CN110389528A - Data-driven MEMS gyroscope drive control method based on disturbance observation - Google Patents

Data-driven MEMS gyroscope drive control method based on disturbance observation Download PDF

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CN110389528A
CN110389528A CN201910648370.9A CN201910648370A CN110389528A CN 110389528 A CN110389528 A CN 110389528A CN 201910648370 A CN201910648370 A CN 201910648370A CN 110389528 A CN110389528 A CN 110389528A
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drive control
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CN110389528B (en
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许斌
张睿
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Northwestern Polytechnical University
Shenzhen Institute of Northwestern Polytechnical University
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Northwest University of Technology
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    • 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

Abstract

The data-driven MEMS gyroscope drive control method based on disturbance observation that the present invention relates to a kind of, belongs to intelligent instrumentation field.Gyroscope kinetic model is converted nondimensional kinetic linearity parameterized model by this method；The suitable historical data of data screening method choice is provided, and combines current data and historical data design parameter adaptive law, realizes parameter identification；It is uncertain using the working environments variation bring system such as neural network estimation temperature, air pressure, magnetic field；It designs disturbance observer and estimates external vibration environment bring dynamical perturbation；Controller is designed, gyro drive control precision is promoted by closed loop feedback.The data-driven MEMS gyroscope drive control method based on disturbance observation that the present invention designs can realize gyroscope high-precision drive control, further improve MEMS gyroscope performance in the case where not knowing there are system and external disturbance.

Description

Data-driven MEMS gyroscope drive control method based on disturbance observation
Technical field
The present invention relates to a kind of drive control methods of MEMS gyroscope, more particularly to a kind of number based on disturbance observation According to driving MEMS gyroscope drive control method, belong to intelligent instrumentation field.
Background technique
In practical engineering applications, the working environments such as the temperature of MEMS gyroscope, air pressure, magnetic field variation causes dynamics to be joined Number changes, and vibration environment can bring external disturbance for dynamics, the two phenomenons lead to the control for lacking capacity of self-regulation Device processed is difficult to adapt to the environment of dynamic change.Currently used two kinds of resolving ideas are: (1) improving hardware design, increase isolation The influence of component shielding external environment；(2) controller design scheme is improved, the adaptive ability of controller is enhanced.
《Adaptive Global Sliding Mode Control for MEMS Gyroscope Using RBF Neural Network " (Yundi Chu and Juntao Fei, " Mathematical Problems in Engineering ", 2015) text is with above-mentioned second method thinking consistent, it proposes a kind of based on RBF neural MEMS gyroscope total-sliding-mode control method adjusts sliding formwork handoff gain using neural network, while giving kinetic model Parameter identification result.However this method pays close attention to sliding formwork and buffets problem, it is difficult to guarantee to system Dynamic Uncertain and outside Disturbance is effectively estimated, and then limits control precision.
Summary of the invention
Technical problems to be solved
To overcome the problems, such as that prior art drive control precision is limited, the present invention proposes a kind of data based on disturbance observation Drive MEMS gyroscope drive control method.This method provides the suitable historical data of data screening method choice, and combines and work as Preceding data and historical data design parameter adaptive law realize parameter identification；Temperature, gas are estimated using adaptive neural network It is uncertain that the working environments such as pressure, magnetic field change bring system；It is dynamic to design disturbance observer estimation external vibration environment bring Mechanics disturbance；Controller is designed, gyro drive control precision is promoted by closed loop feedback.
