CN106500857A - A kind of temperature sensor temperature-responsive lag compensation method - Google Patents
A kind of temperature sensor temperature-responsive lag compensation method Download PDFInfo
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Abstract
The present invention discloses a kind of temperature sensor temperature-responsive lag compensation method, by setting up predictive compensation model, calculate compensating parameter, the measurement delay of temperature sensor can preferably be overcome, and without the need for the detailed system model parameter for knowing sensor, solve the problems, such as temperature lag, in applications such as electric kettle designs, caused by energy effectively solving high temperature section temperature lag, boiling time is difficult to the problems such as predicting, widen the temperature sensor range of choice, it is not required that the test point of especially adjustment temperature sensor.
Description
Technical field
The present invention relates to temperature sensor art, refers in particular to a kind of temperature sensor temperature-responsive lag compensation side
Method.
Background technology
Temperature sensor due to commonly using is thermocouple and thermal resistance, typically has protection sleeve pipe.During thermometric, need
The change of thermal balance sense temperature to be set up with measured medium, response time is oversize, and temperature survey has serious hysteresis quality.Such as
In kettle temperature measurement application, temperature lag causes high temperature section time control to be forbidden.When existing solution one is to select
Between the little sensor of constant；But select the position of test point according to actual needs.But these methods all fundamentally can not be solved
Certainly problem.
Content of the invention
In view of this, disappearance of the present invention for prior art presence, its main purpose are to provide a kind of temperature sensor
Temperature-responsive lag compensation method, real-time optimization model parameter in temperature taking process, so as to realize to thermometric pre-
Compensation is surveyed, kinetic measurement structure is simplified, be can be widely applied to thermometric every field.
For achieving the above object, the present invention is using such as purgation technical scheme：
A kind of temperature sensor temperature-responsive lag compensation method, comprises the following steps
Step (1), sets up predictive compensation model：According to the dynamic characteristic of temperature sensor, can be with a first-order lag
Transmission function formula (1) is represented
In formula, time constants of the τ for pure lag system, TWIt is the time constant of first-order system, K is proportionality coefficient；
Step (2) calculates compensating parameter, obtains reference locus：According to dynamic compensation filter mathematical model, using transmission
Functional expression (2) calculates compensating parameter
T in formulakIt is the time constant of desired acquisition system after compensation；By dynamic optimization, after calculating compensation
Sensor output value is progressively close to preferable curve, and predictive compensation track is learnt by continuous by particle swarm optimization algorithm,
Most suitable point is selected, optimal value is remembered, constantly study is circulated successively and is obtained most suitable reference locus；
The process of step (3) dynamic optimization compensating module：Process is compensated according to output valve before compensation and reference locus, is obtained
The temperature value gone out after compensation.
The present invention has clear advantage and beneficial effect compared with prior art, specifically, by above-mentioned technical proposal
Understand, the present invention calculates compensating parameter, can preferably overcome the measurement of temperature sensor stagnant by setting up predictive compensation model
Afterwards, and without the need for the detailed system model parameter for knowing sensor, temperature lag is solved the problems, such as, should in electric kettle design etc.
Caused by used time energy effectively solving high temperature section temperature lag, boiling time is difficult to the problems such as predicting.Widen temperature sensor and select model
Enclose, it is not required that the test point of especially adjustment temperature sensor.
For more clearly illustrating the architectural feature and effect of the present invention, come to this with specific embodiment below in conjunction with the accompanying drawings
Bright it is described in detail.
Description of the drawings
Fig. 1 is the dynamic compensation filter mathematical model figure of embodiments of the present invention.
Fig. 2 is the compensating parameter identification principle figure of embodiments of the present invention.
Fig. 3 is that tradition does not compensate delayed Data Comparison curve chart.
After Fig. 4 is the addition backoff algorithm of embodiments of the present invention, high temperature section Data Comparison curve chart.
Specific embodiment
Refer to shown in Fig. 1 and Fig. 2, that show the concrete structure of the preferred embodiments of the invention, be a kind of temperature
Sensor temperature response lag compensation method, real-time optimization model parameter in temperature taking process, so that realize that temperature is surveyed
The predictive compensation of amount, simplifies kinetic measurement structure, can be widely applied to thermometric every field.
