Disclosure of Invention
The invention aims to solve the technical problem of providing the intelligent control method for blowing argon at the bottom of the refining furnace, which has simple system structure, low cost and high control precision.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the technical core of the invention is to adopt a fuzzy self-adaptive control method with a hierarchical structure, wherein the hierarchical structure divides the whole control system into three-level control structures: (1) basic fuzzy control stage. In order to meet the real-time control requirement of the system, a fuzzy logic control mode is adopted in the basic fuzzy control stage. (2) And (4) self-adaptive adjustment of the stage. In order to adapt to the time-varying condition of the parameters of the controlled system, a self-adaptive control mode is adopted, and the parameters of the fuzzy controller are adjusted on line at regular time. (3) The process state decision level. In order to overcome the influence of process state change (or different actual working conditions), improve the robust performance of the control system, judge the process state as the auxiliary input quantity, and adopt the corresponding fuzzy controller parameter set according to the process state of the system.
The method comprises the following specific steps:
firstly, an input step:
the following parameters were entered into the industrial computer: the ladle number and the steel type number of the refining furnace, argon blowing flow set values corresponding to the ladle number and the steel type number at each stage, a fuzzy rule base adopted by selection and the range of a fuzzy variable discourse domain;
II, data acquisition:
real-time values of the following sensors are collected by the industrial computer in real time: the device comprises a flow sensor for measuring the flow of argon gas, a pressure sensor for measuring the pressure of the argon gas and a temperature sensor for measuring the ambient temperature of an argon supply branch;
thirdly, calculating:
the following calculation steps are accomplished by an industrial computer equipped with a fuzzy adaptive algorithm with a hierarchical structure:
(1) basic fuzzy control stage:
a. calculating the error e and the error change rate delta e of the system;
b. the error e and the error change rate delta e of the system are transformed into respective discourse domain ranges through scales;
c. carrying out fuzzy processing on the input quantity converted into the discourse domain range to change the original accurate input quantity into a fuzzy quantity;
d. calculating a fuzzy value of the control quantity u through fuzzy reasoning;
e. determination of the control amount u:
firstly, the value z of the controlled variable u in the theory domain is obtained by a weighted average method0(ii) a Then z is transformed by scaling0Becomes the actual control amount uoutput;
(2) self-adaptive regulation stage:
when the output of the control quantity u does not meet the control requirement, adopting a membership degree output value adjusting method to carry out self-adaptive adjustment, then entering the item d in the step (1), and finally correcting the output value of the control quantity u;
(3) process state determination stage:
when the system working condition is changed greatly, such as the steel type and the steel ladle change, the change is judged by the industrial computer, and a fuzzy control rule base is automatically selected to adapt to the change;
fourthly, executing the steps:
the control quantity u is used for controlling argon blowing through an actuating mechanism.
The actuating mechanism is a regulating valve arranged on a normal branch of the argon supply system.
The flow sensor is arranged on the pipeline of the normal branch.
The pressure sensor is arranged on the pipeline of the normal branch.
The temperature sensor is arranged on the pipe wall of the pipeline of the normal branch or around the pipeline of the normal branch.
The invention has the advantages that the fuzzy self-adaptive control method with the hierarchical structure is adopted, so the control precision of the control system is greatly improved, the production efficiency and the steelmaking quality are improved, in addition, the control system has simple structure and lower cost.
Detailed Description
The system structure diagram of the embodiment is shown in fig. 1, and the system is composed of an argon tank 16, a pressure buffer tank 14, a normal branch, an accident branch, three sensors, a flow totalizer 12, an industrial computer 10, a regulating valve 7, a pressure reducing valve 1, a check valve 8, a safety valve 15, an electromagnetic valve 2 and the like (detailed in fig. 1); the pressure buffer tank 14, the flow sensor 3 and the pressure sensor 4 are arranged on the pipeline of the normal branch, and the temperature sensor 5 is arranged on the pipe wall of the pipeline of the normal branch or around the pipeline of the normal branch. An adjusting valve 7 (actuator) and the solenoid valve 2 are also installed on the normal branch, and the opening degree of the adjusting valve 7 is controlled by the output (control amount u) of the industrial computer 10.
The outputs of the flow sensor 3, the pressure sensor 4 and the temperature sensor 5 are respectively connected with the input end of the industrial computer 10 through a flow totalizer 12. The flow totalizer 12 and the industrial computer 10 are installed in the control room 11.
The system can realize two functions of manual control and computer automatic control. On one hand, when the bottom argon blowing system works normally, controlled argon is blown into the refining furnace through the normal branch, so that the argon blowing process is realized; when argon blowing work is started, higher pressure can be provided to blow the blocked air brick open so as to ensure the normal start of the argon blowing work. On the other hand, in order to increase the reliability of the system, the control system can be switched to the accident branch in time for manual operation after the control system fails in the argon blowing process, and the normal operation of production is ensured.
