CN107884018B - A kind of coriolis mass flowmeters driving method - Google Patents
A kind of coriolis mass flowmeters driving method Download PDFInfo
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- CN107884018B CN107884018B CN201711012461.0A CN201711012461A CN107884018B CN 107884018 B CN107884018 B CN 107884018B CN 201711012461 A CN201711012461 A CN 201711012461A CN 107884018 B CN107884018 B CN 107884018B
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- vibrating tube
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- vibration
- displacement
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
- G01F1/76—Devices for measuring mass flow of a fluid or a fluent solid material
- G01F1/78—Direct mass flowmeters
- G01F1/80—Direct mass flowmeters operating by measuring pressure, force, momentum, or frequency of a fluid flow to which a rotational movement has been imparted
- G01F1/84—Coriolis or gyroscopic mass flowmeters
Abstract
The present invention provides a kind of coriolis mass flowmeters driving methods, it is related to intelligent instrumentation field, the present invention designs a kind of coriolis mass flowmeters, after displacement sensor detects the vibration displacement of vibrating tube, the frequency of displacement signal is reached into resonance and generates vibration signal, signal generator generates reference signal according to desired constant amplitude and self-oscillation unit and generates error signal, obtain being applied to the driving force in driving coil, the driving force forces the closed-loop control of vibrating tube vibration realizing flowmeter, the present invention increases the digital correction of amplitude unit based on adaptive neural network sliding mode controller, solves the limited technical problem of conventional ADS driving control program amplitude controlling ability, including correcting the temperature error of flowmeter, fluid mass measurement accuracy is improved；Under two-phase stream mode, vibrating tube is made to do diriven motion, fluid mass caused by vibrating tube " failure of oscillation " is avoided to measure Problem of Failure.
Description
Technical field
The present invention relates to intelligent instrumentation field, especially a kind of coriolis mass flowmeters driving method.
Background technique
Coriolis mass flowmeters are a kind of vibration tube-type flowmeters that can directly measure to fluid mass, to this
For mass flowmenter of the kind based on Coriolis effect, efficient, quick, stable drive system is the important guarantor of its measurement accuracy
Card, ideal drive system are necessary for vibrating tube and provide enough driving forces, and vibrating tube is enable to do permanent width with its intrinsic frequency
Periodic vibration.
Classical analogue, drive scheme includes that amplitude controlling and frequency control two parts, generallys use automatic growth control
Circuit provides variable gain, generates the correction voltage changed with detection error, and then realize stable closed loop amplitude controlling；Using
" frequency-selecting " characteristic of phaselocked loop or self oscillatory system realizes the tracking to flowmeter vibrating tube intrinsic frequency.However, in reality
In, this classics driving method but there is a problem of very important.On the one hand, manufacturing defect, environment temperature and outside
Interference will affect its resonance frequency and Oscillation Amplitude；On the other hand, when fluid is in two-phase stream mode, flowing is extremely complex,
The shape of phase interface and its distribution situation in two phase flow constantly change with flow process, and damping increased dramatically, and cause certainly
Dynamic gain control circuit cannot provide sufficiently large gain and maintain flowtube sustained vibration.
Summary of the invention
For overcome the deficiencies in the prior art, for coriolis mass flowmeters in temperature changing environment measurement accuracy compared with
Fluid mass caused by vibrating tube failure of oscillation measures Problem of Failure under low and two-phase stream mode, and the invention proposes one kind to be based on
The coriolis mass flowmeters analog to digital driving method of intelligent control technology.
