CN110488610A - A kind of miniature gas turbine cogeneration system thermic load control method based on robust fuzzy PREDICTIVE CONTROL - Google Patents
A kind of miniature gas turbine cogeneration system thermic load control method based on robust fuzzy PREDICTIVE CONTROL Download PDFInfo
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Abstract
The miniature gas turbine cogeneration system thermic load control method based on robust fuzzy PREDICTIVE CONTROL that the invention discloses a kind of, the following steps are included: the input fuel quantity and hot water temperature's data of acquisition miniature gas turbine cogeneration system, the T-S for establishing thermic load control process obscures affine model；Establish the global amplification state T-S fuzzy model of thermic load control process；Offline fuzzy observer is established based on global amplification state T-S fuzzy model；Based on global amplification state T-S fuzzy model and offline fuzzy observer, robust fuzzy predictive controller is established.The method of the present invention reduces influence of the mission nonlinear to controller using T-S fuzzy model, the immesurable problem of state is overcome using observer, use state amplification overcomes steady-state error, controller freedom degree is enhanced, is a kind of high-quality stable model forecast Control Algorithm suitable for actual complex industrial process.
Description
Technical field
The present invention relates to autocontrol methods, more particularly to a kind of micro-gas-turbine based on robust fuzzy PREDICTIVE CONTROL
Machine cogeneration system thermic load control method.
Background technique
A large amount of consumption of fossil fuel, global warming and environmental degradation have attracted many researchers to find more effectively section
It can method, reduction greenhouse gas emission and pollutant.Miniature gas turbine cogeneration system is a kind of booming height
Effect and clean production of energy new technology.Due to the cascade utilization of the energy, its whole efficiency can be more than 90%, and centralization hair
The thermal efficiency of power plant is between 18% to 47%.Therefore, miniature gas turbine cogeneration system receives significant attention in recent years,
Have become one of the Main way of energy development.However, since the dynamic of miniature gas turbine cogeneration system has big heat
Complex characteristics, the PID approaches such as inertia, multivariable close coupling, input constraint, non-linear and unknown disturbances can no longer meet performance
It is required that.Therefore, it is necessary to study advanced control method to improve operating characteristics.
Summary of the invention
Goal of the invention: it to improve miniature gas turbine cogeneration system thermic load Control platform, proposes a kind of based on Shandong
The miniature gas turbine cogeneration system thermic load control method of stick Fuzzy Predictive Control.
Technical solution: for achieving the above object, the invention adopts the following technical scheme:
A kind of miniature gas turbine cogeneration system thermic load control method based on robust fuzzy PREDICTIVE CONTROL, including
Following steps:
(1) the input fuel quantity and hot water temperature's data for acquiring miniature gas turbine cogeneration system, establish thermic load
The T-S of control process obscures affine model；
(2) the global amplification state T-S fuzzy model of thermic load control process is established；
(3) offline fuzzy observer is established based on global amplification state T-S fuzzy model；
(4) based on global amplification state T-S fuzzy model and offline fuzzy observer, robust fuzzy PREDICTIVE CONTROL is established
Device.
Further, T-S obscures affine model in step (1), and form is as follows:
Regular Rl: if y (k) belongs to Ml,；
Then
Wherein, RlIndicate l rule, MlIndicate that the l articles fuzzy rule, L indicate that regular number, y (k) are indicated in discrete k
The hot water temperature at quarter, u (k) indicate that the input fuel quantity at the discrete k moment, d (k) indicate to interfere in the lump at discrete k moment,Indicate partial model parameter；
Wherein, al1, al2..., alnAnd bl1, bl2..., blnIt isWithCoefficient, q-1It is backward shift operator, n
It is the order of model.