The flow totalizer 12 adopts a vortex-connected flow meter; the check valve 8 is a device for preventing high-pressure gas of an accident branch from entering a normal branch and damaging the normal branch.
In fig. 1 and 2, 6, 9 and 13 are valves, 15 is a safety valve, 16 is an argon tank, r (k) is a set value, e (k) is a system error, u (k) is a control quantity, and y (k) is an actual argon flow value.
The present embodiment includes the following four steps:
firstly, an input step is carried out,
the following parameters were entered into the industrial computer: the ladle number and the steel type number of the refining furnace, argon blowing flow set values corresponding to the ladle number and the steel type number at each stage, an adopted fuzzy rule base and the range of a fuzzy variable universe are selected;
II, data acquisition:
real-time values of the following sensors are collected by the industrial computer in real time: the device comprises a flow sensor for measuring the flow of argon gas, a pressure sensor for measuring the pressure of the argon gas and a temperature sensor for measuring the ambient temperature of an argon supply branch;
thirdly, calculating:
the following calculation steps are accomplished by an industrial computer equipped with a fuzzy adaptive algorithm with a hierarchical structure:
(1) basic fuzzy control stage:
a. calculating the error e and the error change rate delta e of the system:
e=r-y
△e=de/dt=e(i)-e(i-1)/T
wherein T is the control period of the system, r is the argon flow set value, y is the actual argon flow value, e (i) is the error of the ith moment, and e (i-1) is the error of the ith-1 moment.
b. And (3) converting the error e and the error change rate delta e of the system into respective discourse domain ranges through scales, wherein the general formula is as follows:
wherein k islCalled scale factor
Is the actual input quantity
Is composed of
Range of variation
[xmin,xmax]Is the domain of discourse required;
c. the input quantity converted into the discourse domain range is subjected to fuzzy processing, so that the original accurate input quantity is changed into fuzzy quantity, and the following bell-shaped membership function is adopted:
<math> <mrow> <msub> <mi>μ</mi> <mi>A</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <mfrac> <msup> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <msub> <mi>x</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mrow> <mn>2</mn> <msup> <mi>σ</mi> <mn>2</mn> </msup> </mrow> </mfrac> </mrow> </msup> </mrow></math>
wherein x0Is the central value, σ, of the membership function2Is the variance;
d. calculating a fuzzy value of the control quantity u through fuzzy reasoning, wherein the fuzzy reasoning adopts the following formula:
wherein,
for values of language variables representing error e
For values of linguistic variables representing rates of change of error Δ e
For fuzzy implication relationships obtained from a control rule base
B' is a linguistic variable value representing the controlled variable u;
e. determination of the control amount u:
obtaining the value z of the control quantity u in the theory domain by a weighted average method0:
<math> <mrow> <msub> <mi>z</mi> <mn>0</mn> </msub> <mo>=</mo> <mi>df</mi> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <munderover> <mo>∫</mo> <mi>a</mi> <mi>b</mi> </munderover> <mi>z</mi> <msub> <mi>μ</mi> <msup> <mi>B</mi> <mi>i</mi> </msup> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> <mi>dz</mi> </mrow> <mrow> <munderover> <mo>∫</mo> <mi>a</mi> <mi>b</mi> </munderover> <msub> <mi>μ</mi> <msup> <mi>B</mi> <mi>i</mi> </msup> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </mfrac> </mrow></math>
By transforming z by scale0Becomes the actual control amount uoutput:
wherein k isOCalled the output scale factor
[zmin,zmax]Is z0Scope of discourse of
[umin,umax]Is the variation range of the output quantity;
(2) self-adaptive regulation stage:
when the output of the control quantity u does not meet the control requirement, adopting a membership degree output value adjusting method to carry out self-adaptive adjustment, then entering the item d in the step (1), and finally correcting the output value of the control quantity u;
the adaptive adjustment of the membership output value adjusting method is adopted, and belongs to direct fuzzy adaptive control:
u=uc(x|θ)+uD
wherein u isc(x | θ) is