The technical solution adopted by the present invention to solve the technical problems is through the following steps that realize:
Step 1: according to the kinetic characteristics of coriolis mass flowmeters vibrating tube, provide its kinetics equation:
Wherein,It is respectively vibration acceleration, speed and the displacement of vibrating tube with x；M is the quality of vibrational system,
C, k are the damped coefficient and stiffness coefficient of vibrating tube respectively, and c=-2m ξ ωn,ξ is damping ratio, ωnIt is intrinsic
Frequency, u is adaptive neural network sliding mode controller, and u=f, f are the electrostatic drive power that driving coil generates；
Due to the influence of manufacturing defect, environment temperature and external disturbance, coriolis mass flowmeters vibrating tube is moved
Mechanical equation can be further represented as
Wherein, Δ c, Δ k are Parameter uncertainties unknown caused by manufacturing defect and environment temperature, and d (t) is external dry
It disturbs；
Step 2: the unknown kinetics equation caused by definition manufacturing defect, environment temperature and external disturbance is
Constructing neural networkIt approachesIt obtains
Wherein, XinIt is the input vector of neural network, and For the weight matrix of neural network；θ
(): R2→RMFor the nonlinear function of input, M is neural network node number, and θ is base vector, and i-th of element is by following high
This function is calculated, it may be assumed that
Wherein, Xmi, σiIt is center and the standard deviation of the Gaussian function of formula (5) respectively, and
Define optimal estimation parameter w*For
Wherein, ψ is the set of w；
Therefore, the unknown of kinetic model is represented by
Wherein, ε is the approximate error of neural network；
The evaluated error of the unknown is
Wherein,And For the evaluated error of neural network weight matrix,ForOne
Order derivative,ForFirst derivative；
Step 3: establishing the dynamic (dynamical) reference model of vibrating tube is
Wherein, xmFor with reference to vibration signal, and xm=Amsin(ωxT), AmFor reference amplitude, ωxFor with reference to angular frequency；For xmSecond dervative,
Constructing tracking error is
E=x-xm (10)
Use linear sliding mode function s for
Wherein,It is the first derivative of e, β > 0；
To formula (11) derivation, have
Wherein,ForFirst derivative,For the first derivative of s,For the second dervative of x；
When sliding formwork Reaching Law isWhen, adaptive neural network sliding mode controller is
Wherein, θ is θ (Xin) shorthand, wherein ko> 0；
Choosing adaptive law is
Wherein, r > 0；
Step 4: coriolis mass flowmeters include that analog portion, numerical portion, driving coil, vibrating tube and displacement pass
Sensor, wherein in analog portion include multiplier, automatic growth control and self-oscillation, numerical portion include signal generator and
Adaptive neural network sliding mode controller, after displacement sensor detects the vibration displacement of mass flowmenter vibrating tube, by the displacement
Signal inputs analog portion, wherein and self-oscillation unit makes the frequency of displacement signal gradually level off to the intrinsic frequency of vibrating tube,
Until reaching resonance, automatic gain control unit provides variable gain, makes the amplitude fluctuation of displacement signal less than 10%, automatic to increase
The amplitude of beneficial control unit output and the frequency of self-oscillation unit output pass through multiplier, vibrating tube after generation simulation control
Vibration signal, in numerical portion, signal generator generates ginseng according to the frequency that desired constant amplitude and self-oscillation unit export
Sinusoidal signal, i.e. formula (10) are examined, the vibration signal of vibrating tube compares after the reference signal and simulation control, error signal is generated,
Error signal input adaptive neural networks sliding mode controller, according to adaptive neural network sliding mode controller formula (13) and adaptive
Formula (14) should be restrained, obtain being applied to the driving force in driving coil, which forces vibration tube vibration, and then realizes Ke Liao
The amplitude and Frequency servo of sharp mass flowmenter vibrating tube vibration displacement, i.e. realization analog to digital drive control, make flow
The temperature error of meter is corrected.
Beneficial effects of the present invention are due on the basis of traditional analog drive control scheme, increasing based on adaptive mind
It is limited to solve conventional ADS driving control program amplitude controlling ability for digital correction of amplitude unit through Sliding-Mode Control Based on Network device
Technical problem, including correcting the temperature error of flowmeter, fluid mass measurement accuracy is improved；In two-phase stream mode
Under, so that vibrating tube is done diriven motion, avoids fluid mass caused by vibrating tube " failure of oscillation " from measuring Problem of Failure, substantially improve in section
The performance of benefit mass flowmenter difficult to understand.
Detailed description of the invention
Fig. 1 is that the present invention can inhibit temperature error and can be applied to the mould of the coriolis mass flowmeters of two-phase stream mode
Quasi--digital drive scheme block diagram.
Fig. 2 is the functional block diagram of adaptive neural network sliding mode controller of the present invention.
Fig. 3 is the functional block diagram of the digital correction of amplitude module of the present invention.