Technical solution
A kind of data-driven MEMS gyroscope drive control method based on disturbance observation, it is characterised in that steps are as follows:
Step 1: consideration is not known there are quadrature error, system and the MEMS gyro kinetic model of external disturbance are as follows:
Wherein, m is the quality for detecting mass block, ΩzFor gyro input angular velocity,And x*Respectively MEMS gyro Instrument detects mass block along the acceleration of drive shaft, speed and displacement,Acceleration, speed with y* respectively along detection axis Degree and displacement,WithFor electrostatic drive power, cxxAnd cyyFor damped coefficient, kxxAnd kyyFor stiffness coefficient,WithIt is non- Linear coefficient, cxyAnd cyxFor damping couple coefficient, kxyAnd kyxFor stiffness coupling coefficient,WithRespectively drive External disturbance on axis and detection axis；And Wherein WithIt is parametric nominal value, according to the oscillatory type of certain model Silicon micromechanical gyroscope is chosen, Δ kxx、Δkyy、Δcxx、Δcyy、Δkxy、Δkyx、ΔcxyWith Δ cyxIt is not The uncertain parameter known；
Take nondimensionalization time t=ωot*, nondimensionalization displacement x=x*/q0, y=y*/q0, wherein ω0For reference frequency, q0For reference length, nondimensionalization processing carried out to MEMS gyro kinetic model, and both members simultaneously divided by It obtains
Wherein,It is respectively nondimensional acceleration of the MEMS gyroscope detection mass block along drive shaft, dimensionless with x Speed and nondimensional displacement,It is respectively along the nondimensional acceleration of detection axis, nondimensional velocity and dimensionless position with y It moves, dx(t) and dy(t) it is respectively dimensionless external disturbance in drive shaft and detection axis；
It redefines
Formula (2) can be expressed as
Define θ1=[x, y]T,Then formula (3) can be written as
Wherein, U=[u1,u2]T, F (z)=[f1,f2]T, Δ F (z)=[Δ f1,Δf2]T,
Assuming thatIt is matrix of unknown parameters to be identified,It is continuously differentiable regression function vector, Linear parameterization is carried out to F (z), is obtained
F (z)=W*TΦ(z) (5)
Wherein, Φ (z)=z；
Constructing neural networkΔ F (z) is approached, is obtained
Wherein,It is the input vector of neural network,For the power of neural network Value matrix, M are neural network node number to be designed,For base vector, q-th of element definition is as follows Gaussian function, wherein q=1,2 ..., M；
Wherein, σqIt is Gaussian function standard deviation to be designed,It is that the Gaussian function waits for The center of design；
Step 2: the reference locus for providing MEMS gyro dynamics formula (1) is
Wherein,WithRespectively detection mass block along drive shaft and detection axis reference vibration displacement signal,WithThe respectively reference amplitude of drive shaft and detection shaft vibration, ω1And ω2The respectively reference angle of drive shaft and detection shaft vibration Frequency,WithThe respectively phase of drive shaft and detection shaft vibration；
Then the reference locus of dimensionless dynamics formula (4) is
Wherein, And parameter to be designed
Defining tracking error is
Then controller design is
U=Un+Upd-Uad (11)
Upd=K1e1+K2e2 (13)
Wherein, parameter to be designedWithMeet Hurwitz condition,It is W*Estimated value,For The estimated value of external disturbance D；
The adaptive law for providing parameter is
Wherein, first item is calculated using current time data on the right of equation, and Section 2 uses pHThe storage number of a moment point According to calculating, Φ (zi) it is Φ (z) in the value of i moment point, wherein i=1,2 ..., pH,F (zi) it is value of the F (z) in i moment point, and parameter to be designedMeet Hurwitz condition, B= [02×2,I2×2]T；
The more new law for providing neural network weight is
Wherein,For matrix to be designed；
Designing disturbance observer is
Wherein,For positive definite matrix to be designed,For intermediate variable；
Step 3: defining a matrix ZtStoring data Φ (z), the matrix line number are 6, and columns p changes with storage data quantity AndAssuming that p*It is the last one moment point of storing data,For p*The Φ (z) of moment point, ε are normal Number；The data screening process that parameter update law formula (15) is selected is as follows:
1. ifOr rank ([Zt,Φ(z)])>rank([Zt]), execute step 2., otherwise Give up data Φ (z)；
2. ifThen by pHThe Φ (z) at moment is stored in ZtMatrix, even pH=pH+ 1, Zt(:,pH)=Φ (z), It is no to then follow the steps 3.；
3. calculating current ZtThe minimum singular value of matrix, and it is denoted as Sold；Then, Φ (z) is deposited at the i moment respectively Enter ZtMatrix, wherein i=1,2 ..., pH, obtain one group of matrix Calculate different Zt Minimum singular value, and select the maximum value S in all minimum singular values；Continue to execute step 4.；
4. if S > Sold, by pHThe Φ (z) at moment is stored in ZtMatrix, i.e. Zt(:,pH)=Φ (z), otherwise gives up pHMoment Φ (z)；1. return step continues garbled data；
Step 4: using based on the data screening method described in step 3, parameter update law formula (15), neural network weight More new law formula (16) and the controller formula (11) of disturbance observer formula (17) design drive dimensionless dynamics formula (4), and pass through Dimension conversion returns to MEMS gyro kinetic simulation pattern (1), realizes gyro drive control.