Further, step (2) the following steps are included:
(21) T-S of step (1) is obscured into affine model conversion conditions space form:
Regular Rl: if y (k) belongs to Ml,；
Then
Wherein, x (k) indicates state, MlIndicate the l articles fuzzy rule,I indicates unit square
Battle array,
(22) definition status x (k)=[△ x (k) y (k)]T, amplification state T-S fuzzy model is established, form is as follows:
Regular Rl: if y (k) belongs to Ml,；
Then
Wherein,Cl=[0 I],△ u (k) was indicated at the discrete k moment
Input fuel increment, △ d (k) indicates lump interference increment at the discrete k moment, and △ x (k) indicates the shape at the discrete k moment
State increment；
(23) global amplification state T-S fuzzy model is established, form is as follows:
Wherein,μlIt is subordinating degree function.
Further, offline fuzzy observer in step (3), form are as follows:
Wherein,Indicate estimated state,Indicate the hot water temperature of estimation, yreal(k) hot water of measurement is indicated
Temperature, FlIt is Fuzzy Observer gain, Fl=M-1Nl, l=1,2 ..., L, M and NlIt is obtained by the feasibility problems for solving following
It arrives:
Wherein, * indicates corresponding symmetry blocks, S1,…,SL、N1,…,NLIt is matrix to be asked with M,It is that Fuzzy Observer is estimated
Meter speed degree adjusts matrix, Am, Cm, Ak, CkIt is the local model parameter for expanding model, m=1,2 ..., L, k=1,2 ..., L.
Further, step (4) the following steps are included:
(41) prediction model of robust fuzzy PREDICTIVE CONTROL is established, form is as follows:
Wherein, stateWithIt is the state that the following k+i+1 moment and k+i moment are predicted, △ respectively
U (k+i | k) it is future time instance controlling increment to be asked；WithUtilize matrixIt iterates to calculate to obtain with prediction model,It is obtained using Fuzzy Observer, matrix xr(k)=[0
yr(k)]T, yr(k) it is hot water temperature's setting value at the discrete k moment；
(42) the Infinite horizon performance indicator of robust fuzzy PREDICTIVE CONTROL is established, form is as follows:
Wherein,Q and R is amplification shape respectively
State and controlling increment adjustment parameter；
(43) following optimization problem is converted by the Infinite horizon performance indicator of step (42):
Restrictive condition:
Wherein,
GA(k)=[Aμ(k)]N-1,
GB(k)=[[Aμ(k)]N-1Bμ(k) [Aμ(k)]N-2Bμ(k) … [Aμ(k)]0Bμ(k)],
γ1,γ2, Y, S, △ U (k) is variable to be optimized, △ U (k)=[△ u (k | k), △ u (k+1 | k) ..., △ u (k
+N-1|k)]T,Indicate Kronecker product, Q and R are that state and controlling increment are adjusted respectively
Parameter,φ=γ1+(1+αw2)γ2, w is observation errorThe upper bound,α be to
Fixed parameter, uminAnd umaxRespectively indicate the minimum value and maximum value of given fuel quantity, △ uminWith △ umaxIt respectively indicates given
The minimum value and maximum value of fuel increment, N are the step number freely controlled, AiAnd BiIt is the local model parameter for expanding model；
(44) input fuel quantity u (k)=△ u (k | the k)+u (k-1) at discrete k moment, the fuel quantity at discrete k moment are calculated
Increment △ u (k | k) it is obtained according to the optimization problem of step (43), and then control hot water temperature's tracking fixed valure.
The utility model has the advantages that compared with prior art, the invention has the following advantages:
(1) the robust fuzzy forecast Control Algorithm proposed, reduces mission nonlinear to controller using T-S fuzzy model
Influence, the immesurable problem of state is overcome using observer, use state amplification overcomes steady-state error, enhances control
Device freedom degree is a kind of high-quality stable model forecast Control Algorithm suitable for actual complex industrial process.
(2) emulation experiment shows that the robust fuzzy forecast Control Algorithm of proposition efficiently solves miniature gas turbine heat
Chp system thermic load controls existing controlling difficulties, and control effect is better than Fuzzy Predictive Control and PID control, has tracking
The advantages of speed is fast, and overshoot is small, strong antijamming capability.
