newton iteration based on the above formula (2), and the data format of the calculated reciprocal is set to Q0.16. Finally, the reciprocal of the current ambient temperature T and the constant term "1" are assembled into the regression vector corresponding to the kth iteration .

[0109] The error observation value is determined according to the current measurement angle and the reference angle, and specifically, the error observation value is shown in the following formula (7):

[0110] (7) ;

[0111] In the formula, T k is the error observation value corresponding to the kth iteration, T k is the current measurement angle of the fine channel or the coarse channel at the kth moment, which is a variable that changes with the iteration update. T ref is the reference angle, that is, the measurement angle of the channel or the coarse channel at the reference temperature of 25 DEG C, which is pre-stored in the BRAM of the FPGA and is a fixed value that does not update with the iteration.

[0112] The covariance matrix is constructed according to the preset initial value, and specifically, in some embodiments of the present application, the preset initial value of the covariance matrix is wherein, I is an identity matrix, indicating that the result of the initial iteration has high uncertainty.

[0113] Finally, the regression vector, the error observation value and the covariance matrix are used as the initial iteration parameters.

[0114] In some embodiments of the present application, the data format, value range and accuracy of the preset conditions of the above regression vector, error observation value, covariance matrix and initial linear coefficient are shown in Table 1 as follows:

[0115] Table 1

[0116]

[0117] Therefore, in order to map floating-point variables to 16-bit fixed-point representations and match the 16-bit data bus size of the FPGA coefficients, the data format of each variable adopts the format Qm.n, where m represents the integer digits of the variable and n represents the decimal digits, satisfying the fixed-point constraint that the sum of the integer and decimal digits equals 16, i.e., m + n = 16. Furthermore, when two data items in Qm1.n1 and Qm2.n2 formats are multiplied, the product result is in the format Qa.b, where a = m1 + m2 and b = n1 + n2.

[0118] Figure 2 A flowchart of step S5 above is shown, as follows: Figure 2 As shown, step S5 above includes the following process steps:

[0119] Step S51: Determine the original residual vector corresponding to the current iteration based on the error observation, regression vector and initial linear coefficients, and perform data fixed-point format conversion on the original residual vector based on the first preset fixed-point conversion rule to obtain the converted residual vector.

[0120] Specifically, the original residual vector is shown in the following formula (8):

[0121] (8);

[0122] In the formula, This is the original residual vector corresponding to the k-th iteration. This represents the error observation value corresponding to the k-th iteration. Let be the transpose of the regression vector corresponding to the k-th iteration. These are the linear coefficients corresponding to the (k-1)th iteration. When k=1, the linear coefficients are... Including the slope with a pre-set initial value and intercept .

[0123] Among them, inner product The calculation is performed using two 16×16 multipliers, i.e., a Digital Signal Processor (DSP48). The first multiplier is used to calculate... and slope The product of . As can be seen from Table 1 above, The data format is Q0.16. Q15.1, the data format of the first product result obtained after multiplication of the two is Q15.17, in order to match the number of 16-bit data buses of the FPGA system, the embodiment of the application converts the first product result to the data format of Q8.8 after shifting the decimal point of the first product result to the left by 1 bit through the first preset point conversion rule, that is, the data format of the first vector element of the converted residual vector is Q8.8. The first multiplier is used to calculate the product of the constant term "1" and the intercept The data format of the constant term "1" is Q16.0, The data format of the intercept is Q8.8, and the data format of the second product result obtained after multiplication of the two is Q24.8. In order to match the number of 16-bit data buses of the FPGA system, the embodiment of the application converts the second product result to the data format of Q8.8 after shifting the decimal point of the second product result to the right by 8 bits through the first preset point conversion rule, that is, the data format of the second vector element of the converted residual vector is Q8.8.

[0124] and The subtraction operation of the constant term "1" and the intercept is implemented through a 16-bit carry chain subtractor combined with saturation logic, so that the difference value is always kept within the range of ±256 codes, effectively preventing the problem of parameter divergence caused by data overflow.

