Resonant gyroscope error identification method based on variable direct current bias voltage

Abstract

A resonant gyroscope error identification method based on variable direct current bias voltage relates to a resonant gyroscope detection technology, and comprises the following steps: setting n direct current bias voltages UD, and measuring a frequency cracking f and a main shaft azimuth angle # imgabs0 #; establishing a frequency splitting function and a main shaft azimuth angle function based on the direct current bias voltage, a quality fourth harmonic coefficient # imgabs 1 #, a quality fourth harmonic azimuth angle # imgabs 2 #, a gap fourth harmonic coefficient # imgabs 3 # and a gap fourth harmonic azimuth angle # imgabs 4 #; randomly valuing the error parameter group, and substituting the error parameter group into the simulation function to obtain a frequency splitting decomposition and main shaft azimuth simulation array; calculating an evaluation optimization function value MF; judging whether the evaluation optimization function value MF is greater than a preset threshold, and if yes, updating the random error parameter group according to a swarm intelligence optimization algorithm; otherwise, outputting an error parameter. According to the method, the fourth harmonic error of harmonic oscillator quality non-uniformity and the fourth harmonic error of lip edge gap non-uniformity can be identified.

Description

Technical Field

[0001] The invention relates to the technical field of resonant gyroscope detection, and in particular to a resonant gyroscope error identification method based on variable direct current bias voltage. Background Art

[0002] The resonant gyroscope is an inertial-grade solid-state wave gyroscope. Its working principle is to be sensitive to the external angle or angular velocity based on the precession effect of the standing wave of the resonator's vibration. It has the unique advantages of high precision, small size, strong impact resistance, long life and high reliability. It has broad application prospects in military and civilian fields such as precision guidance, aviation, aerospace, navigation, consumer electronics, etc., and is recognized by the inertial technology community at home and abroad as a disruptive inertial device in the 21st century.

[0003] At present, Northrop Grumman of the United States and Safran of France have mastered most of the core technologies of resonant gyroscopes and have achieved mass production applications. Among them, the assembly difficulty and production cost of the resonant gyroscope with a two-piece planar electrode structure developed by Safran of France have been greatly reduced, and the annual output can reach 25,000 units, with an accuracy ranging from 0.1° / h to 0.0001° / h, which has very strong market competitiveness. Although experimental prototypes have been developed in the research of resonant gyroscopes in China, there is still a long way to go before engineering prototypes and model applications. In addition, the accuracy of resonant gyroscopes publicly reported in China is still 2-3 orders of magnitude away from the highest international level.

[0004] The development of a resonant gyroscope involves multiple processes / procedures, including the processing, chemical etching, coating, leveling and precision assembly of the resonator, the processing and etching of flat electrodes, circuit design, manufacturing and testing, and vacuum packaging of the meter head. Among these processes, the development process of the resonant gyroscope is usually evaluated by testing indicators such as the quality factor, quality factor non-uniformity, and frequency cracking of the resonator.

[0005] Taking frequency splitting as an example, existing studies have shown that in addition to the fourth harmonic error of the non-uniformity of the mass of the resonator, the fourth harmonic error of the non-uniformity of the lip gap during the assembly of the resonator and the flat electrode will also affect the frequency splitting of the gyroscope head, and the frequency splitting introduced by the latter is also related to the DC bias voltage of the head. Among them, the fourth harmonic error of the non-uniformity of the mass of the resonator specifically includes the fourth harmonic coefficient of the mass of the resonator and mass fourth harmonic azimuth The fourth harmonic error of the uneven gap along the lip of the resonator mainly includes the fourth harmonic coefficient of the gap and gap fourth harmonic azimuth From the above, it can be seen that both the fourth harmonic error of mass non-uniformity and the fourth harmonic error of lip gap non-uniformity during assembly will affect the frequency splitting of the resonant gyroscope head. Therefore, it is necessary to identify these two types of errors so that they can be eliminated or compensated in the assembly process or control algorithm, laying the foundation for improving the assembly accuracy and assembly performance of the resonant gyroscope. Summary of the invention