When Fig. 4 is that temperature of the present invention increases 3 DEG C, the vibration displacement schematic diagram of vibrating tube.
Fig. 5 is the vibrating tube displacement diagram under two-phase stream mode of the present invention.
Fig. 6 is the vibrating tube displacement diagram after correction of amplitude of the present invention.
Specific embodiment
Present invention will be further explained below with reference to the attached drawings and examples.
The invention discloses a kind of coriolis mass flowmeters analog to digital driving side based on intelligent control technology
Method, in conjunction with Fig. 1-6, specific implementation is analyzed as follows:
Step 1: according to the kinetic characteristics of coriolis mass flowmeters vibrating tube, provide its kinetics equation:
Wherein,It is respectively vibration acceleration, speed and the displacement of vibrating tube with x；M is the quality of vibrational system,
C, k are the damped coefficient and stiffness coefficient of vibrating tube respectively, and c=-2m ξ ωn,ξ is damping ratio, ωnIt is intrinsic
Frequency, the present invention choose m=5.5kg, ξ=0.0005, ωnThe structural parameters of=2 π × 100rad/s, value and flowmeter and
Kinetic characteristics are related, and u is adaptive neural network sliding mode controller, and u=f, f are the electrostatic drive that driving coil generates
Power；
Due to the influence of manufacturing defect, environment temperature and external disturbance, coriolis mass flowmeters vibrating tube is moved
Mechanical equation can be further represented as
Wherein, Δ c, Δ k are Parameter uncertainties unknown caused by manufacturing defect and environment temperature, and d (t) is external dry
It disturbs；
Step 2: the unknown kinetics equation caused by definition manufacturing defect, environment temperature and external disturbance is
Constructing neural networkIt approachesIt obtains
Wherein, XinIt is the input vector of neural network, and For the weight matrix of neural network；θ
(): R2→RMFor the nonlinear function of input, M is neural network node number, and the present invention chooses M=10 × 10=100；θ is base
Vector, i-th of element are calculated by following Gaussian functions, it may be assumed that
Wherein, Xmi, σiIt is center and the standard deviation of the Gaussian function of formula (5) respectively, andIts
Value is arbitrarily chosen between [- 50 50] × [- 5 5], in addition σi=1；
Define optimal estimation parameter w*For
Wherein, ψ is the set of w；
Therefore, the unknown of kinetic model is represented by
Wherein, ε is the approximate error of neural network；
The evaluated error of the unknown is
Wherein,And For the evaluated error of neural network weight matrix,ForOne
Order derivative,ForFirst derivative；
Step 3: establishing the dynamic (dynamical) reference model of vibrating tube is
Wherein, xmFor with reference to vibration signal, and xm=Amsin(ωxT), AmFor reference amplitude, ωxFor with reference to angular frequency；For xmSecond dervative,The present invention chooses Am=5mm, ωx=2 π × 100rad/s；
Constructing tracking error is
E=x-xm (10)
Use linear sliding mode function s for
Wherein,It is the first derivative of e, β > 0；Take β=50；
To formula (11) derivation, have
Wherein,ForFirst derivative,For the first derivative of s,For the second dervative of x；
When sliding formwork Reaching Law isWhen, adaptive neural network sliding mode controller is
Wherein, θ is θ (Xin) shorthand, ko> 0, the present invention takes ko=10；
Choosing adaptive law is
Wherein, r > 0, value of the present invention are r=0.001；
Step 4: coriolis mass flowmeters include that analog portion, numerical portion, driving coil, vibrating tube and displacement pass
Sensor includes wherein referring to Fig.1 multiplier, automatic growth control and self-oscillation in analog portion, numerical portion includes signal
Generator and adaptive neural network sliding mode controller, after displacement sensor detects the vibration displacement of mass flowmenter vibrating tube,
The displacement signal is inputted into analog portion, wherein self-oscillation unit makes the frequency of displacement signal gradually level off to vibrating tube
Intrinsic frequency, until reaching resonance, automatic gain control unit provides variable gain, keeps the amplitude amplitude fluctuation of displacement signal small
In 10%, the amplitude of automatic gain control unit output and the frequency of self-oscillation unit output pass through multiplier, generate simulation
The vibration signal of vibrating tube after control, in numerical portion, signal generator is defeated according to desired constant amplitude and self-oscillation unit
Frequency out, which generates, refers to sinusoidal signal, i.e. formula (10), and the vibration signal of vibrating tube compares after the reference signal and simulation control,
Desired constant amplitude of the invention takes 5mm, generates error signal, and for the purpose of reducing the error signal, error signal input is certainly
Neural networks sliding mode controller is adapted to, Fig. 2 is the functional block diagram of adaptive neural network sliding mode controller, according to adaptive neural network
Sliding-Mode Control Based on Network device formula (13) and adaptive law formula (14) obtain being applied to the driving force in driving coil, which forces
Tube vibration is vibrated, and then realizes the amplitude and Frequency servo of coriolis mass flowmeters vibrating tube vibration displacement, i.e., in fact
Existing analog to digital drive control, corrects the temperature error of flowmeter, and fluid mass measurement accuracy is improved, in addition,
Under two-phase stream mode, vibrating tube is made to do diriven motion, fluid mass caused by vibrating tube " failure of oscillation " is avoided to measure Problem of Failure,
Substantially improve the performance of coriolis mass flowmeters.