Beneficial effect
A kind of data-driven MEMS gyroscope drive control method based on disturbance observation proposed by the present invention, with existing skill What art was compared has the beneficial effect that
(1) it is directed to the uncertain dynamics and external disturbance of MEMS gyro, separately designs adaptive neural network and disturbance Observer is estimated, and promotes gyro drive control precision by feedback compensation.
(2) problem unknown for kinetic parameter in Practical Project, design data screening technique select suitable history number According to constructing parameter update law jointly using current data and historical data, realize parameter identification.
Detailed description of the invention
Flow chart is embodied in Fig. 1 present invention
Fig. 2 data screening flow chart of the present invention
Specific embodiment
Now in conjunction with embodiment, attached drawing, the invention will be further described:
The data-driven MEMS gyroscope drive control method based on disturbance observation that the invention discloses a kind of, in conjunction with Fig. 1, Specific design procedure is as follows:
(a) consider there are quadrature error, system is uncertain and the MEMS gyro kinetic model of external disturbance are as follows:
Wherein, m is the quality for detecting mass block, ΩzFor gyro input angular velocity,And x*Respectively MEMS gyro Instrument detects mass block along the acceleration of drive shaft, speed and displacement,And y*Respectively along the acceleration of detection axis, speed Degree and displacement,WithFor electrostatic drive power, cxxAnd cyyFor damped coefficient, kxxAnd kyyFor stiffness coefficient,WithIt is non- Linear coefficient, cxyAnd cyxFor damping couple coefficient, kxyAnd kyxFor stiffness coupling coefficient,WithRespectively drive External disturbance on axis and detection axis.And Wherein WithIt is parametric nominal value, according to the oscillatory type silicon micromechanical gyro of certain model, choosing each parameter of gyro is M=5.7 × 10-9Kg, q0=10-5M, ω0=1kHz, Ωz=5.0rad/s, Δkxx、Δkyy、Δcxx、Δcyy、Δkxy、Δkyx、ΔcxyWith Δ cyxIt is unknown uncertain parameter,
Take the nondimensionalization timeNondimensionalization displacement x=x*/q0, y=y*/q0, wherein ω0For reference frequency, q0For reference length, nondimensionalization processing is carried out to MEMS gyro kinetic model, is obtained
Wherein,It is respectively nondimensional acceleration of the MEMS gyroscope detection mass block along drive shaft, dimensionless with x Speed and nondimensional displacement,It is respectively along the nondimensional acceleration of detection axis, nondimensional velocity and dimensionless position with y It moves, dx(t)=5.7 × 10-8Sin (2t) and dy(t)=5.7 × 10-8Sin (1.2t) is respectively the nothing in drive shaft and detection axis Dimension external disturbance.
Formula (2) both sides simultaneously divided byIt is reduced to
Redefining kinetic parameter is
Formula (3) can be expressed as
Wherein, And
Definition
Then formula (4) can be rewritten as
Define θ1=[x, y]T, Then formula (5) can be written as
Wherein, U=[u1,u2]T, F (z)=[f1,f2]T, Δ F (z)=[Δ f1,Δf2]T,
Assuming thatIt is matrix of unknown parameters to be identified,It is continuously differentiable regression function vector, Linear parameterization is carried out to F (z), is obtained
F (z)=W*TΦ(z) (7)
Wherein, Φ (z)=z.