[0006] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a resonant gyroscope error identification method based on variable DC bias voltage, which can simultaneously realize the identification of the fourth harmonic error of the resonator mass non-uniformity and the fourth harmonic error of the lip gap non-uniformity.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions: A resonant gyroscope error identification method based on variable DC bias voltage comprises the following steps: S1: Data measurement, based on the resonant gyro component to be tested, setting n More than one different DC bias voltage U D ,and n is an integer greater than 2, and the frequency cracking △ under the corresponding DC bias voltage conditions is measured respectively f and the main axis azimuth , and obtain a set of DC bias voltage values ​​{ U D1 , U D2 , … , U Dn} and the frequency cracking measured array corresponding to each DC bias voltage value { , ,···, } and the measured array of the main axis azimuth { , ,···, }; S2: Establishing a resonant gyro error identification simulation calculation function, based on a set of DC bias voltage values ​​obtained in step S1 { U D1 , U D2 , … , U Dn} and the fourth harmonic coefficient of mass , mass fourth harmonic azimuth , gap fourth harmonic coefficient and gap fourth harmonic azimuth Four variables are established to obtain the corresponding DC bias voltage value n Frequency splitting function { , ,···, }and n Frequency principal axis azimuth function { , ,···, }; S3: Simulation calculation, the fourth harmonic coefficient of mass , mass fourth harmonic azimuth , gap fourth harmonic coefficient and gap fourth harmonic azimuth Randomly select a value within the range of values ​​to obtain a random error parameter group, substitute the random parameter group into the simulation calculation function established in step S2, and calculate the values ​​of each DC bias voltage { U D1 , U D2 , … , U Dn}The corresponding frequency cracking simulation array { , ,···, } and the spindle azimuth simulation array { , ,···, }; S4: Calculate the value of the evaluation optimization function MF , based on formula (1), the evaluation optimization function value is calculated MF ; In formula (1), and are weight factors respectively; S5: judge, judge the value of the evaluation optimization function MF Is it greater than the preset threshold? If the evaluation optimization function value MF If the error is greater than a preset threshold, step S3 is repeated, and the random error parameter group is updated according to the swarm intelligence optimization algorithm; otherwise, the current error parameter is output as the error parameter of the resonant gyro assembly to be tested.

[0008] In step S2, the simulation calculation function is as shown in formula (2): In formula (2), in, ε is the permittivity of the medium between the lip edge of the resonator and the flat electrode, is the density of the oscillator, is the Young's modulus of the oscillator, is the thickness of the resonator shell, is the mid-surface radius of the resonator shell, It is the reciprocal of the gap between the lip of the resonator and the flat electrode.

[0009] The mass fourth harmonic coefficient , mass fourth harmonic azimuth , gap fourth harmonic coefficient and gap fourth harmonic azimuth The value ranges are: The value range is 0~0.001. The value range is 0~0.1. and The value range is ±180°.

[0010] The random error parameter group includes at least 20 columns of error parameters, each column of error parameters is composed of the fourth harmonic coefficient of mass , mass fourth harmonic azimuth , gap fourth harmonic coefficient and gap fourth harmonic azimuth In step S3, the error parameters in the random error parameter group are substituted into the simulation calculation function column by column.

[0011] The swarm intelligence optimization algorithm is a particle swarm optimization algorithm, a genetic algorithm, or an ant colony algorithm.

[0012] The preset threshold value ranges from 0 to 1.

[0013] The weight factor and The value range is 0.01~1000.

[0014] Compared with the prior art, the advantages of the present invention are: The resonant gyro error identification method based on variable DC bias voltage of the present invention is mainly to solve the two types of error identification technical problems of the fourth harmonic error of the resonator mass non-uniformity and the fourth harmonic error of the resonator lip edge assembly gap non-uniformity, and provide data basis for the correction and compensation of the resonant gyro error, which is of great significance to improving the assembly accuracy and assembly performance of the resonant gyro. The present invention can realize the identification of the fourth harmonic error of the resonator mass non-uniformity and the fourth harmonic error of the lip edge gap non-uniformity by establishing a resonant gyro error identification simulation calculation function and combining a swarm intelligence optimization algorithm; the present invention adopts a swarm intelligence optimization algorithm for error parameter identification, and has a higher degree of automation and intelligence. After obtaining the data of frequency decomposition and main axis azimuth under different DC bias voltages, the automatic identification of error parameters can be quickly realized, which is more efficient and more versatile. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0016] Picture 1 It is a flow chart of an embodiment of a resonant gyro error identification method based on a variable DC bias voltage according to the present invention.

[0017] Picture 2 It is a comparison chart of simulation results and measured data of frequency cracking in an embodiment of the resonant gyro error identification method based on variable DC bias voltage of the present invention.

