CN110389529B - MEMS gyroscope parameter identification driving control method based on parallel estimation - Google Patents

MEMS gyroscope parameter identification driving control method based on parallel estimation Download PDF

Info

Publication number
CN110389529B
CN110389529B CN201910648374.7A CN201910648374A CN110389529B CN 110389529 B CN110389529 B CN 110389529B CN 201910648374 A CN201910648374 A CN 201910648374A CN 110389529 B CN110389529 B CN 110389529B
Authority
CN
China
Prior art keywords
dimensionless
substances
mems
content
parameter
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201910648374.7A
Other languages
Chinese (zh)
Other versions
CN110389529A (en
Inventor
许斌
张睿
魏琦
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Northwestern Polytechnical University
Original Assignee
Northwestern Polytechnical University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Northwestern Polytechnical University filed Critical Northwestern Polytechnical University
Priority to CN201910648374.7A priority Critical patent/CN110389529B/en
Publication of CN110389529A publication Critical patent/CN110389529A/en
Application granted granted Critical
Publication of CN110389529B publication Critical patent/CN110389529B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor

Abstract

The invention relates to a parallel estimation-based MEMS gyroscope parameter identification drive control method, and belongs to the field of intelligent instruments. The method converts a gyroscope kinetic model into a dimensionless kinetic linear parameterized model; designing a dynamics parallel estimation model, constructing a system prediction error, designing a parameter updating law by combining a tracking error, and improving the parameter identification precision; and designing a controller by combining a parameter updating law, and simultaneously realizing gyro driving control and kinetic parameter identification. The MEMS gyroscope parameter identification drive control method based on parallel estimation can solve the problem that the parameters are difficult to identify on line, meanwhile, gyroscope drive control and high-precision parameter identification are realized, and the performance of the MEMS gyroscope is further improved.