The temperature sensor temperature-responsive lag compensation method of the present invention is specifically：
First, predictive compensation model is set up：According to the dynamic characteristic of temperature sensor, a dynamic compensation filter number is set up
Learn model.
In actual applications, exposed temperature sensor can be considered single order link, and its dynamic characteristic can use first differential
Equation is representing.And industry spot is to protect sensor, often in thermal resistance dress people's protection pipe box, cause its lag time compared with
Long.Now, the dynamic characteristic of temperature sensor can add purely retarded to represent with first order inertial loop.Its transmission function can be represented
For:
In formula, time constants of the τ for pure lag system, TWIt is the time constant of first-order system, K is proportionality coefficient.Wherein
Time constant and pure delay time are the key parameters for affecting temperature survey dynamic property, and they depend on the material of sensor itself
Matter and construction featuress, additionally relevant with the flow regime of measured medium and heat transfer type.
In order to realize the compensation to sensor characteristics, need to identify these model parameters first.For biography formula (1) Suo Shi
Sensor model, can be calculated systematic parameter in the following way in real time.
During dut temperature suddenly change △ T, temperature sensor outputting measurement value direct and be S1And quadratic sum is S2, then
When output is stable, as the temperature sensor characteristic parameter of formula (1) can be obtained as follows:
TW=2 (S1-S2/ΔT)/ΔT (2)
τ=tf-S1/ΔT-TW(3)
In formula, tfTime of measuring for sensor stabilization output.
Formula (2) and formula (3) show, as long as being just estimated which using one section of sufficiently long response curve of temperature sensor
The parameter of dynamic model.The time constant of inertial element and time of measuring are without direct relation, but the time constant of pure lag system
Directly related with the data length for calculating.After due to stable state, integration time increases necessarily causes S1Become big, so τ estimates
Can stablize.After tentatively having demarcated sensor, it is possible to the output result for predicting sensor using model parameter.
Dynamic compensation filter mathematical model mainly includes forecast model, reference locus and rolling optimization these thoughts,
Model parameter passes through on-line correction, for realizing the compensation of the dynamic response to existing temperature sensor.Meter for input △ T
Calculate, need by being predicted to realize to the Stepped Impedance Resonators of system on the sensor model parameter basis for estimating.
2nd, compensating parameter is calculated, obtains reference locus.
The predictive compensation algorithm of the present invention is an open cycle system.Signal before compensation comes from the original dynamic of sensor
Output, through forecast model, the reference locus value needed for being compensated, is calculated compensation by the rolling optimization of a period of time
Output result afterwards.Due to the parameter employed in forecast model be all by measurement data direct estimation, with certain with
Machine and error；The interference of external environment, also results in estimated result and there is deviation simultaneously.Must constantly be adjusted by rolling optimization
Whole, the purpose for making system response approach is can be only achieved with reference locus.
Sensor model to (1) formula, can adopt plus a zero-order holder is discrete, obtain the difference equation of model:
ym(k+1)=amym(k)+K(1-am)ΔT(k-L) (4)
In formula:am=e-Ts/τ, TSFor the sampling period, τ is pure delay time, integer parts of the L for τ/Ts.
The output of sensor requires tracking input as quickly as possible, can regard a typical servo system as.One is not lost
As property, it is assumed that sensor input be a step signal, then utilize (2) formula recursion can obtain, P step after system be output as:
ym(k+P)=aPym(k)+K(1-aP)ΔT(k) (5)
Free response of the previous item for model in formula, forced response of the latter for model.
Predictive compensation track can pass through particle swarm optimization algorithm by constantly study, select most suitable point, and memory is optimum
Value, circulates constantly study successively and obtains most suitable reference locus.The particle swarm optimization algorithm has algorithm simple and compensation essence
The characteristics of spending high.
According to dynamic compensation principle, it is because that the frequency band of sensor dynamic characteristic is inadequate the reason for dynamic measurement error is formed
Width, is not enough to cover all frequency components included in transient signal, and makes part high fdrequency components be subject to different degrees of decay.