[0125] Step S53, determining the original gain vector corresponding to the current iteration according to the regression vector and the covariance matrix, and performing data fixed-point format conversion on the original gain vector based on the second preset point conversion rule to obtain the converted gain vector.

[0126] Specifically, the original gain vector is as shown in the following formula (9):

[0127] (9) ;

[0128] In the formula, g(k) is the original gain vector corresponding to the kth iteration, is the covariance matrix corresponding to the (k-1) th iteration, is the regression vector corresponding to the kth iteration, is the transposed vector of the regression vector corresponding to the kth iteration. The inner product of the numerator

[0129] is implemented through a 2x2 matrix vector multiplier, that is, four 16x16 multipliers and two adders. Each element of the result 2x1 vector is truncated to Q15.1 to align with .

[0130] For the denominator , first calculate inner product of, whose inner product calculation formula is shown in the following formula (10):

[0131] (10);

[0132] wherein, and are respectively the first element and the second element of , , and are respectively the first row first column element, the first row second column element, the second column first row element and the second column second row element of

[0133] Since the covariance matrix is a symmetric matrix, i.e. = , the above formula (10) can be simplified as the following formula (11):

[0134] (11);

[0135] For the first term of the above formula (11), first calculate , Q0.16 of , so the data format of is Q0.32, and its physical meaning is , and its value range is . As can be seen, the calculation result of does not need integer bit, only 32-bit decimal. Then multiply and , the data format of is Q12.4, so the data format of the first term is Q12.36. In order to adapt to the operation hardware conditions of FPGA, and to ensure that the deformation compensation precision of the inductive synchronizer under hot vacuum is met, the decimal point is right shifted by 20 bits, i.e. only 16 decimal is reserved, and 4 integer bits are supplemented, and finally the data format of the first term of formula (11) is Q16.16.

[0136] For the second term of the above formula (11), first the data format of is Q0.16, the data format of is Q16.0, so the data format of is Q16.16, and its physical meaning is , and its value range is . Then multiply and , The data format of the first term is Q12.4, and the multiplication result is multiplied by the constant term "2" with a data format of Q1.0, so that the data format of the second term is Q28.20. Similarly, the decimal point is right shifted by 4 bits, and finally the data format of the second term of formula (11) is Q16.16.

[0137] For the third term of formula (11) above, The data format of the first term is Q12.4, and the multiplication result is multiplied by the constant term "2" with a data format of Q1.0, so that the data format of the second term is Q28.20. Similarly, the decimal point is right shifted by 4 bits, and finally the data format of the second term of formula (11) is Q16.16. The data format is Q32.0. Again, is multiplied by The data format of the first term is Q12.4, and the multiplication result is multiplied by the constant term "2" with a data format of Q1.0, so that the data format of the second term is Q28.20. Similarly, the decimal point is right shifted by 4 bits, and finally the data format of the second term of formula (11) is Q16.16.

[0138] In this way, the data format of the three terms in formula (11) is Q16.16, and the inner product is obtained after addition, and the final format of the inner product is Q16.16.

[0139] For the addition operation of the inner product and the constant term "1", first, the data format of the constant term "1" is Q16.0, and the decimal point of the constant term "1" is left shifted by 16 bits and the decimal places are filled with 0, so that the data format is converted to Q16.16. Then, it is added to the inner product with a data format of Q16.16, and the addition result has a format of Q16.16. Finally, the integer processing is performed, that is, the decimal point of the addition result is right shifted by 16 bits, that is, the decimal places are discarded, and the data format of the denominator is Q16.0, which is beneficial to subsequent simplification of the division.

[0140] The reciprocal of the denominator is stored in the reciprocal lookup table (LUT), which is pre-stored in the 16KB BRAM. Specifically, for the denominator with a value range of 1 to 100, the reciprocal range is 0.01 to 1, and it can be seen that the reciprocal of the denominator does not need integer bits, and needs to keep enough decimal places to meet the compensation accuracy. The data format stored in the reciprocal lookup table in the embodiment of the application is Q0.16, which ensures that the accuracy is .