Description

MEMS gyroscope parameter identification driving control method based on parallel estimation
Technical Field
The invention relates to a drive control method of an MEMS gyroscope, in particular to a MEMS gyroscope parameter identification drive control method based on parallel estimation, and belongs to the field of intelligent instruments.
Background
The accurate dynamic model is an important condition for hardware design, control system design and system simulation of the MEMS gyroscope, and the parameter identification of the dynamic model is a key technology. In the text of Nonlinear Estimator Design for MEMS gyro with Time-varying and regulated Rate (Kral Ladispav and Straka Ondrej, International Federation of Automatic Control, 2017), parameters and states in a dynamic model are all brought into a state vector of a Kalman filter, the displacement of a detection mass block of the MEMS gyro is taken as measurement, and an unscented Kalman filter is adopted for parameter estimation. However, this method requires a large amount of prior information and cannot realize online parameter identification.
Disclosure of Invention
Technical problem to be solved
In order to solve the problem that the prior art is difficult to realize parameter online identification, the invention provides a MEMS gyroscope parameter identification driving control method based on parallel estimation. On one hand, the method designs a dynamics parallel estimation model, constructs a system prediction error, designs a parameter updating law by combining a tracking error, and improves the parameter identification precision; on the other hand, dynamics is converted into a linear parameterized model, a controller is designed by combining a parameter updating law, and gyro driving control and dynamics parameter identification are realized simultaneously.
Technical scheme
A MEMS gyroscope parameter identification drive control method based on parallel estimation is characterized by comprising the following steps:
step 1: the MEMS gyroscopic dynamics model considering the presence of quadrature errors is:
Figure BDA0002134333400000021
wherein m is the mass of the proof mass; omegazIn order to input the angular velocity for the gyro,
Figure BDA0002134333400000022
and x*Respectively the acceleration, the speed and the displacement of the MEMS gyroscope detection mass block along the driving shaft,
Figure BDA0002134333400000023
and y*Acceleration, velocity and displacement along the detection axis,
Figure BDA0002134333400000024
and
Figure BDA0002134333400000025
as an electrostatic driving force, cxxAnd cyyAs damping coefficient, kxxAnd kyyIn order to be a stiffness factor, the stiffness factor,
Figure BDA00021343334000000214
and
Figure BDA00021343334000000215
is a nonlinear coefficient, cxyAnd cyxTo damp the coupling coefficient, kxyAnd kyxIs the stiffness coupling coefficient; the parameters are selected according to the parameters of the vibrating type silicon micro-mechanical gyroscope；
Taking the dimensionless time t as omegaot*Non-dimensionalized displacement x ═ x*/q0，y＝y*/q0Wherein ω is0As reference frequency, q0For reference length, the MEMS gyroscopic dynamics model is dimensionless and divided by the equation on both sides simultaneously
Figure BDA0002134333400000026
To obtain
Figure BDA0002134333400000027
Wherein the content of the first and second substances,
Figure BDA0002134333400000028
and x is the dimensionless acceleration, the dimensionless speed and the dimensionless displacement of the MEMS gyroscope proof mass along the driving shaft respectively,
Figure BDA0002134333400000029
and y is the dimensionless acceleration, the dimensionless speed and the dimensionless displacement along the detection axis, respectively;
redefining
Figure BDA00021343334000000210
Figure BDA00021343334000000211
Figure BDA00021343334000000212
Figure BDA00021343334000000213
The formula (2) can be rewritten as
Figure BDA0002134333400000031
Definition of theta1＝[x,y]T，
Figure BDA0002134333400000032
Then formula (3) can be written as
Figure BDA0002134333400000033
Wherein U is [ U ]1,u2]T，F(Φ)＝[f1,f2]T，
Figure BDA0002134333400000034
Definition of
Figure BDA0002134333400000035
Carrying out linear parameterization on F (phi) to obtain
F(Φ)＝WΦ (5)
Step 2: the reference trajectory given for MEMS gyroscopic dynamics (1) is
Figure BDA0002134333400000036
Wherein the content of the first and second substances,
Figure BDA0002134333400000037
and
Figure BDA0002134333400000038
respectively reference vibration displacement signals of the proof mass along the drive axis and the detection axis,
Figure BDA0002134333400000039
and
Figure BDA00021343334000000310
reference amplitudes, ω, of drive and sense shaft vibrations, respectively1And ω2Respectively the reference angular frequencies of the drive shaft and the detection shaft vibrations,
Figure BDA00021343334000000311
and
Figure BDA00021343334000000312
the phases of the drive shaft and the detection shaft vibration respectively;
the reference trajectory of the dimensionless kinetic equation (4) is
Figure BDA00021343334000000313
Wherein the content of the first and second substances,
Figure BDA00021343334000000314
Figure BDA00021343334000000315
and the parameters to be designed
Figure BDA00021343334000000316
Defining a tracking error as
Figure BDA00021343334000000317
The controller is designed as
U＝Un+Upd-Uad (9)
Figure BDA0002134333400000041
Upd＝K1e1+K2e2 (11)
Figure BDA0002134333400000042
Wherein the content of the first and second substances,
Figure BDA0002134333400000043
is an estimate of W, the parameter to be designed
Figure BDA0002134333400000044
And
Figure BDA0002134333400000045
meets the Hurwitz condition;
and step 3: defining a model prediction error of
Figure BDA0002134333400000046
Wherein the content of the first and second substances,
Figure BDA0002134333400000047
is theta2Is obtained by the following parallel estimation model
Figure BDA0002134333400000048
Wherein the content of the first and second substances,
Figure BDA0002134333400000049
is composed of
Figure BDA00021343334000000410
Derivative of (2), parameter to be designed