Detailed description of the invention
Fig. 1 is the method for the present invention flow chart of steps；
Fig. 2 is miniature gas turbine cogeneration system schematic diagram in the embodiment of the present invention；
Fig. 3 is fuzzy membership function in the embodiment of the present invention；
Fig. 4 is proposition method of the present invention miniature gas turbine cogeneration system thermic load control result in experiment 1；
Fig. 5 is proposition method of the present invention miniature gas turbine cogeneration system thermic load control result in experiment 2.
Specific embodiment
Technical solution of the present invention is described further in the following with reference to the drawings and specific embodiments.
The method of the present invention obscures affine model by establishing the T-S of thermic load control process first come the dynamic of approximate procedure
Then characteristic is promoted Design of Predictive by state space transformation, and then eliminates control deviation by state amplification,
Improve controller freedom degree, then due to state can not be surveyed, based on fuzzy liapunov function establish Fuzzy Observer come
Estimate not measured state, robust fuzzy predictive controller is finally established based on amplification fuzzy model and Fuzzy Observer derivation.
As shown in Figure 1, a kind of miniature gas turbine cogeneration system heat based on robust fuzzy PREDICTIVE CONTROL of the present invention
Duty control method, comprising the following steps:
It is negative to establish heat for step 1, the input fuel quantity and hot water temperature's data for acquiring miniature gas turbine cogeneration system
The T-S of lotus control process obscures affine model, and form is as follows:
Regular Rl: if y (k) belongs to Ml,
Then
Wherein, RlIndicate l rule, MlIndicate that the l articles fuzzy rule, L indicate that regular number, y (k) are indicated in discrete k
The hot water temperature at quarter, u (k) indicate that the input fuel quantity at the discrete k moment, d (k) indicate to interfere in the lump at discrete k moment,Indicate partial model parameter；
Wherein, al1, al2..., alnAnd bl1, bl2..., blnIt isWithCoefficient, q-1It is backward shift operator, n
It is the order of model.
Step 2, the global amplification state T-S fuzzy model for establishing thermic load control process, specifically in accordance with the following steps:
The T-S of step 1 is obscured affine model conversion conditions space form by step 2.1:
Regular Rl: if y (k) belongs to Ml,
Then
Wherein, x (k) indicates state,I indicates unit matrix,
Step 2.2, definition status x (k)=[△ x (k) y (k)]T, amplification state T-S fuzzy model is established, form is such as
Under:
Regular Rl: if y (k) belongs to Ml, then
Wherein,Cl=[0 I],△ u (k) was indicated at the discrete k moment
Input fuel increment, △ d (k) indicates lump interference increment at the discrete k moment, and △ x (k) indicates the shape at the discrete k moment
State increment；
Step 2.3 establishes global amplification state T-S fuzzy model, and form is as follows:
Wherein,μlIt is subordinating degree function.
Step 3 establishes offline fuzzy observer based on global amplification state T-S fuzzy model, and form is as follows:
Wherein,Indicate estimated state,Indicate the hot water temperature of estimation, yreal(k) hot water of measurement is indicated
Temperature, FlIt is Fuzzy Observer gain, Fl=M-1Nl, l=1,2 ..., L, M and NlIt is obtained by the feasibility problems for solving following
It arrives:
Wherein, * indicates corresponding symmetry blocks, S1,…,SL、N1,…,NLIt is matrix to be asked with M,It is that Fuzzy Observer is estimated
Meter speed degree adjusts matrix, Am, Cm, Ak, CkIt is the local model parameter for expanding model, m=1,2 ..., L, k=1,2 ..., L.