So, the essence of compensator is that the frequency band for making sensor dynamic characteristic is suitably extended, and it can be a band logical or high pass
Wave filter.But, high pass filter will cause serious noise jamming, therefore, be employed herein band filter.Compensator institute
Corresponding band filter can be represented with a linear difference equation:
A(Z-1)yc(k)=B (Z-1)y(k)+ξ(k) (6)
ξ (k)=A (Z-1)e(k) (7)
Prewhitening filter is taken for 1/A (Z-1), the output of compensator can be written as
In formula:ycThe output of (k) for k moment dynamic compensators, output and dynamic compensator of the y (k) for k moment sensors
Input, e (k) be outfan integrated noise, m for sensor dynamic model order, n for compensation after sensing system dynamic analog
The order of type, the parameter of compensator is
θ=(a1, a2... an,b1,b2...bm) (9)
The mathematical model of sensor dynamic characteristic is relied on during design of Compensator to reduce, using the system of model reference
Discrimination method is compensated device H (Z-1), the principle of identification is as shown in Figure 2.Compensator is designed to one to L=(L=m+n)
The optimization process of dimension parameter θ, even if
Wherein, yc(k) and ydK () is respectively the compensator reality output under pumping signal effect and the experiment for wishing to export
Data, compensator by measuring system in computer realize.
Particle swarm optimization algorithm is as an individual, by multiple group of individuals into colony using the parameter of optimizing.It is by individuality
Regard a microgranule (point) without volume of N-dimensional optimizing search space as, by the fitness to environment by colony
The region movement that individuality is become better, the history optimum position and colony's history optimum position information in conjunction with microgranule, with certain speed
Approach to desired value.I-th microgranule can be expressed as N-dimensional vector xi=[xi1,xi2,...xiN], the speed of microgranule is expressed as:vi
=[vi1,vi2,...viN], the optimum position (corresponding to best fitness) of this microgranule experience is expressed as Pi=[Pi1,
Pi2,...PiN], also referred to as Pbest.The call number of the desired positions of colony's all microgranules experience represented with g, is designated as Pg, also referred to as
gbest.I-th microgranule evolves to n+1 generations from n generations, is updated by following formula.
In formula, w represents inertia weight (inertia weight), and it makes microgranule keep motional inertia so as to extension
The trend of search space, contributes to the search of new region.c1,c2It is arithmetic number, referred to as acceleration constant (acceleration
Constants), they represent and push each microgranule to PbestAnd gbestAcceleration weight.It is in interval [0,1]
The random number of change.
By simulating the process of looking for food of flock of birds, the calculation process of particle swarm optimization algorithm can be described as follows:
(1) according to particular problem, related parameter, such as population scale m, inertia weight w, acceleration constant c are provided with1And c2, calculate
Condition that method terminates etc..And initialize the microgranule in colony, the initial position of microgranule and speed.
(2) execute and { calculate fitness function value f of each microgranulefitness；
F by each microgranulefitnessWith PbestCompare, if ffitnessValue is more excellent, then use ffitnessUpdate Pbest；
F by each microgranulefitnessWith gbestCompare, if ffitnessValue is more excellent, then be set to new gbest, with
Shi Jilu its call number；
According to position and speed that formula (11), (12) update each microgranule }；
When ((be unsatisfactory for performance indications and require) ＆＆ (not completing the optimizing of regulation algebraically)).
(3) optimum microgranule is selected from population, export optimizing result.
According to the measured data that sensor and reference model are responded to input stimulus, by the excellent of particle swarm optimization algorithm
Chemical acquistion is divided into three steps to the process that compensator L ties up parameter θ:
1. according to the dynamic characteristic of sensor, the reference model of desired sensor dynamic characteristic, compensator H (Z are determined-1)
Order n and m, and the parameter θ of compensator=(a1, a2... an,b1,b2...bm).
2. an input signal for being capable of the abundant dynamic characteristic of stimulus sensor is chosen, by experiment acquisition in the excitation
Sensor reality output y (k) and reference model under signal function exports yd(k).
3. with y (k) and yd(k) input and hope output respectively as compensator, by particle swarm optimization algorithm optimizing,
It is compensated the parameter θ of device.Wherein, ffitnessFor formula (10), so, through the compensator that particle swarm optimization algorithm optimization is obtained
H(Z-1), with output yv(k) and hope output ydThe minimum characteristic of (k) error.