Figure BDA00021343334000000411
Meets the Hurwitz condition;
giving an update law of kinetic parameters as
Figure BDA00021343334000000412
Wherein the content of the first and second substances,
Figure BDA00021343334000000413
and
Figure BDA00021343334000000414
a matrix is to be designed;
and 4, step 4: and designing a controller formula (9) to drive the dimensionless dynamics (4) based on a parameter adaptive law formula (15), and returning to the MEMS gyro dynamics model (1) through dimension conversion to realize gyro drive control and dynamics parameter identification.
Advantageous effects
Compared with the prior art, the MEMS gyroscope parameter identification drive control method based on parallel estimation has the beneficial effects that:
(1) aiming at the problem of low identification precision of kinetic parameters, a kinetic parallel estimation model is designed, a system prediction error is constructed, a kinetic parameter updating law is designed by combining a tracking error, and the parameter identification precision is improved.
(2) Aiming at the problem that kinetic parameters are difficult to identify on line, dynamics is rewritten into a linear parameterization form, a controller is designed by combining a parameter updating law, and meanwhile, gyro driving control and accurate kinetic identification are achieved.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention
Detailed Description
The invention will now be further described with reference to the following examples and drawings:
the invention discloses a MEMS gyroscope parameter identification drive control method based on parallel estimation, which comprises the following specific steps in combination with figure 1:
(a) the MEMS gyroscopic dynamics model considering the presence of quadrature errors is:
Figure BDA0002134333400000051
where m is the mass of the proof mass, ΩzIn order to input the angular velocity for the gyro,
Figure BDA0002134333400000052
and x*Respectively the acceleration, the speed and the displacement of the MEMS gyroscope detection mass block along the driving shaft,
Figure BDA0002134333400000053
and y*Acceleration, velocity and displacement along the detection axis,
Figure BDA0002134333400000054
and
Figure BDA0002134333400000055
as an electrostatic driving force, cxxAnd cyyAs damping coefficient, kxxAnd kyyIn order to be a stiffness factor, the stiffness factor,
Figure BDA0002134333400000056
and
Figure BDA0002134333400000057
is a nonlinear coefficient, cxyAnd cyxTo damp the coupling coefficient, kxyAnd kyxIs the stiffness coupling coefficient. According to a certain type of vibrating silicon micromechanical gyroscope, selecting each parameter of the gyroscope as m ═ 5.7 × 10-9kg，q0＝10-5m，ω0＝1kHz，Ωz＝5.0rad/s，kxx＝80.98N/m，kyy＝71.62N/m，kxy＝0.05N/m，kyx＝0.05N/m，
Figure BDA00021343334000000511
cxx＝4.29×10-7Ns/m，cyy＝4.29×10-8Ns/m，cxy＝4.29×10-8Ns/m，cyx＝4.29×10-8Ns/m。
Taking the dimensionless time t as omegaot*Non-dimensionalized displacement x ═ x*/q0，y＝y*/q0Wherein ω is0As reference frequency, q0Carrying out dimensionless processing on the MEMS gyro dynamic model to obtain a reference length
Figure BDA0002134333400000058
Wherein the content of the first and second substances,
Figure BDA0002134333400000059
and x is the dimensionless acceleration, the dimensionless speed and the dimensionless displacement of the MEMS gyroscope proof mass along the driving shaft respectively,
Figure BDA00021343334000000510
and y is the dimensionless acceleration, the dimensionless velocity, and the dimensionless displacement along the detection axis, respectively.
On both sides of formula (2) simultaneously by
Figure BDA0002134333400000061
Simplify it into
Figure BDA0002134333400000062
Redefining the kinetic parameters to
Figure BDA0002134333400000063
Figure BDA0002134333400000064
The formula (3) can be represented as
Figure BDA0002134333400000065
Definition of
Figure BDA0002134333400000066
Figure BDA0002134333400000067
The formula (4) can be rewritten as
Figure BDA0002134333400000068
Definition of theta1＝[x,y]T，
Figure BDA0002134333400000069
Then formula (5) can be written as
Figure BDA00021343334000000610
Wherein U is [ U ]1,u2]T，F(Φ)＝[f1,f2]T，
Figure BDA00021343334000000611
Definition of
Figure BDA00021343334000000612
Carrying out linear parameterization on F (phi) to obtain
F(Φ)＝WΦ (7)
(b) The reference trajectory given for MEMS gyroscopic dynamics (1) is
Figure BDA0002134333400000071
Wherein the content of the first and second substances,
Figure BDA0002134333400000072
and
Figure BDA0002134333400000073
reference vibration displacement signals of the proof mass along the drive axis and the proof axis, respectively.
The reference trajectory of the dimensionless kinetic equation (6) is
Figure BDA0002134333400000074
Wherein x isd＝6.2sin(4.71t+π/3)，yd＝5sin(5.11t-π/6)，
Figure BDA0002134333400000075
Figure BDA0002134333400000076
Defining a tracking error as
Figure BDA0002134333400000077
The controller is designed as
U＝Un+Upd-Uad (11)
Figure BDA0002134333400000078
Upd＝K1e1+K2e2 (13)
Figure BDA0002134333400000079
Wherein the content of the first and second substances,
Figure BDA00021343334000000710
is an estimate of the value of W,
Figure BDA00021343334000000711
(c) defining a model prediction error of
Figure BDA00021343334000000712
Wherein the content of the first and second substances,
Figure BDA00021343334000000713
is theta2Is obtained by the following parallel estimation model
Figure BDA00021343334000000714
Wherein the content of the first and second substances,
Figure BDA00021343334000000715
is composed of
Figure BDA00021343334000000716
The derivative of (a) of (b),
Figure BDA00021343334000000717
giving an update law of kinetic parameters as
Figure BDA0002134333400000081
Wherein the content of the first and second substances,
Figure BDA0002134333400000082
(d) the controller type (11) is designed to drive the dimensionless dynamics (6) based on the parameter adaptive law type (17), and returns to the MEMS gyro dynamics model (1) through dimension conversion, so that gyro drive control and dynamics parameter identification are realized.