[0141] ​The reciprocal lookup table can avoid real-time division operation, so that the reciprocal calculation of the denominator can be completed in two clock cycles of the FPGA system, the operation error is less than 0.1%, the compensation accuracy requirement in the hot vacuum environment is met, the 16-bit operation resource of the space-level FPGA is adapted, the division period is compressed to 20ns, the iteration frequency of 12.5kHz is supported, and the temperature change of 5℃ / min can be tracked in real time.

[0142] In step S55, the initial linear coefficient is updated based on the converted gain vector and the converted residual vector according to the recursive least square method, and the initial iteration parameter is updated to obtain an updated linear coefficient and an updated iteration parameter.

[0143] Specifically, the initial linear coefficient is updated based on the iteration update formula, and then the initial iteration parameter is updated, and then the next iteration update calculation is performed by using the updated linear coefficient and the updated iteration parameter, and the iteration is performed in sequence.

[0144] The iteration update calculation of the linear coefficient is shown in the following formula (12):

[0145] (12) ;

[0146] In the formula, is the linear coefficient corresponding to the k-1th iteration, is the linear coefficient corresponding to the kth iteration.

[0147] The iteration update calculation of the covariance matrix is shown in the following formula (13):

[0148] (13) ;

[0149] In the formula, is the covariance matrix corresponding to the k-1th iteration, is the covariance matrix corresponding to the kth iteration, is a forgetting factor, which is used to balance the data weight of the k-1th iteration and the kth iteration.

[0150] Therefore, the updated linear coefficient and the updated iteration parameter are obtained by calculating the linear coefficient and the iteration parameter corresponding to the k-1th iteration according to the above formula (12) and formula (13), and the corresponding and y are updated according to the current measurement angle and the absolute value of the current environmental temperature k at the kth moment in each iteration update process.

[0151] In step S57, the updated linear coefficient and the updated iteration parameter are subjected to data fixed-point format conversion based on a third preset fixed-point conversion rule to obtain a converted updated linear coefficient and a converted updated iteration parameter for the next iteration, and the converted updated linear coefficient is taken as the target linear coefficient when a preset iteration stop condition is met.

[0152] Specifically, the embodiment of the application uses two 16x1 multipliers to calculate the 2x1 original gain vector and the inner product of the original residual vector , wherein the data format of the original gain vector is Q15.1, and the data format of the original residual vector is Q8.8. For the calculation result of the inner product of the two, the third preset fixed-point conversion rule is used to right shift the result by 8 bits to reserve the Q15.1 data format for and the Q8.8 data format for .

[0153] The embodiment of the application uses the error observation value, the regression vector, the initial linear coefficient and the covariance matrix to determine the original gain vector and the original residual vector in the iteration process based on the recursive least square method, and performs fixed-point truncation on the original gain vector and the original residual vector based on the corresponding first preset fixed-point conversion rule and the second preset fixed-point conversion rule, respectively, to solve the floating point overflow in the iteration update calculation process, which ensures that the updated iteration parameter obtained by the iteration update can be matched with the hardware of the FPGA system, effectively reduces the occupation of the FPGA system calculation resources by the iteration update calculation, and significantly improves the iteration update calculation efficiency, thereby facilitating the real-time adaptive compensation of the induction synchronizer.

[0154] In some embodiments of the application, the FPGA system includes a plurality of registers and a plurality of voters, and the method further includes storing the converted updated linear coefficient and the converted updated iteration parameter in the registers in each iteration update process. Among the converted updated linear coefficient and the converted updated iteration parameter, key data are pre-selected, and each key data corresponds to a plurality of registers designed with redundancy for backup.

[0155] Specifically, in order to meet the space-level reliability requirement, the embodiment of the application uses three redundant registers to store the key data , , and selects the majority of the same data in the three registers through a majority voter when reading the data, for example, When the data stored in the first register and the second register are the same, and the data stored in the third register is different from the data stored in the first register and the second register, the data stored in the first register and the second register are selected by the majority voter to participate in the calculation, thereby avoiding data errors caused by single event upsets.

[0156] In addition, 5 ns and 5 ns margins are reserved for register setup time and hold time respectively, which can effectively compensate for timing drift caused by radiation.