The reference locus of predictive compensation are the responding trajectories of the ideal temperature sensor being set by the user, and should generally have
Response time is short, basic non-overshoot, the features such as step response is without steady-state error, can be by a normalized typical first-order system
Step response is described.Shown in its concrete output form such as formula (13):
T in formulakIt is the time constant of desired acquisition system after compensation.Discretization reference locus are as follows:
yr(k+1)=Δ T (k+i)-e(-Tk/Ts)i[ΔT(k)-ym(k)] (14)
In order to prevent oscillatory occurences caused by overcompensation, reference locus from have selected time constant Tk, excellent in conjunction with rolling
For the prediction of input Δ T value in change, system is made progressively to obtain the result of optimum output trajectory.
3rd, dynamic optimization compensating module is processed, and is compensated process according to output valve before compensation and reference locus, is drawn benefit
Temperature value after repaying.
The purpose of predictive compensation is exactly that the output procedure of sensor after rolling optimization calculating makes compensation is progressively close to reason
The curve that thinks.Ideal model formula (13) is compared with the step response model formula (1) of temperature sensor and is understood, mended through prediction
After repaying network processes, the originally larger time constant of system becomes desired smaller value, improves sensor sound so as to reach
Answer the compensation requirement of speed.
4th, experimental data
According to above theory, by taking high temperature section as an example, measured value and simulation result contrast are as follows：
Fig. 3 is that tradition does not compensate delayed Data Comparison curve.After Fig. 4 is addition backoff algorithm, high temperature section Data Comparison is bent
Line, as shown in Figure 4, after backoff algorithm is added, the temperature detection data of temperature sensor closely actual value.
In sum, design focal point of the invention is, by setting up predictive compensation model, calculates compensating parameter, can
Preferably overcome the measurement delay of temperature sensor, and without the need for the detailed system model parameter for knowing sensor, solve temperature
Degree lag issues, in applications such as electric kettle designs can caused by effectively solving high temperature section temperature lag boiling time be difficult to pre-
The problems such as survey.Widen the temperature sensor range of choice, it is not required that the test point of especially adjustment temperature sensor.
The above, is only presently preferred embodiments of the present invention, and not the technical scope of the present invention is imposed any restrictions,
Therefore any trickle amendment, equivalent variations and the modification made to above example by every technical spirit according to the present invention, still
Belong in the range of technical solution of the present invention.
Claims (4)
1. a kind of temperature sensor temperature-responsive lag compensation method, it is characterised in that：Comprise the following steps
Step (1), sets up predictive compensation model：According to the dynamic characteristic of temperature sensor, can be transmitted with a first-order lag
Functional expression (1) is represented
In formula, time constants of the τ for pure lag system, TWIt is the time constant of first-order system, K is proportionality coefficient；
Step (2) calculates compensating parameter, obtains reference locus：According to dynamic compensation filter mathematical model, using transmission function
Formula (2) calculates compensating parameter
T in formulakIt is the time constant of desired acquisition system after compensation；By dynamic optimization, sensor after compensation is calculated
Output valve is progressively close to preferable curve, and by particle swarm optimization algorithm by constantly study, choosing is most closed for predictive compensation track
Suitable point, remembers optimal value, circulates constantly study successively and obtains most suitable reference locus；
The process of step (3) dynamic optimization compensating module：Process is compensated according to output valve before compensation and reference locus, benefit is drawn
Temperature value after repaying.