Claims (1)

1. A MEMS gyroscope parameter identification drive control method based on parallel estimation is characterized by comprising the following steps:
step 1: the MEMS gyroscopic dynamics model considering the presence of quadrature errors is:
Figure FDA0002134333390000011
wherein m is the mass of the proof mass; omegazIn order to input the angular velocity for the gyro,
Figure FDA0002134333390000012
and x*Respectively the acceleration, the speed and the displacement of the MEMS gyroscope detection mass block along the driving shaft,
Figure FDA0002134333390000013
and y*Acceleration, velocity and displacement along the detection axis,
Figure FDA0002134333390000014
and
Figure FDA0002134333390000015
as an electrostatic driving force, cxxAnd cyyAs damping coefficient, kxxAnd kyyIn order to be a stiffness factor, the stiffness factor,
Figure FDA0002134333390000016
and
Figure FDA0002134333390000017
is a nonlinear coefficient, cxyAnd cyxTo damp the coupling coefficient, kxyAnd kyxIs the stiffness coupling coefficient; the parameters are selected according to the parameters of the vibrating type silicon micro-mechanical gyroscope;
taking the dimensionless time t as omegaot*Non-dimensionalized displacement x ═ x*/q0，y＝y*/q0Wherein ω is0Is prepared from radix GinsengFrequency of examination, q0For reference length, the MEMS gyroscopic dynamics model is dimensionless and divided by the equation on both sides simultaneously
Figure FDA0002134333390000018
To obtain
Figure FDA0002134333390000019
Wherein the content of the first and second substances,
Figure FDA00021343333900000110
and x is the dimensionless acceleration, the dimensionless speed and the dimensionless displacement of the MEMS gyroscope proof mass along the driving shaft respectively,
Figure FDA00021343333900000111
and y is the dimensionless acceleration, the dimensionless speed and the dimensionless displacement along the detection axis, respectively;
redefining
Figure FDA00021343333900000112
Figure FDA00021343333900000113
Figure FDA00021343333900000114
Figure FDA0002134333390000021
The formula (2) can be rewritten as
Figure FDA0002134333390000022
Definition of theta1＝[x,y]T，
Figure FDA0002134333390000023
Then formula (3) can be written as
Figure FDA0002134333390000024
Wherein U is [ U ]1,u2]T，F(Φ)＝[f1,f2]T，
Figure FDA0002134333390000025
Definition of
Figure FDA0002134333390000026
Carrying out linear parameterization on F (phi) to obtain
F(Φ)＝WΦ (5)
Step 2: the reference trajectory given for MEMS gyroscopic dynamics (1) is
Figure FDA0002134333390000027
Wherein the content of the first and second substances,
Figure FDA0002134333390000028
and
Figure FDA0002134333390000029
respectively reference vibration displacement signals of the proof mass along the drive axis and the detection axis,
Figure FDA00021343333900000210
and
Figure FDA00021343333900000211
reference amplitudes, ω, of drive and sense shaft vibrations, respectively1And ω2Respectively the reference angular frequencies of the drive shaft and the detection shaft vibrations,
Figure FDA00021343333900000212
and
Figure FDA00021343333900000213
the phases of the drive shaft and the detection shaft vibration respectively;
the reference trajectory of the dimensionless kinetic equation (4) is
θ1d＝[xd,yd]T，
Figure FDA00021343333900000214
Wherein the content of the first and second substances,
Figure FDA00021343333900000215
Figure FDA00021343333900000216
and the parameters to be designed
Figure FDA00021343333900000217
Defining a tracking error as
e1＝θ1d-θ1，e2＝θ2d-θ2，
Figure FDA00021343333900000218
The controller is designed as
U＝Un+Upd-Uad (9)
Figure FDA0002134333390000031
Upd＝K1e1+K2e2 (11)
Figure FDA0002134333390000032
Wherein the content of the first and second substances,
Figure FDA0002134333390000033
is an estimate of W, the parameter to be designed
Figure FDA0002134333390000034
And
Figure FDA0002134333390000035
meets the Hurwitz condition;
and step 3: defining a model prediction error of
Figure FDA0002134333390000036
Wherein the content of the first and second substances,
Figure FDA0002134333390000037
is theta2Is obtained by the following parallel estimation model
Figure FDA0002134333390000038
Wherein the content of the first and second substances,
Figure FDA0002134333390000039
is composed of
Figure FDA00021343333900000310
Derivative of (2), parameter to be designed
Figure FDA00021343333900000311
Meets the Hurwitz condition;
giving an update law of kinetic parameters as
Figure FDA00021343333900000312
Wherein the content of the first and second substances,
Figure FDA00021343333900000313
and
Figure FDA00021343333900000314
a matrix is to be designed;
and 4, step 4: and designing a controller formula (9) to drive the dimensionless dynamics (4) based on a parameter adaptive law formula (15), and returning to the MEMS gyro dynamics model (1) through dimension conversion to realize gyro drive control and dynamics parameter identification.
CN201910648374.7A 2019-07-18 2019-07-18 MEMS gyroscope parameter identification driving control method based on parallel estimation Active CN110389529B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910648374.7A CN110389529B (en) 2019-07-18 2019-07-18 MEMS gyroscope parameter identification driving control method based on parallel estimation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910648374.7A CN110389529B (en) 2019-07-18 2019-07-18 MEMS gyroscope parameter identification driving control method based on parallel estimation