[0018] Picture 3 It is a comparison chart of simulation results and measured data of the main axis azimuth angle in the embodiment of the resonant gyro error identification method based on variable DC bias voltage of the present invention. DETAILED DESCRIPTION

[0019] like Picture 1 As shown, an embodiment of a resonant gyroscope error identification method based on a variable DC bias voltage of the present invention comprises the following steps: S1: Data measurement, based on the resonant gyro component to be tested, setting n More than one different DC bias voltage U D ,and n is an integer greater than 2, and the frequency cracking △ under the corresponding DC bias voltage conditions is measured respectively f and the main axis azimuth , and obtain a set of DC bias voltage values ​​{ U D1 , U D2 , … ,U Dn} and the frequency cracking measured array corresponding to each DC bias voltage value { , ,···, } and the measured array of the main axis azimuth { , ,···, }; In this embodiment, n =7, the measured array data are shown in Table 1; Table 1: Measured array data based on different DC bias voltages S2: Establishing a resonant gyro error identification simulation calculation function, based on a set of DC bias voltage values ​​obtained in step S1 { U D1 , U D2 , … , U Dn} and the fourth harmonic coefficient of mass , mass fourth harmonic azimuth , gap fourth harmonic coefficient and gap fourth harmonic azimuth Four variables are established to obtain the corresponding DC bias voltage value n Frequency splitting function { , ,···, }and n Frequency principal axis azimuth function { , ,···, }; S3: Simulation calculation, the fourth harmonic coefficient of mass , mass fourth harmonic azimuth , gap fourth harmonic coefficient and gap fourth harmonic azimuth Randomly select a value within the range of values ​​to obtain a random error parameter group, substitute the random parameter group into the simulation calculation function established in step S2, and calculate a frequency cracking simulation array corresponding to each group of DC bias voltage values ​​{ , ,···, } and the spindle azimuth simulation array { , ,···, }; S4: Calculate the value of the evaluation optimization function MF, based on formula (1), the evaluation optimization function value is calculated MF ; In formula (1), and They are weight factor; weight factor and The value range of is 0.01~1000. In this embodiment, =10, =1.

[0020] S5: judge, judge the value of the evaluation optimization function MF Is it greater than a preset threshold? The preset threshold ranges from 0 to 1. In this embodiment, the preset threshold is 0.183. MF If the error is greater than a preset threshold, step S3 is repeated and the random error parameter group is updated according to the swarm intelligence optimization algorithm; otherwise, the current error parameter is output as the error parameter of the resonant gyro component to be tested.

[0021] In this embodiment, the error parameters of the resonant gyro assembly to be tested are output as shown in Table 2: Table 2: Output error parameters of the resonant gyro assembly under test Based on the error parameters and measured array data shown in Table 2, the simulation results of frequency decomposition and spindle azimuth angle and the measured data comparison diagram are plotted respectively, as shown in Picture 2 , Picture 3 As shown, it can be known that the error parameters shown in Table 2 have good matching with the measured data. The resonant gyro error identification method based on variable DC bias voltage of the present invention is mainly to solve the two types of error identification technical problems of the fourth harmonic error of the resonator mass non-uniformity of the resonator and the fourth harmonic error of the lip edge assembly gap non-uniformity of the resonator, and provide data basis for the correction and compensation of the resonant gyro error, which is of great significance to improving the assembly accuracy and assembly performance of the resonant gyro. The present invention can realize the identification of the fourth harmonic error of mass non-uniformity and the fourth harmonic error of lip edge gap non-uniformity by establishing a resonant gyro error identification simulation calculation function and combining it with a swarm intelligence optimization algorithm; the present invention adopts a swarm intelligence optimization algorithm for error parameter identification, and the degree of automation and intelligence is higher. After obtaining the data of frequency decomposition and main axis azimuth under different high pressures, the automatic identification of error parameters can be quickly realized, which is more efficient and more versatile.

[0022] In step S2 of this embodiment, the simulation calculation function is as shown in formula (2): In formula (2), in, ε is the permittivity of the medium between the lip edge of the resonator and the flat electrode, is the density of the oscillator, is the Young's modulus of the oscillator, is the thickness of the resonator shell, is the mid-surface radius of the resonator shell, It is the reciprocal of the gap between the lip of the resonator and the flat electrode.

[0023] In this embodiment, the fourth harmonic coefficient of mass , mass fourth harmonic azimuth , gap fourth harmonic coefficient and gap fourth harmonic azimuth The value ranges are: The value range is 0~0.001. The value range is 0~0.1. and The value range is ±180°. In this embodiment, the random error parameter group includes 100 columns of error parameters, each of which is composed of the fourth harmonic coefficient of mass. , mass fourth harmonic azimuth , gap fourth harmonic coefficient and gap fourth harmonic azimuth In step S3, the error parameters in the random error parameter group are substituted into the simulation calculation function column by column. In other embodiments, the random error parameter group includes at least 20 columns of error parameters.