2. a kind of temperature sensor temperature-responsive lag compensation method according to claim 1, it is characterised in that：Step
(2) in, in order to realize the compensation to sensor characteristics, need to identify compensation model parameter first, for sensing formula (1) Suo Shi
Device model, can be calculated systematic parameter in the following way in real time：
During dut temperature suddenly change △ T, temperature sensor outputting measurement value direct and be S1And quadratic sum is S2, then export
When stable, as the temperature sensor characteristic parameter of formula (1) can be obtained as follows:
TW=2 (S1-S2/ΔT)/ΔT (2)
τ=tf-S1/ΔT-TW(3)
In formula, tfTime of measuring for sensor stabilization output；
Formula (2) and formula (3) show, as long as being just estimated that its dynamic using one section of sufficiently long response curve of temperature sensor
The parameter of model；The time constant of inertial element and time of measuring are without direct relation, but the time constant and use of pure lag system
Directly related in the data length for calculating；After due to stable state, integration time increases necessarily causes S1Become big, thus τ estimate can be with
Stable；After tentatively having demarcated sensor, it is possible to the output result for predicting sensor using model parameter；
Dynamic compensation filter mathematical model mainly includes forecast model, reference locus and rolling optimization these thoughts, model
Parameter passes through on-line correction, for realizing the compensation of the dynamic response to existing temperature sensor；For the calculating of input △ T,
Need by being predicted to realize to the Stepped Impedance Resonators of system on the sensor model parameter basis for estimating；
The predictive compensation algorithm of this forecast model is an open cycle system, and the signal before compensation comes from the original dynamic of sensor
Output, through forecast model, the reference locus value needed for being compensated, is calculated compensation by the rolling optimization of a period of time
Output result afterwards；Due to the parameter employed in forecast model be all by measurement data direct estimation, with certain with
Machine and error；The interference of external environment, is also resulted in estimated result and there is deviation, it is necessary to constantly adjusted by rolling optimization simultaneously
Whole, the purpose for making system response approach is can be only achieved with reference locus；
Sensor model to (1) formula, can adopt plus a zero-order holder is discrete, obtain the difference equation of model:
ym(k+1)=amym(k)+K(1-am)ΔT(k-L) (4)
In formula:am=e-Ts/τ, TSFor the sampling period, τ is pure delay time, integer parts of the L for τ/Ts；
The output of sensor requires tracking input as quickly as possible, can regard a typical servo system as. without loss of generality,
Assume that sensor input is a step signal, then utilize (2) formula recursion obtain, system is output as after P steps:
ym(k+P)=aPym(k)+K(1-aP)ΔT(k) (5)
Free response of the previous item for model in formula, forced response of the latter for model.
3. a kind of temperature sensor temperature-responsive lag compensation method according to claim 1, it is characterised in that：Step
(2), in, the process description of looking for food that the particle swarm optimization algorithm of predictive compensation track can pass through to simulate flock of birds is as follows：
(1) according to particular problem, related parameter, such as population scale m, inertia weight w, acceleration constant c are provided with1And c2, algorithm knot
The condition of beam, and initialize the microgranule in colony, the initial position of microgranule and speed；
(2) execute and { calculate fitness function value f of each microgranulefitness；
F by each microgranulefitnessWith PbestCompare, if ffitnessValue is more excellent, then use ffitnessUpdate Pbest；
F by each microgranulefitnessWith gbestCompare, if ffitnessValue is more excellent, then be set to new gbesT, while note
Record its call number；
According to position and speed that formula (11), (12) update each microgranule }；
In formula, w represents inertia weight, and it makes microgranule keep motional inertia so as to the trend with expanded search space, contributes to
The search of new region；c1,c2Arithmetic number, referred to as acceleration constant is, they represent pushes each microgranule to PbestAnd gbest's
The weight of acceleration；r1 n、It is the random number changed in interval [0,1]；
When ((be unsatisfactory for performance indications and require) ＆＆ (not completing the optimizing of regulation algebraically)).
(3) optimum microgranule being selected from population, exporting optimizing result, method is as follows：According to sensor and reference model to defeated
Enter the measured data of exciter response, the process for device L dimension parameter θs being compensated by the Optimization Learning of particle swarm optimization algorithm is divided into
Three steps:
1. according to the dynamic characteristic of sensor, the reference model of desired sensor dynamic characteristic, compensator H (Z are determined-1) rank
Secondary n and m, and the parameter θ of compensator=(a1, a2... an,b1,b2...bm)；
2. an input signal for being capable of the abundant dynamic characteristic of stimulus sensor is chosen, by experiment acquisition in the pumping signal
Sensor reality output y (k) and reference model under effect exports yd(k)；
3. with y (k) and ydK (), is obtained by particle swarm optimization algorithm optimizing respectively as the input and hope output of compensator
The parameter θ of compensator；Wherein, ffitnessFor formula (10), so, through the compensator H (Z that particle swarm optimization algorithm optimization is obtained-1), with output yv(k) and hope output ydThe minimum characteristic of (k) error.
4. a kind of temperature sensor temperature-responsive lag compensation method according to claim 1, it is characterised in that：Step
(4), in, set up discretization reference locus as follows according to formula (13):
yr(k+1)=Δ T (k+i)-e(-Tk/Ts)i[ΔT(k)-ym(k)] (14)
In order to prevent oscillatory occurences caused by overcompensation, reference locus from have selected time constant Tk, in conjunction with right in rolling optimization
In the prediction of input Δ T value, system is made progressively to obtain the result of optimum output trajectory.
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