Publications (2)

Publication Number Publication Date
CN110389529A CN110389529A (en) 2019-10-29
CN110389529B true CN110389529B (en) 2022-04-01

Family

ID=68285165

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910648374.7A Active CN110389529B (en) 2019-07-18 2019-07-18 MEMS gyroscope parameter identification driving control method based on parallel estimation

Country Status (1)

Country Link
CN (1) CN110389529B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104614993A (en) * 2015-01-15 2015-05-13 河海大学常州校区 Adaptive sliding mode preset performance control method for micro-gyroscope
CN107608216A (en) * 2017-11-05 2018-01-19 西北工业大学 MEMS gyroscope Hybrid Learning control method based on parallel estimation model
CN107831655A (en) * 2017-10-23 2018-03-23 河海大学常州校区 The adaptive inverting modified fuzzy sliding mode controlling method of fractional order of gyroscope
CN107870566A (en) * 2017-11-05 2018-04-03 西北工业大学 MEMS gyroscope quick start method based on parallel estimation Hybrid Learning
CN108241299A (en) * 2018-01-19 2018-07-03 南通大学 The microthrust test adaptive sliding-mode observer method limited with error
CN109062048A (en) * 2018-08-20 2018-12-21 西北工业大学 The nonsingular sliding-mode control of MEMS gyroscope default capabilities based on Hybrid Learning