As shown in figure 3, when the operating ambient temperature variation of coriolis mass flowmeters leads to the stiffness coefficient of vibrating tube
It changes correspondingly, vibrating tube cannot do permanent width sinusoidal vibration, so that the present invention makes flowmeter when reducing the measurement accuracy of flowmeter
Temperature error is corrected, and fluid mass measurement accuracy is improved；When the fluid air content in flowmeter is higher, i.e., in institute
Two-phase stream mode is called, damping increased dramatically, when automative interest increasing controlling circuit cannot provide the vibration of enough gain maintenance vibrating tubes,
The present invention makes vibrating tube forced oscillation, avoids failure of oscillation phenomenon.
Fig. 4 shows that environment temperature increases 3 DEG C, the process that vibrating tube vibration amplitude is gradually reduced；Fig. 5 shows that fluid contains
Tolerance is stepped up, and vibrating tube displacement strongly reduces, and the process of vibrating tube " failure of oscillation " phenomenon occurs.
On the basis of traditional analog drive control scheme, increase based on the total of adaptive neural network sliding mode controller
Word correction of amplitude unit, vibrating tube vibration displacement is as shown in fig. 6, comparison diagram 4 and Fig. 5, amplitude constant, i.e. temperature error are repaired
Just, and vibrating tube " failure of oscillation " phenomenon is avoided.
Claims (1)
1. a kind of coriolis mass flowmeters driving method, it is characterised in that include the following steps:
Step 1: according to the kinetic characteristics of coriolis mass flowmeters vibrating tube, provide its kinetics equation:
Wherein,It is respectively vibration acceleration, speed and the displacement of vibrating tube with x；M is the quality of vibrational system, and c, k divide
It is not the damped coefficient and stiffness coefficient of vibrating tube, and c=-2m ξ ωn,ξ is damping ratio, ωnFor intrinsic frequency,
U is the electrostatic drive power that driving coil generates, and is obtained by adaptive neural network sliding mode controller；
Due to the influence of manufacturing defect, environment temperature and external disturbance, the dynamics of coriolis mass flowmeters vibrating tube
Equation can be further represented as
Wherein, Δ c, Δ k are Parameter uncertainties unknown caused by manufacturing defect and environment temperature, and d (t) is external disturbance；
Step 2: the unknown kinetics equation caused by definition manufacturing defect, environment temperature and external disturbance is
Constructing neural networkIt approachesIt obtains
Wherein, XinIt is the input vector of neural network, and For the weight matrix of neural network；θ (): R2
→RMFor the nonlinear function of input, M is neural network node number, and θ is base vector, and i-th of element is by following Gaussian functions
It is calculated, it may be assumed that
Wherein, Xmi, σiIt is center and the standard deviation of the Gaussian function of formula (5) respectively, and
Define optimal estimation parameter w*For
Wherein, ψ is the set of w；
Therefore, the unknown of kinetic model is represented by
Wherein, ε is the approximate error of neural network；
The evaluated error of the unknown is
Wherein,And For the evaluated error of neural network weight matrix,ForSingle order lead
Number,ForFirst derivative；
Step 3: establishing the dynamic (dynamical) reference model of vibrating tube is
Wherein, xmFor with reference to vibration signal, and xm=Amsin(ωxT), AmFor reference amplitude, ωxFor with reference to angular frequency；For xm
Second dervative,
Constructing tracking error is
E=x-xm (10)
Use linear sliding mode function s for
Wherein,It is the first derivative of e, β > 0；