Step 4 establishes robust fuzzy PREDICTIVE CONTROL based on global amplification state T-S fuzzy model and offline fuzzy observer
Device, specifically in accordance with the following steps:
Step 4.1, the prediction model for establishing robust fuzzy PREDICTIVE CONTROL, form are as follows:
Wherein, stateWithIt is the state that the following k+i+1 moment and k+i moment are predicted, △ respectively
U (k+i | k) it is future time instance controlling increment to be asked；WithUtilize matrixIt iterates to calculate to obtain with prediction model,It is obtained using Fuzzy Observer, matrix xr(k)=[0
yr(k)]T, yr(k) it is hot water temperature's setting value at the discrete k moment；
Step 4.2, the Infinite horizon performance indicator for establishing robust fuzzy PREDICTIVE CONTROL, form such as:
Wherein,Q and R is amplification shape respectively
State and controlling increment adjustment parameter；
The infinite performance indicator of step 4.2 is converted following optimization problem by step 4.3:
Restrictive condition:
Wherein,
GA(k)=[Aμ(k)]N-1,
GB(k)=[[Aμ(k)]N-1Bμ(k) [Aμ(k)]N-2Bμ(k) … [Aμ(k)]0Bμ(k)],
γ1,γ2, Y, S, △ U (k) is variable to be optimized, △ U (k)=[△ u (k | k), △ u (k+1 | k) ..., △ u (k
+N-1|k)]T,Indicate Kronecker product, Q and R are that state and controlling increment are adjusted respectively
Parameter,φ=γ1+(1+αw2)γ2, w is observation errorThe upper bound,α is given
Parameter, uminAnd umaxRespectively indicate the minimum value and maximum value of given fuel quantity, △ uminWith △ umaxRespectively indicate given combustion
Expect the minimum value and maximum value of increment, N is the step number freely controlled, AiAnd BiIt is the local model parameter for expanding model；
Step 4.4, input fuel quantity u (k)=△ u (k | the k)+u (k-1) for calculating discrete k moment, the combustion at discrete k moment
Doses increment △ u (k | k) it is obtained according to the optimization problem of step 4.3, and then control hot water temperature's tracking fixed valure.
Embodiment.
The miniature gas turbine cogeneration system formed herein with certain 80kw miniature gas turbine and 115kw heating system
For carry out emulation experiment, structure chart is as shown in Fig. 2, and illustrate design method and embodiment of the invention.Fig. 2 institute
The miniature gas turbine cogeneration system shown is made of miniature gas turbine and heating system.The main portion of miniature gas turbine
Part includes compressor, combustion chamber, turbine, regenerator, generator etc..The course of work of miniature gas turbine is as follows: compressor from
Ambient inlet air is re-fed into combustion chamber by regenerator after compression, while gas fuel is also injected into combustion chamber and high temperature
Compressed air mixing, burn under level pressure；The high temperature and high pressure flue gas of generation enters turbine acting, and impeller is pushed to drive compressor
Impeller rotates together with, while the rotation of pushing generator rotor produces electricl energy；Lack of gas after acting are by regenerator, then arrange through blower
Out.The main component of heating system is shell-and-tube heat exchanger and water-water heat-exchangers of the plate type.The gas of miniature gas turbine discharge
Body empties after flue gas heat-exchange unit and the water heat exchange from cooling tower；Water of high temperature by network management road into
Enter water-water heat-exchangers of the plate type, heating secondary water generates heat, flows back into cooling tower again after itself cooling and completes circulation.System
The performance variable of system is input fuel quantity, and output variable is hot water temperature.
In order to keep the technical problem to be solved in the present invention, technical scheme and beneficial effects clearer, with reference to the accompanying drawing
And specific embodiment is described in detail.
According to the nonlinear Distribution of thermic load system, it is 74.6 DEG C, 79 DEG C and 82.8 DEG C operating points in hot water temperature, utilizes
Least squares identification partial model, it is as follows that the T-S of foundation obscures affine model parameter:
Submodel 1:
A1(q-1)=2.135120q-1-1.494200q-2+0.353138q-3,
B1(q-1)=1.687021q-1+10.832402q-2+3.668229q-3,
c1=0.391291；
Submodel 2:
A2(q-1)=2.345988q-1-1.870562q-2+0.519516q-3,
B2(q-1)=11.685152q-1-1.476615q-2-1.074589q-3,
c2=0.362511；
Submodel 3:
A3(q-1)=2.287583q-1-1.802933q-2+0.508128q-3,
B3(q-1)=1.343743q-1+11.237466q-2+4.476165q-3,
c3=0.506673.