[0157] In addition, the application embodiment also uses the error detection and correction (EDAC) technology of the BRAM to implement the reciprocal lookup table and The initial value of the reciprocal lookup table is protected by a Hamming (7, 4) code to achieve 1-bit error correction and 2-bit error detection.

[0158] The application embodiment adopts a redundant design register to store the key update iteration parameter, and reads the correct parameter through majority voting when the key update iteration parameter needs to participate in the iteration calculation, thereby effectively protecting the reading of the key update iteration parameter from the SEU in the space environment, ensuring the data integrity and accuracy of the conversion update iteration parameter participating in the next iteration each time, and making the adaptive compensation of the induction synchronizer meet the reliability requirements of the space level.

[0159] Further, in some embodiments of the application, the reference database also pre-stores an angle mean of the reference angle.

[0160] Specifically, in the application embodiment, the angle mean includes a coarse channel angle mean and a fine channel angle mean.

[0161] The coarse channel angle mean is calculated based on the following formula (14):

[0162] ;

[0163] In the formula, , , The coarse channel angle mean is denoted as, and the value is 100.

[0164] The fine channel angle mean is calculated based on the following formula (15):

[0165] (15);

[0166] In the formula, , , the angle mean corresponding to the i-th phase interval of the fine channel, is 100.

[0167] Figure 3 A flowchart of step S7 is shown as follows, Figure 3 Step S7 includes the following flow steps:

[0168] Step S71, determining an initial compensation value corresponding to the current environment temperature according to the target linear model.

[0169] Specifically, the current environment temperature is input into the target linear model to obtain the initial compensation value corresponding to the coarse channel and the initial compensation value corresponding to the coarse channel .

[0170] Step S73, determining a target compensation value according to the difference between the initial compensation value and the angle mean.

[0171] Specifically, the difference between and is taken as the target compensation value for the coarse channel, and the difference between and is taken as the target compensation value corresponding to the i-th phase interval of the fine channel. In this way, when the coarse channel and the fine channel are adaptively compensated, the temperature drift characteristics of the coarse and fine channels can be mapped to the same reference temperature.

[0172] The embodiment of the present application first obtains the initial compensation value corresponding to the current environment temperature according to the target linear model, and then determines the target compensation value through the difference between the initial compensation value and the angle mean, so as to ensure that the adaptive compensation of each channel of the inductive synchro is mapped to the same reference temperature, effectively eliminating the compensation error caused by the temperature drift characteristics of the channel, and further improving the accuracy of adaptive compensation.

[0173] Correspondingly, please refer to Figure 4 , the embodiment of the present application provides an inductive synchro temperature adaptive compensation device, which comprises:

[0174] The modeling module 100 is configured to determine the initial linear coefficient of the initial linear model, and obtain the current environment temperature and the current measurement angle of the inductive synchro, and determine the initial iteration parameter of the initial linear model according to the current environment temperature and the current measurement angle. For details, please refer to steps S1 and S3.

[0175] The iteration module 200 is configured to update the initial linear coefficient according to the initial iteration parameter to obtain the target linear coefficient, wherein data format conversion is performed in each iteration update process, and the next iteration is performed according to the conversion result. For details, please refer to step S5.

[0176] The compensation module 300 is configured to update the initial linear model based on the target linear coefficient to obtain an updated target linear model, and determine a measurement angle compensation value of the induction synchro according to the target linear model. Details are described in step S7.

[0177] Further functions of the above modules and units are the same as those of the corresponding embodiments, and will not be described here.

[0178] The induction synchro temperature adaptive compensation device in the embodiment is in the form of functional units. The units refer to ASIC (Application Specific Integrated Circuit) circuits, processors and memories executing one or more software or fixed programs, and / or other devices that can provide the above functions.

[0179] Please refer to Figure 5 , Figure 5 is a structural schematic diagram of a computer device provided by the embodiment of the present application, as Figure 5 shown, the computer device includes one or more processors 10, a memory 20, and an interface for connecting various components, including a high-speed interface and a low-speed interface. Various components communicate and connect with each other by using different buses, and can be installed on a common motherboard or in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in the memory or memory to display GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, each providing part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 In the embodiment, the processor 10 is taken as an example.