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104614993A (en) * 2015-01-15 2015-05-13 河海大学常州校区 Adaptive sliding mode preset performance control method for micro-gyroscope
CN107831655A (en) * 2017-10-23 2018-03-23 河海大学常州校区 The adaptive inverting modified fuzzy sliding mode controlling method of fractional order of gyroscope
CN107608216A (en) * 2017-11-05 2018-01-19 西北工业大学 MEMS gyroscope Hybrid Learning control method based on parallel estimation model
CN107870566A (en) * 2017-11-05 2018-04-03 西北工业大学 MEMS gyroscope quick start method based on parallel estimation Hybrid Learning
CN108241299A (en) * 2018-01-19 2018-07-03 南通大学 The microthrust test adaptive sliding-mode observer method limited with error
CN109062048A (en) * 2018-08-20 2018-12-21 西北工业大学 The nonsingular sliding-mode control of MEMS gyroscope default capabilities based on Hybrid Learning

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Sliding mode control of MEMS gyroscopes using composite learning;RUI ZHANG etc.;《Neurocomputing》;20180131;第275卷;第2555-2564页 *
基于智能控制技术的微惯性器件误差抑制方法研究;张睿;《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》;20180915(第9期);第I140-78页 *

Also Published As

Publication number Publication date
CN110389529A (en) 2019-10-29

Similar Documents

Publication Publication Date Title
CN110389528B (en) Data-driven MEMS gyroscope driving control method based on disturbance observation
CN108897226B (en) The nonsingular sliding-mode control of MEMS gyroscope default capabilities based on interference observer
Esmaeili et al. Dynamic modeling and performance evaluation of a vibrating beam microgyroscope under general support motion
Fei Robust adaptive vibration tracking control for a micro-electro-mechanical systems vibratory gyroscope with bound estimation
CN104049534B (en) Self-adaption iterative learning control method for micro-gyroscope
CN110703610B (en) Nonsingular terminal sliding mode control method for recursive fuzzy neural network of micro gyroscope
CN108710296B (en) Fractional order self-adaptive fast terminal sliding mode control method of micro gyroscope
CN109240083B (en) Self-adaptive fuzzy super-distortion sliding mode control method of micro gyroscope system
Shaw et al. A review of nonlinear dynamics of mechanical systems in year 2008
Gill et al. MEMS multi-vibrating ring gyroscope for space applications
CN110389527B (en) Heterogeneous estimation-based MEMS gyroscope sliding mode control method
CN110389530B (en) MEMS gyroscope parameter identification drive control method based on data screening
Clark et al. Modified nodal analysis for MEMS with multi-energy domains
CN103345154A (en) Indirect self-adaptive fuzzy sliding-mode control method of micro gyroscope system
CN110389529B (en) MEMS gyroscope parameter identification driving control method based on parallel estimation
CN110426952B (en) High-precision drive control method for interval data learning MEMS gyroscope considering external interference
CN110456639B (en) MEMS gyroscope self-adaptive driving control method based on historical data parameter identification
CN109062048A (en) The nonsingular sliding-mode control of MEMS gyroscope default capabilities based on Hybrid Learning
CN110471293B (en) Z-axis gyroscope sliding mode control method for estimating time-varying angular velocity
CN110456638B (en) MEMS gyroscope parameter identification adaptive drive control method based on interval data excitation
CN110456640B (en) MEMS gyroscope parameter identification neural network control method based on nonsingular terminal sliding mode design
Lee et al. Real-time rls-based joint model identification and state observer design for robot manipulators: Experimental studies
CN110579966B (en) Z-axis gyroscope control method based on neural network identification parameters
CN110389526B (en) MEMS gyroscope self-adaptive sliding mode control method based on Super-Twisting algorithm
Yin et al. Structure-decoupled dual-mass MEMS gyroscope with self-adaptive closed-loop detection

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant