CN103941584A - Temperature control method based on fuzzy self-adaptive controller - Google Patents

Abstract

The invention discloses a temperature control method based on a fuzzy self-adaptive controller. The temperature control method is realized via the fuzzy self-adaptive controller, a three-phase voltage-regulating module, a heating module and a temperature sensor. The fuzzy self-adaptive controller is composed of generalized prediction control and fuzzy control. The fuzzy self-adaptive controller is generated via combination of the generalized prediction control and the fuzzy control. The generalized prediction control or the fuzzy control are selected by the fuzzy self-adaptive controller to realize flexible control of a heating system according to different error situations of the system so that heating is more uniform, temperature control is more accurate and thus industrial production efficiency is enhanced.

Description

A kind of temperature-controlled process based on fuzzy adaptive controller

Technical field:

Patent of the present invention relates to a kind of temperature-controlled process, relates in particular to the design of fuzzy adaptive controller.

Background technology:

In commercial production, some processing technology has strict requirement to heating-up temperature, and excess Temperature or temperature are too low all may cause immeasurable loss, has become a major issue so how strictly to control temperature.The electric heater of general control has a wider heated perimeter, in the time that commercial production is higher to temperature control requirement, the electric heater of general control often can not meet more accurate control requirement, and in some commercial production, use the electric heater of general control easily to cause the inequality of being heated, cause heating effect not good.

In the middle of some need the production of Manual material feeding, the homogeneity that is difficult to ensure each feed weight and volume, causes system model parameter uncertain, and the electric heating system of general control simultaneously itself has large time delay, nonlinear feature, adopt conventional PID to control, performance is difficult to be guaranteed.

Generalized predictive control and fuzzy control have made up the large time delay in common heating system, nonlinear shortcoming well.By by generalized predictive control and fuzzy control combination, can realize the elementary object that comparatively sensitive output control and the temperature difference are dwindled, and system robustness is strong, be conducive to product homogeneous heating simultaneously, reach good heating effect.

Summary of the invention:

The present invention is taking fuzzy adaptive controller as core.By by generalized predictive control and fuzzy control combination, produce fuzzy adaptive controller, the error condition different according to system, select voluntarily generalized predictive control or fuzzy control by fuzzy adaptive controller, realize the flexible control to heating system, make heating more even, temperature control is more accurate, thereby improves commercial production efficiency.

The technical scheme that patent of the present invention adopts is:

Based on a temperature-controlled process for fuzzy adaptive controller, realized by fuzzy adaptive controller, three-phase voltage regulation module, heating module, temperature sensor.

Fuzzy adaptive controller is made up of generalized predictive control and fuzzy control.

Fuzzy adaptive controller has a soft switch E ₀, E ₀can be normal value, variable or function, work as error | e|≤E ₀, switching over is to generalized predictive control; Work as error | e| > E ₀, switching over is to fuzzy control.

Generalized predictive control is the generalized predictive control based on CARMA model, and the CARMA model of controlled device can be expressed as:

A(z ^-1)y(k)＝z ^-dB(z ^-1)u(k)+C(z ^-1)ξ(k)，

Y in formula (k), u (k), ξ (k) represent respectively output, controlled quentity controlled variable and white noise, and when adopting when unknown parameters band forgetting factor recursion Recursive Extended Least Squares Method to carry out parameter estimation in formula, d is pure time delay, and:

$\left\{\begin{array}{c}A\left(z^{-1}\right)=1+a_{\mathrm{1,1}}{z}^{-1}+a_{\mathrm{1,2}}{z}^{-2}+...+a_{1,n_a}{z}^{-n_a}\\ B\left(z^{-1}\right)=b_{\mathrm{1,0}}+b_{\mathrm{1,1}}{z}^{-1}+b_{\mathrm{1,2}}{z}^{-2}+...+b_{1,n_b}{z}^{-n_b},b_{\mathrm{1,0}}\≠0\\ C\left(z^{-1}\right)=1+c_{\mathrm{1,1}}{z}^{-1}+c_{\mathrm{1,2}}{z}^{-2}+...+c_{1,n_c}{z}^{-n_c}\end{array}\right.$

In formula be real number.

GPC (Generalized Predictive Control) algorithm is designed to:

Get initial value P (0)=10 ⁶i, θ (0)=[0,0,0,0,0,0] ^t, forgetting factor λ=0.95, controls its parameter N=8, and controlling weighting matrix Γ is unit matrix I _{5 × 5}, output softening factor α=0.7, GPC (Generalized Predictive Control) algorithm is as follows:

1) sample current actual output y (k) and with reference to output y _r(k+j);

2) adopt the online estimation in real time of forgetting factor recursion Recursive Extended Least Squares Method measurand θ, i.e. A (z ^-1), B (z ^-1), C (z ^-1);

3) solve gating matrix G;

4) structure compute vector Y _r, Y _m;

5) calculate u (k);

6) return to the first step, continue circulation.

FUZZY ALGORITHMS FOR CONTROL is designed to:

In control system, fuzzy controller input quantity is the deviation e=y-y of observed temperature and preferred temperature _rwith deviation variation rate ec=e ₂-e ₁, its actual domain is e ∈ (10,10), unit is degree Celsius.Select quantizing factor K _e=0.3, X, Y is discrete, and domain is all [3 ,-2 ,-1,0,1,2,3], is output as the control voltage u of three-phase voltage regulation module.Determine linguistic variable according to adding heat control experience e, eC, usubordinate function curve.The FUZZY ALGORITHMS FOR CONTROL of this patent is based on inquiry fuzzy control rule table, and the cardinal rule of choosing controlled quentity controlled variable is: in the time that error is larger, select taking the controlled quentity controlled variable of eliminating as early as possible error as main; When error hour, select taking can anti-overshoot controlled quentity controlled variable as main.

Brief description of the drawings:

Fig. 1 is the control method block diagram based on fuzzy adaptive controller.

Fig. 2 is linguistic variable e, eC, usubordinate function figure.

Fig. 3 is fuzzy control rule table.

Embodiment:

Based on a temperature-controlled process for fuzzy adaptive controller, realized by fuzzy adaptive controller 1, three-phase voltage regulation module 2, heating module 3, temperature sensor 4.

Fuzzy adaptive controller 1 is made up of generalized predictive control 5 and fuzzy control 6.

Fuzzy adaptive controller 1 has a soft switch E ₀, E ₀can be normal value, variable or function, work as error | e|≤E ₀, switching over is to generalized predictive control 5; Work as error | e| > E ₀, switching over is to fuzzy control 6.

Generalized predictive control 5 its algorithm design are:

Get initial value P (0)=10 ⁶i, θ (0)=[0,0,0,0,0,0] ^t, forgetting factor λ=0.95, controls its parameter N=8, and controlling weighting matrix Γ is unit matrix I _{5 × 5}, output softening factor α=0.7, GPC (Generalized Predictive Control) algorithm is as follows:

1) sample current actual output y (k) and with reference to output y _r(k+j);

2) adopt the online estimation in real time of forgetting factor recursion Recursive Extended Least Squares Method measurand θ, i.e. A (z ^-1), B (z ^-1), C (z ^-1);

3) solve gating matrix G;

4) calculate and construct vectorial Y _r, Y _m;

5) calculate u (k);

6) return to the first step, continue circulation.

Fuzzy control 6 algorithm design are:

In control system, fuzzy controller input quantity is the deviation e=y-y of observed temperature and preferred temperature _rwith deviation variation rate ec=e ₂-e ₁, its actual domain is e ∈ (10,10), unit is degree Celsius.Select quantizing factor K _e=0.3, X, Y is discrete, and domain is all [3 ,-2 ,-1,0,1,2,3].Be output as the control voltage u of three-phase voltage regulation module 3.Determine linguistic variable according to adding heat control experience e, eC, usubordinate function curve.The FUZZY ALGORITHMS FOR CONTROL of this patent is based on inquiry fuzzy control rule table, and the cardinal rule of choosing controlled quentity controlled variable is: in the time that error is larger, select taking the controlled quentity controlled variable of eliminating as early as possible error as main; When error hour, select taking can prevent overshoot controlled quentity controlled variable as main.

Claims (10)

1. the temperature-controlled process based on fuzzy adaptive controller, is characterized in that: described control method is realized by fuzzy adaptive controller (1), three-phase voltage regulation module (2), heating module (3), temperature sensor (4).

2. a kind of temperature-controlled process based on fuzzy adaptive controller according to claim 1, is characterized in that: described fuzzy adaptive controller (1) comprises generalized predictive control (5) and fuzzy control (6).

3. fuzzy adaptive controller according to claim 2 (1), is characterized in that: fuzzy adaptive controller (1) has a soft switch, can be normal value, variable or function, works as error, and switching over is to generalized predictive control (5); Work as error, switching over is to fuzzy control (6).

4. generalized predictive control according to claim 2 (5), is characterized in that: generalized predictive control (5) is the generalized predictive control based on CARMA model, and the CARMA model of controlled device can be expressed as:

A(z ^-1)y(k)＝z ^-dB(z ^-1)u(k)+C(z ^-1)ξ(k)，

Y in formula (k), u (k), ξ (k) represent respectively output, controlled quentity controlled variable and white noise, and when adopting when unknown parameters band forgetting factor recursion Recursive Extended Least Squares Method to carry out parameter estimation in formula, d is pure time delay, and:

In formula be real number.

5. generalized predictive control according to claim 2 (5) algorithm design is:

Get initial value, forgetting factor, controls parameter N=8, and control weighting matrix is unit matrix, output softening factor, and GPC (Generalized Predictive Control) algorithm is as follows:

1) sample current actual output and with reference to output;

2) adopt the online estimation in real time of forgetting factor recursion Recursive Extended Least Squares Method measurand;

3) solve gating matrix G;

4) calculate and construct vector;

5) calculate;

6) return to the first step, continue circulation.

6. fuzzy control according to claim 2 (6) algorithm design is:

In control system, fuzzy controller input quantity is deviation and the deviation variation rate of observed temperature and preferred temperature, and its actual domain is, unit is degree Celsius.

7. select quantizing factor, X, the discrete domain of Y is all [3 ,-2 ,-1,0,1,2,3].

8. be output as the control voltage u of three-phase voltage regulation module (3).

9. according to the subordinate function curve that adds heat control experience and determine linguistic variable.

10. the FUZZY ALGORITHMS FOR CONTROL of this patent is based on inquiry fuzzy control rule table, and the cardinal rule of choosing controlled quentity controlled variable is: in the time that error is larger, select taking the controlled quentity controlled variable of eliminating as early as possible error as main; When error hour, select controlled quentity controlled variable can prevent overshoot as main.