[0024] In this embodiment, the swarm intelligence optimization algorithm used is a particle swarm optimization algorithm. In other embodiments, a genetic algorithm or an ant colony algorithm may also be used.

[0025] Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. Any technician familiar with the art can make many possible changes and modifications to the technical solution of the present invention by using the technical content disclosed above without departing from the scope of the technical solution of the present invention, or modify it into an equivalent embodiment of equivalent changes. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention should fall within the scope of protection of the technical solution of the present invention.

Claims

1. A resonant gyroscope error identification method based on variable DC bias voltage, characterized in that: The following steps are involved: S1: Data measurement, based on the resonant gyro component to be tested, setting n More than one different DC bias voltage U D ,and n is an integer greater than 2, and the frequency cracking △ under the corresponding DC bias voltage conditions is measured respectively f and the main axis azimuth , and obtain a set of DC bias voltage values ​​{ U D1 , U D2 , … , U Dn } and the frequency cracking measured array corresponding to each DC bias voltage value { , ,···, } and the measured array of the main axis azimuth { , ,···, }; S2: Establishing a resonant gyro error identification simulation calculation function, based on a set of DC bias voltage values ​​obtained in step S1 { U D1 , U D2 , … , U Dn } and the fourth harmonic coefficient of mass , mass fourth harmonic azimuth , gap fourth harmonic coefficient and gap fourth harmonic azimuth Four variables are established to obtain the corresponding DC bias voltage value n Frequency splitting function { , ,···, }and n Frequency principal axis azimuth function { , ,···, }; S3: Simulation calculation, the fourth harmonic coefficient of mass , mass fourth harmonic azimuth , gap fourth harmonic coefficient and gap fourth harmonic azimuth Randomly select a value within the range of values ​​to obtain a random error parameter group, substitute the random parameter group into the simulation calculation function established in step S2, and calculate the values ​​of each DC bias voltage { U D1 , U D2 , … , U Dn }The corresponding frequency cracking simulation array { , ,···, } and the spindle azimuth simulation array { , ,···, }; S4: Calculate the value of the evaluation optimization function MF , based on formula (1), the evaluation optimization function value is calculated MF ; In formula (1), and are weight factors respectively; S5: judge, judge the value of the evaluation optimization function MF Is it greater than the preset threshold? If the evaluation optimization function value MF If the error is greater than a preset threshold, step S3 is repeated, and the random error parameter group is updated according to the swarm intelligence optimization algorithm; otherwise, the current error parameter is output as the error parameter of the resonant gyro assembly to be tested.

2. The resonant gyro error identification method based on variable DC bias voltage according to claim 1, characterized in that: In step S2, the simulation calculation function is as shown in formula (2): In formula (2), in, ε is the permittivity of the medium between the lip edge of the resonator and the flat electrode, is the density of the oscillator, is the Young's modulus of the oscillator, is the thickness of the resonator shell, is the mid-surface radius of the resonator shell, It is the reciprocal of the gap between the lip of the resonator and the flat electrode.

3. The resonant gyro error identification method based on variable DC bias voltage according to claim 2, characterized in that: The mass fourth harmonic coefficient , mass fourth harmonic azimuth , gap fourth harmonic coefficient and gap fourth harmonic azimuth The value ranges are: The value range is 0~0.

001. The value range is 0~0.

1. and The value range is ±180°.

4. The resonant gyro error identification method based on variable DC bias voltage according to any one of claims 1 to 3, characterized in that: The random error parameter group includes at least 20 columns of error parameters, each column of error parameters is composed of the fourth harmonic coefficient of mass , mass fourth harmonic azimuth , gap fourth harmonic coefficient and gap fourth harmonic azimuth In step S3, the error parameters in the random error parameter group are substituted into the simulation calculation function column by column.

5. The resonant gyro error identification method based on variable DC bias voltage according to any one of claims 1 to 3, characterized in that: The swarm intelligence optimization algorithm is a particle swarm optimization algorithm, a genetic algorithm, or an ant colony algorithm.

6. The resonant gyro error identification method based on variable DC bias voltage according to any one of claims 1 to 3, characterized in that: The preset threshold value ranges from 0 to 1.

7. The resonant gyro error identification method based on variable DC bias voltage according to any one of claims 1 to 3, characterized in that: The weight factor and The value range is 0.01~1000.

Images