[0180] The processor 10 can be a central processor, a network processor, or a combination thereof. The processor 10 can further include a hardware chip. The hardware chip can be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device can be a complex programmable logic device, a field programmable logic gate array, a generic array logic, or any combination thereof.

[0181] The memory 20 stores instructions executable by the at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments.

[0182] The memory 20 can include a program storage area and a data storage area. The program storage area can store an operating system, application programs required by at least one function, and the like. The data storage area can store data created according to the use of the computer device, and the like. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-transitory memory such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some alternative embodiments, the memory 20 can optionally include a memory disposed remotely from the processor 10, which can be connected to the computer device through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0183] The memory 20 can include a volatile memory such as a random access memory, and can also include a non-volatile memory such as a flash memory, a hard disk, or a solid state disk. The memory 20 can also include a combination of the above-mentioned kinds of memories.

[0184] The computer device also includes a communication interface 30 for communication of the computer device with other devices or communication networks.

[0185] The embodiments of the present application also provide a computer readable storage medium. The above-mentioned method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or implemented as computer code originally stored in a remote storage medium or non-transitory machine readable storage medium and downloaded to a local storage medium, so that the method described herein can be processed by such software on a storage medium using a general-purpose computer, a special-purpose processor, or programmable or special-purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid state disk, etc. Further, the storage medium can also include a combination of the above-mentioned kinds of memories. It can be understood that the computer, the processor, the microprocessor controller, or the programmable hardware includes a storage component that can store or receive software or computer code, which, when accessed and executed by the computer, the processor, or the hardware, implements the method shown in the above-mentioned embodiments.

[0186] The embodiments of the present application provide a computer program product, which includes computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to make the computer device execute the method of any of the embodiments of the present application.

[0187] Although the embodiments of the present application have been described with reference to the accompanying drawings, it is to be understood that various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes are intended to fall within the scope of the present application as defined by the appended claims.

[0188] For the sake of description, the above device is described in various units by functions for the sake of description. Of course, the functions of the units can be implemented in one or more software and / or hardware in implementing the present application.

[0189] Those skilled in the art will appreciate that embodiments of the present application can be supplied as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0190] The present application is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts 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, a special purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing apparatus, generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that implements the functions specified in the flowcharts and / or block diagrams.

[0191] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that implements the functions specified in the flowcharts and / or block diagrams.

[0192] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that implements the functions specified in the flowcharts and / or block diagrams.

[0193] It should also be noted that the terms "comprising," "including," or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a... " does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0194] Various embodiments of the present application are described in the specification, which are exemplary and illustrative, and should not be construed as limiting the present application. The present application can be modified and changed in various ways by those skilled in the art without departing from the spirit and scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0195] The above description is merely illustrative of the embodiments of the present application and is not intended to limit the present application. The present application can be variously changed and modified by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0196] Although the embodiments of the present application are described with reference to the accompanying drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes should be included in the scope of the claims of the present application.

Claims

1. A method for adaptive temperature compensation of an inductive synchro, characterized in that, The method includes: Determine the initial linear coefficients of the initial linear model; The current ambient temperature and current measurement angle of the inductive synchro are obtained, and the initial iteration parameters of the initial linear model are determined based on the current ambient temperature and the current measurement angle. The initial linear coefficients are iteratively updated according to the initial iteration parameters to obtain the target linear coefficients. In each iteration update process, data format conversion is performed so that the next iteration can be performed based on the conversion result. The initial linear model is updated based on the target linear coefficient to obtain the updated target linear model, and the measurement angle compensation value for the inductive synchronizer is determined based on the target linear model. The step of determining the initial iteration parameters of the initial linear model based on the current ambient temperature and the current measurement angle includes: The regression vector is determined based on the current ambient temperature and a preset constant term; The error observation value is determined based on the current measurement angle and the reference angle; Construct the covariance matrix based on preset initial values; The regression vector, the error observations, and the covariance matrix are used as the initial iteration parameters; The reference angle is pre-stored in a reference database.