Subordinating degree function is as shown in Figure 3.Solid line is the subordinating degree function of submodel 1, and dotted line is the degree of membership letter of submodel 2
Number, chain-dotted line is the subordinating degree function of submodel 3.
The design parameter of robust fuzzy predictive controller are as follows:
Sampling time Ts=5s, Q=diag { 7,12,8,0.8 }, R=10, w=11, α=40000, N=6,Fuzzy Observer gain F1=[- 0.327617 1.184540-1.290176-
2.284492],
F2=[- 0.477774 1.526774-1.482163-2.477671], F3=[- 0.467327 1.464713-
1.428585 -2.424010]。
In view of fuel transfer valve door restrict, following constraint is considered:
0≤u (k)≤0.011kg/s, -0.0005≤△ u (k)≤0.0005kg/s.
In order to verify the superiority of proposition method, while fuzzy model prediction control and PID control being implemented into shown in Fig. 2
Miniature gas turbine cogeneration system.
Experiment 1: thermic load tracing control is changed hot water temperature's setting value 1000 seconds and 1500 seconds, emulation knot
Fruit is as shown in Figure 4.Solid line indicates the control effect of proposition method of the present invention, and chain-dotted line is Fuzzy Predictive Control as a result, dotted line is
PID control result.Fig. 4 (a) indicates that hot water temperature's controlling curve figure, Fig. 4 (b) indicate input fuel regulation curve graph, Fig. 4
(c) input fuel quantity increment control algorithm curve graph is indicated；
Experiment 2: interference--free experiments were interfered d to be influenced, hot water temperature at 1500 seconds by hot water temperature shown in Fig. 5 (a)
Setting value remains unchanged, and simulation result is as shown in Figure 5.Solid line indicates the control effect of proposition method of the present invention, and chain-dotted line is mould
PREDICTIVE CONTROL is pasted as a result, dotted line is PID control result.Fig. 5 (a) indicates that hot water temperature's controlling curve figure, Fig. 5 (b) indicate input
Fuel regulation curve graph, Fig. 5 (c) indicate input fuel quantity increment control algorithm curve graph；
As known to experiment 1 and 2, the robust fuzzy forecast Control Algorithm of proposition efficiently solves miniature gas turbine thermoelectricity
Co-feeding system thermic load controls existing controlling difficulties, and control effect is better than Fuzzy Predictive Control and PID control, has tracking speed
The advantages of degree is fast, and overshoot is small, strong antijamming capability.
Claims (5)
1. a kind of miniature gas turbine cogeneration system thermic load control method based on robust fuzzy PREDICTIVE CONTROL, feature
It is, comprising the following steps:
(1) the input fuel quantity and hot water temperature's data for acquiring miniature gas turbine cogeneration system, establish thermic load control
The T-S of process obscures affine model；
(2) the global amplification state T-S fuzzy model of thermic load control process is established；
(3) offline fuzzy observer is established based on global amplification state T-S fuzzy model；
(4) based on global amplification state T-S fuzzy model and offline fuzzy observer, robust fuzzy predictive controller is established.
2. a kind of miniature gas turbine cogeneration system heat based on robust fuzzy PREDICTIVE CONTROL according to claim 1
Duty control method, which is characterized in that T-S obscures affine model in step (1), and form is as follows:
Regular Rl: if y (k) belongs to Ml,；
Then
Wherein, RlIndicate l rule, MlIndicate that the l articles fuzzy rule, L indicate that regular number, y (k) indicated at the discrete k moment
Hot water temperature, u (k) indicate that the input fuel quantity at the discrete k moment, d (k) indicate to interfere in the lump at discrete k moment,Indicate partial model parameter；
Wherein, al1, al2..., alnAnd bl1, bl2..., blnIt isWithCoefficient, q-1It is backward shift operator, n is mould
The order of type.