Constructing neural networkΔ F (z) is approached, is obtained
Wherein,It is the input vector of neural network,For the power of neural network Value matrix, M are neural network node number, are chosen for M=5 × 5 × 3 × 3=225,For base vector, q (q=1,2 ..., M) a element definition is following Gaussian function
Wherein, σqIt is the standard deviation of the Gaussian function, is chosen for σq=1,It is the Gauss The center of function, value are any between [- 29.202,29.202] × [- 25.55,25.55] × [- 6.2,6.2] × [- 5,5] It chooses.
(b) reference locus for providing MEMS gyro dynamics formula (1) is
Wherein,WithRespectively reference vibration displacement signal of the detection mass block along drive shaft and detection axis.
Then the reference locus of dimensionless dynamics formula (6) is
Wherein, xd=6.2sin (π/3 4.71t+), yd=5sin (π/6 5.11t-),
Defining tracking error is
Then controller design is
U=Un+Upd-Uad (13)
Upd=K1e1+K2e2 (15)
Wherein,It is W*Estimated value,It is the estimated value of D,
The adaptive law for providing parameter to be identified is
Wherein, first item is calculated using current time data on the right of equation, and Section 2 uses pHThe storage number of a moment point According to calculating, Φ (zi) be Φ (z) i (i=1,2 ..., pH) moment point value,F(zi) For F (z) i (i=1,2 ..., pH) moment point value, and ΓW=diag ([10,12,8,3,71,31]), B=[02×2, I2×2]T, P=diag ([10,12,71,31]).
The more new law for providing neural network weight is
Wherein,
Designing disturbance observer is
Wherein,For intermediate variable,
(c) a matrix Z is definedtStoring data Φ (z), the matrix line number be 6, columns p with storage data quantity change andAssuming that p*It is the last one moment point of storing data,For p*The Φ (z) of moment point, ε=0.08.Ginseng Fig. 2 is examined, the data screening process that parameter update law formula (17) is selected is as follows:
1. ifOr rank ([Zt,Φ(z)])>rank([Zt]), execute step 2., otherwise Give up data Φ (z).
2. ifThen by pHThe Φ (z) at moment is stored in ZtMatrix, even pH=pH+ 1, Zt(:,pH)=Φ (z), It is no to then follow the steps 3..
3. calculating current ZtThe minimum singular value of matrix, and it is denoted as Sold.Then, respectively in i (i=1,2 ..., pH) Φ (z) is stored in Z by the momenttMatrix obtains one group of matrix Calculate different ZtMinimum singular value, and select all minimum singular values In maximum value S.Continue to execute step 4..
4. if S > Sold, by pHThe Φ (z) at moment is stored in ZtMatrix, i.e. Zt(:,pH)=Φ (z), otherwise gives up pHMoment Φ (z).1. return step continues garbled data.
(d) using based on (c) partial data screening technique, parameter update law formula (17), neural network weight more new law Formula (18) and the controller formula (13) of disturbance observer formula (19) design drive dimensionless dynamics formula (6), and are turned by dimension It changes and returns to MEMS gyro kinetic simulation pattern (1), realize gyro drive control.

Claims (1)

1. a kind of data-driven MEMS gyroscope drive control method based on disturbance observation, it is characterised in that steps are as follows:
Step 1: consideration is not known there are quadrature error, system and the MEMS gyro kinetic model of external disturbance are as follows:
Wherein, m is the quality for detecting mass block, ΩzFor gyro input angular velocity,And x*Respectively MEMS gyroscope is examined Mass metering block along the acceleration of drive shaft, speed and displacement,And y*Respectively along the acceleration of detection axis, speed and Displacement,WithFor electrostatic drive power, cxxAnd cyyFor damped coefficient, kxxAnd kyyFor stiffness coefficient,WithIt is non-linear Coefficient, cxyAnd cyxFor damping couple coefficient, kxyAnd kyxFor stiffness coupling coefficient,WithRespectively drive shaft and External disturbance in detection axis；And Wherein WithIt is parametric nominal value, according to the oscillatory type of certain model Silicon micromechanical gyroscope is chosen, Δ kxx、Δkyy、Δcxx、Δcyy、Δkxy、Δkyx、ΔcxyWith Δ cyxIt is not The uncertain parameter known；
Take nondimensionalization time t=ωot*, nondimensionalization displacement x=x*/q0, y=y*/q0, wherein ω0For reference frequency, q0For Reference length, to MEMS gyro kinetic model carry out nondimensionalization processing, and both members simultaneously divided byIt obtains