2. The temperature adaptive compensation method for the inductive synchro according to claim 1, characterized in that, The method is applied to an FPGA system, and the fixed-point format of the conversion result matches the number of data buses in the FPGA system.

3. The temperature adaptive compensation method for the inductive synchro according to claim 1, characterized in that, The initial linear model characterizes the negative correlation between the error of the current measurement angle of the inductive synchro and the current ambient temperature, wherein the reciprocal of the current ambient temperature is the input variable of the initial linear model; After obtaining the current ambient temperature of the inductive synchronizer, the method further includes: The reciprocal of the current ambient temperature is approximated using Newton's iteration method, and the input variables of the initial linear model are characterized based on the approximate calculation results.

4. The temperature adaptive compensation method for the inductive synchro according to claim 1, characterized in that, The benchmark database is determined in the following manner: A preset reference temperature is determined, and each sampling point of the inductive synchro is measured at the reference temperature to obtain the reference angle corresponding to each sampling point; The reference temperature, the sampling point, and the corresponding reference angle are associated and stored in the reference database.

5. The temperature adaptive compensation method for the inductive synchro according to claim 1, characterized in that, The step of iteratively updating the initial linear coefficients according to the initial iteration parameters to obtain the target linear coefficients includes, in each iteration update process, performing data format conversion to perform the next iteration based on the conversion result, including: The original residual vector corresponding to the current iteration is determined based on the error observation, the regression vector, and the initial linear coefficients. The original residual vector is then converted to a fixed-point format based on the first preset fixed-point conversion rule to obtain the converted residual vector. The original gain vector corresponding to the current iteration is determined based on the regression vector and the covariance matrix, and the original gain vector is converted to a fixed-point format based on the second preset fixed-point conversion rule to obtain the converted gain vector. The initial linear coefficients are updated based on the transformed gain vector and the transformed residual vector using the recursive least squares method, and the initial iteration parameters are also updated to obtain the updated linear coefficients and updated iteration parameters. Based on the third preset point conversion rule, the updated linear coefficients and the updated iteration parameters are converted into a fixed-point format to obtain the converted updated linear coefficients and the converted updated iteration parameters for the next iteration. If the preset iteration stopping condition is met, the converted updated linear coefficients are used as the target linear coefficients.

6. The temperature adaptive compensation method for the inductive synchro according to claim 5, characterized in that, The method further includes storing the transformed updated linear coefficients and the transformed updated iteration parameters in a register during each iteration update process; Specifically, key data are pre-selected from the post-transformation update linear coefficients and post-transformation update iteration parameters, and each key data corresponds to multiple registers with redundant design for backup.

7. The temperature adaptive compensation method for the inductive synchro according to claim 1, characterized in that, The benchmark database also pre-stores the average angle of the benchmark angle; Determining the target compensation value for the measured angle of the inductive synchronizer based on the target linear model includes: Determine the initial compensation value corresponding to the current ambient temperature based on the target linear model; The target compensation value is determined based on the difference between the initial compensation value and the mean angle.

8. A temperature adaptive compensation device for an inductive synchro, characterized in that, The device includes: The modeling module is used to determine the initial linear coefficients of the initial linear model, and to obtain the current ambient temperature and current measurement angle of the inductive synchro, and to determine the initial iteration parameters of the initial linear model based on the current ambient temperature and the current measurement angle. An iterative module is used to iteratively update the initial linear coefficients according to the initial iterative parameters to obtain the target linear coefficients, wherein data format conversion is performed during each iterative update process so as to perform the next iteration based on the conversion result; The compensation module is used to update the initial linear model based on the target linear coefficient to obtain the updated target linear model, and to determine the measurement angle compensation value for the inductive synchronizer based on the target linear model. The modeling module is also used for: The regression vector is determined based on the current ambient temperature and a preset constant term; The error observation value is determined based on the current measurement angle and the reference angle; Construct the covariance matrix based on preset initial values; The regression vector, the error observations, and the covariance matrix are used as the initial iteration parameters.

9. A computer device, characterized in that, include: The system includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the adaptive temperature compensation method for the inductive synchronizer as described in any one of claims 1 to 7.

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