<math> <mrow> <msub> <mi>u</mi> <mi>c</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>|</mo> <mi>θ</mi> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>[</mo> <munderover> <mi>Σ</mi> <mrow> <mi>l</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <msub> <mover> <mi>y</mi> <mo>‾</mo> </mover> <mi>l</mi> </msub> <mrow> <mo>|</mo> <munderover> <mi>Π</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>μ</mi> <msubsup> <mi>F</mi> <mi>i</mi> <mi>l</mi> </msubsup> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>|</mo> </mrow> <mo>]</mo> </mrow> <mo>/</mo> <mrow> <mo>[</mo> <munderover> <mi>Σ</mi> <mrow> <mi>l</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <mrow> <mo>|</mo> <munderover> <mi>Π</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>μ</mi> <msubsup> <mi>F</mi> <mi>i</mi> <mi>l</mi> </msubsup> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>|</mo> </mrow> <mo>]</mo> </mrow> </mrow></math>
Wherein
For state x in the l rule
iFor fuzzy subsets
Degree of membership, n is the number of states, M is the number of rules, y
lThe output value corresponding to the conclusion membership degree of 1 in the first rule; will y
lAs tunable parameters, the above equation can be written as:
<math> <mrow> <msub> <mi>u</mi> <mi>c</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>|</mo> <mi>θ</mi> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mi>θ</mi> <mi>T</mi> </msup> <mi>ξ</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>ξ</mi> <mi>l</mi> </msup> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <munderover> <mi>Π</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>μ</mi> <msubsup> <mi>F</mi> <mi>i</mi> <mi>l</mi> </msubsup> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <munderover> <mi>Σ</mi> <mrow> <mi>l</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <mrow> <mo>|</mo> <munderover> <mi>Π</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>μ</mi> <msubsup> <mi>F</mi> <mi>i</mi> <mi>l</mi> </msubsup> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>|</mo> </mrow> </mrow> </mfrac> </mrow></math>
wherein θ ═ y1,…,yM)TIs a parameter vector, xi (x) ═ xi (xi)1(x),…,ξM(x))TIs the regression vector, and xi1(x) Referred to as fuzzy basis functions; u. ofD=kdsgn(eTPbc) For D control, kd≥0,bc=[0,0,…,b]TIf e isTPbc>0, then uD=kdIf e isTPbc<0, then uD=-kd;
The adaptive law for the parameter vector θ is:
wherein P isr[*]Is defined as:
<math> <mrow> <msub> <mi>P</mi> <mi>r</mi> </msub> <mrow> <mo>[</mo> <mi>γ</mi> <msup> <mi>e</mi> <mi>T</mi> </msup> <msub> <mi>P</mi> <mi>n</mi> </msub> <mi>ξ</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> <mo>=</mo> <mi>γ</mi> <msup> <mi>e</mi> <mi>T</mi> </msup> <msub> <mi>p</mi> <mi>n</mi> </msub> <mi>ξ</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>-</mo> <mi>γ</mi> <msup> <mi>e</mi> <mi>T</mi> </msup> <msub> <mi>P</mi> <mi>n</mi> </msub> <mfrac> <mrow> <msup> <mi>θθ</mi> <mi>T</mi> </msup> <mi>ξ</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> <msup> <mrow> <mo>|</mo> <mi>θ</mi> <mo>|</mo> </mrow> <mn>2</mn> </msup> </mfrac> </mrow></math>
Pnis the last column of P, | theta | < Mθ<∞,MθIs a finite upper bound of the θ vector;
p is a positive definite matrix and satisfies the Lyapunov equation ΛTP + Λ ═ Q, where Q is an arbitrary positive definite matrix of n × n;
<math> <mrow> <mi>Λ</mi> <mo>=</mo> <mrow> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mn>1</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mo>·</mo> <mo>·</mo> <mo>·</mo> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>1</mn> </mtd> <mtd> <mo>·</mo> <mo>·</mo> <mo>·</mo> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mo>·</mo> <mo>·</mo> <mo>·</mo> </mtd> <mtd> <mo>·</mo> <mo>·</mo> <mo>·</mo> </mtd> <mtd> <mo>·</mo> <mo>·</mo> <mo>·</mo> </mtd> <mtd> <mo>·</mo> <mo>·</mo> <mo>·</mo> </mtd> <mtd> <mo>·</mo> <mo>·</mo> <mo>·</mo> </mtd> </mtr> <mtr> <mtd> <mo>-</mo> <msub> <mi>k</mi> <mi>n</mi> </msub> </mtd> <mtd> <mo>-</mo> <msub> <mi>k</mi> <mrow> <mi>n</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> </mtd> <mtd> <mo>-</mo> <msub> <mi>k</mi> <mrow> <mi>n</mi> <mo>-</mo> <mn>2</mn> </mrow> </msub> </mtd> <mtd> <mo>·</mo> <mo>·</mo> <mo>·</mo> </mtd> <mtd> <mo>-</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> </mtd> </mtr> </mtable> </mfenced> </mrow> <mo>;</mo> </mrow></math>
(3) process state determination stage:
when the system working condition is changed greatly, such as the steel type and the steel ladle change, the change is judged by the industrial computer, and a fuzzy control rule base is automatically selected to adapt to the change;
fourthly, executing the steps:
the control quantity u is used for controlling argon blowing through an actuating mechanism.