Wherein,It is respectively nondimensional acceleration, nondimensional velocity of the MEMS gyroscope detection mass block along drive shaft with x And nondimensional displacement,With nondimensional acceleration, nondimensional velocity and nondimensional displacement that y is respectively along detection axis, dx (t) and dy(t) it is respectively dimensionless external disturbance in drive shaft and detection axis；
It redefines
Formula (2) can be expressed as
Define θ1=[x, y]T,Then formula (3) can be written as
Wherein, U=[u1,u2]T, F (z)=[f1,f2]T, Δ F (z)=[Δ f1,Δf2]T,
Assuming thatIt is matrix of unknown parameters to be identified,It is continuously differentiable regression function vector, to F (z) linear parameterization is carried out, is obtained
F (z)=W*TΦ(z) (5)
Wherein, Φ (z)=z；
Constructing neural networkΔ F (z) is approached, is obtained
Wherein,It is the input vector of neural network,For the weight square of neural network Battle array, M are neural network node number to be designed,For base vector, q-th of element definition is following Gauss Function, wherein q=1,2 ..., M；
Wherein, σqIt is Gaussian function standard deviation to be designed,It is that the Gaussian function is to be designed Center；
Step 2: the reference locus for providing MEMS gyro dynamics formula (1) is
Wherein,WithRespectively detection mass block along drive shaft and detection axis reference vibration displacement signal,WithPoint Not Wei drive shaft and detection shaft vibration reference amplitude, ω1And ω2The respectively reference angular frequency of drive shaft and detection shaft vibration,WithThe respectively phase of drive shaft and detection shaft vibration；
Then the reference locus of dimensionless dynamics formula (4) is
Wherein, And parameter to be designed
Defining tracking error is
Then controller design is
U=Un+Upd-Uad (11)
Upd=K1e1+K2e2 (13)
Wherein, parameter to be designedWithMeet Hurwitz condition,It is W*Estimated value,For outside Interfere the estimated value of D；
The adaptive law for providing parameter is
Wherein, first item is calculated using current time data on the right of equation, and Section 2 uses pHThe storing data meter of a moment point It calculates, Φ (zi) it is Φ (z) in the value of i moment point, wherein i=1,2 ..., pH,F(zi) Value for F (z) in i moment point, and parameter to be designedMeet Hurwitz condition, B=[02×2, I2×2]T；
The more new law for providing neural network weight is
Wherein,For matrix to be designed；
Designing disturbance observer is
Wherein,For positive definite matrix to be designed,For intermediate variable；
Step 3: defining a matrix ZtStoring data Φ (z), the matrix line number be 6, columns p with storage data quantity change andAssuming that p*It is the last one moment point of storing data,For p*The Φ (z) of moment point, ε are normal number； The data screening process that parameter update law formula (15) is selected is as follows:
1. ifOr rank ([Zt,Φ(z)])>rank([Zt]), it executes step 2., otherwise gives up Data Φ (z)；
2. ifThen by pHThe Φ (z) at moment is stored in ZtMatrix, even pH=pH+ 1, Zt(:,pH)=Φ (z), otherwise Execute step 3.；
3. calculating current ZtThe minimum singular value of matrix, and it is denoted as Sold；Then, Φ (z) is stored in Z at the i moment respectivelytSquare Battle array, wherein i=1,2 ..., pH, obtain one group of matrix Calculate different Zt Minimum singular value, and select the maximum value S in all minimum singular values；Continue to execute step 4.；
4. if S > Sold, by pHThe Φ (z) at moment is stored in ZtMatrix, i.e. Zt(:,pH)=Φ (z), otherwise gives up pHThe Φ at moment (z)；1. return step continues garbled data；
Step 4: using based on the data screening method described in step 3, parameter update law formula (15), neural network weight update Rule formula (16) and the controller formula (11) of disturbance observer formula (17) design drive dimensionless dynamics formula (4), and pass through dimension Conversion returns to MEMS gyro kinetic simulation pattern (1), realizes gyro drive control.
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