To formula (11) derivation, have
Wherein,ForFirst derivative,For the first derivative of s,For the second dervative of x；
When sliding formwork Reaching Law isWhen, adaptive neural network sliding mode controller is
Wherein, θ is θ (Xin) shorthand, wherein ko> 0；
Choosing adaptive law is
Wherein, r > 0；
Step 4: coriolis mass flowmeters include analog portion, numerical portion, driving coil, vibrating tube and displacement sensing
Device, wherein includes multiplier, automatic growth control and self-oscillation in analog portion, and numerical portion includes signal generator and oneself
Neural networks sliding mode controller is adapted to believe the displacement after displacement sensor detects the vibration displacement of mass flowmenter vibrating tube
Number input analog portion, wherein self-oscillation unit makes the frequency of displacement signal gradually level off to the intrinsic frequency of vibrating tube, directly
To resonance is reached, automatic gain control unit provides variable gain, makes the amplitude fluctuation of displacement signal less than 10%, automatic gain
The amplitude of control unit output and the frequency of self-oscillation unit output pass through multiplier, generate the vibration of vibrating tube after simulation control
Dynamic signal, in numerical portion, signal generator is generated according to reference amplitude and with reference to angular frequency with reference to sinusoidal signal, i.e. formula (9),
The vibration signal of vibrating tube compares after the reference signal and simulation control, generates error signal, error signal input adaptive mind
Applied through Sliding-Mode Control Based on Network device according to adaptive neural network sliding mode controller formula (13) and adaptive law formula (14)
Driving force in driving coil, which forces vibration tube vibration, and then realizes coriolis mass flowmeters vibrating tube
The amplitude and Frequency servo of vibration displacement, i.e. realization analog to digital drive control, repair the temperature error of flowmeter
Just.
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| US6378354B1 (en) * | 2000-07-21 | 2002-04-30 | Micro Motion, Inc. | System for calibrating a drive signal in a coriolis flowmeter to cause the driver to vibrate a conduit in a desired mode of vibration |
| CN1372632A (en) * | 1999-07-09 | 2002-10-02 | 微动公司 | Self-characterizing vibrating condult paraneter sensors |
| CN1860350A (en) * | 2003-10-22 | 2006-11-08 | 微动公司 | Diagnostic apparatus and methods for a Coriolis flow meter |
| CN103162755A (en) * | 2013-01-31 | 2013-06-19 | 西安东风机电有限公司 | Coriolis flowmeter signal tracking method based on improved adaptive algorithm |
| CN107102586A (en) * | 2017-05-24 | 2017-08-29 | 西北工业大学 | A kind of coriolis mass flowmeters amplitude control method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103984228B (en) * | 2014-05-31 | 2017-02-01 | 福州大学 | Method for designing Coriolis mass flow meter digital drive system |
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| CN1372632A (en) * | 1999-07-09 | 2002-10-02 | 微动公司 | Self-characterizing vibrating condult paraneter sensors |
| US6378354B1 (en) * | 2000-07-21 | 2002-04-30 | Micro Motion, Inc. | System for calibrating a drive signal in a coriolis flowmeter to cause the driver to vibrate a conduit in a desired mode of vibration |
| CN1860350A (en) * | 2003-10-22 | 2006-11-08 | 微动公司 | Diagnostic apparatus and methods for a Coriolis flow meter |
| CN103162755A (en) * | 2013-01-31 | 2013-06-19 | 西安东风机电有限公司 | Coriolis flowmeter signal tracking method based on improved adaptive algorithm |
| CN107102586A (en) * | 2017-05-24 | 2017-08-29 | 西北工业大学 | A kind of coriolis mass flowmeters amplitude control method |
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