3. a kind of miniature gas turbine cogeneration system heat based on robust fuzzy PREDICTIVE CONTROL according to claim 1
Duty control method, which is characterized in that step (2) the following steps are included:
(21) T-S of step (1) is obscured into affine model conversion conditions space form:
Regular Rl: if y (k) belongs to Ml,；
Then
Wherein, x (k) indicates state, MlIndicate the l articles fuzzy rule,I indicates unit matrix,
(22) definition status x (k)=[△ x (k) y (k)]T, amplification state T-S fuzzy model is established, form is as follows:
Regular Rl: if y (k) belongs to Ml,；
Then
Wherein,△ u (k) was indicated at the discrete k moment
Fuel increment is inputted, △ d (k) indicates the lump interference increment at the discrete k moment, and △ x (k) indicates the state at the discrete k moment
Increment；
(23) global amplification state T-S fuzzy model is established, form is as follows:
Wherein,μlIt is subordinating degree function.
4. a kind of miniature gas turbine cogeneration system heat based on robust fuzzy PREDICTIVE CONTROL according to claim 1
Duty control method, which is characterized in that offline fuzzy observer in step (3), form are as follows:
Wherein,Indicate estimated state,Indicate the hot water temperature of estimation, yreal(k) hot water temperature of measurement is indicated,
FlIt is Fuzzy Observer gain, Fl=M-1Nl, l=1,2 ..., L, M and NlIt is obtained by the feasibility problems for solving following:
Wherein, * indicates corresponding symmetry blocks, S1,…,SL、N1,…,NLIt is matrix to be asked with M,It is Fuzzy Observer estimation speed
Degree adjusts matrix, Am, Cm, Ak, CkIt is the local model parameter for expanding model, m=1,2 ..., L, k=1,2 ..., L.
5. a kind of miniature gas turbine cogeneration system heat based on robust fuzzy PREDICTIVE CONTROL according to claim 1
Duty control method, which is characterized in that step (4) the following steps are included:
(41) prediction model of robust fuzzy PREDICTIVE CONTROL is established, form is as follows:
Wherein, stateWithIt is the state that the following k+i+1 moment and k+i moment are predicted, △ u (k+ respectively
I | it k) is future time instance controlling increment to be asked；WithUtilize matrixWith it is pre-
Model is surveyed to iterate to calculate to obtain,It is obtained using Fuzzy Observer, matrix xr(k)=[0 yr(k)]T, yrIt (k) is in discrete k
Hot water temperature's setting value at moment；
(42) the Infinite horizon performance indicator of robust fuzzy PREDICTIVE CONTROL is established, form is as follows:
Wherein,Q and R be respectively amplification state and
Controlling increment adjustment parameter；
(43) following optimization problem is converted by the Infinite horizon performance indicator of step (42):
Restrictive condition:
Wherein,
GA(k)=[Aμ(k)]N-1,
GB(k)=[[Aμ(k)]N-1Bμ(k) [Aμ(k)]N-2Bμ(k) … [Aμ(k)]0Bμ(k)],
γ1,γ2, Y, S, △ U (k) is variable to be optimized, △ U (k)=[△ u (k | k), △ u (k+1 | k) ..., △ u (k+N-1
|k)]T, Indicating Kronecker product, Q and R are state and controlling increment adjustment parameter respectively,φ=γ1+(1+αw2)γ2, w is observation errorThe upper bound,α is given ginseng
Number, uminAnd umaxRespectively indicate the minimum value and maximum value of given fuel quantity, △ uminWith △ umaxGiven fuel is respectively indicated to increase
The minimum value and maximum value of amount, N are the step number freely controlled, AiAnd BiIt is the local model parameter for expanding model；
(44) input fuel quantity u (k)=△ u (k | the k)+u (k-1) at discrete k moment, the fuel quantity increment at discrete k moment are calculated
△ u (k | k) it is obtained according to the optimization problem of step (43), and then control hot water temperature's tracking fixed valure.
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