CN110471285B - Trend event driven fuzzy control method for zinc smelting roasting process - Google Patents
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Abstract
The invention relates to a trend event driven fuzzy control method for a zinc smelting roasting process, which is characterized in that a temperature set value and a control period N are set, temperature deviation and temperature trend extraction are calculated according to a real-time sampling temperature value of a sensor, fuzzy control is performed in time according to the temperature trend and the temperature deviation when the working condition changes or reaches a preset control period, and the problems of difficult working condition evaluation and reduced control performance of a fuzzy controller caused by dynamic characteristics of the roasting process and field environment constraints are solved.
Description
Technical Field
The invention relates to the technical field of fuzzy control, in particular to a trend event driven fuzzy control method for a zinc smelting roasting process.
Background
The roasting process is the first procedure of the zinc smelting process, and in the process, the mixed zinc concentrate is sent into a roasting furnace for full combustion to produce zinc calcine, sulfur dioxide, smoke dust and other products. Zinc calcine is used as a main raw material in a zinc hydrometallurgy process, and the product quality of the zinc calcine is crucial to the production of downstream processes. The main purpose of the roasting process is to ensure the product quality of the zinc calcine, namely to improve the zinc-soluble rate of the zinc calcine and reduce the content of insoluble impurities. Because the quality of the zinc calcine mainly depends on the composition of the mixed zinc concentrate and the temperature of the roasting process, the most important problem in the roasting process is to ensure the stability of the roasting temperature under different working conditions. Because the dynamic characteristics of the roasting process and the field environment are very complex, the roasting process is often changed under various working conditions, and the traditional PID controller is difficult to realize the stable control of the temperature in an industrial field. However, the performance of the manual control based on experience is greatly dependent on the subjective factors of operators, so that the performance of the manual control is unstable, and untimely and improper control often occurs.
Based on the characteristics of the roasting process, fuzzy control is more suitable for temperature control in the roasting process, in the prior art, the fuzzy control on the temperature mostly adopts temperature deviation and the change of the temperature deviation as input variables, respectively shows the current state and the potential state in a period of time in the future, evaluates the working condition according to the two states, and then controls the output regulating variable, namely the regulating value of the feeding quantity, so that the temperature is stabilized in the required range. However, due to the dynamic characteristics of the roasting process and the constraints of the field environment, the general control period in the industrial field is selected to be over ten minutes and far greater than one sampling period per minute, under the condition, the dynamic state of the roasting process is evaluated by using the change of the temperature deviation, and due to the change of the working condition and various disturbances in the roasting process, inaccurate or wrong evaluation results can be obtained, so that the performance of the fuzzy controller is reduced; if the control period is shortened, because a great time lag exists in the roasting process, a period of time is needed to reflect the temperature change after the feeding amount is adjusted, so that the feeding amount is frequently adjusted, the roasting system is unstable, and the performance of the fuzzy controller is also reduced. Therefore, a fuzzy control method is needed to solve the problems of difficult working condition evaluation and reduced control performance of the fuzzy controller caused by the dynamic characteristics of the roasting process and the constraints of the field environment.
Disclosure of Invention
Technical problem to be solved
Based on the problems, the invention provides a trend event driven fuzzy control method for a zinc smelting roasting process, which is used for responding to the dynamic characteristics and the field environment of the roasting process, carrying out effective working condition evaluation by using a trend extraction method, and carrying out fuzzy control according to temperature deviation and temperature trend in time when the working condition changes or reaches a preset control period so as to improve the performance of a fuzzy controller.
(II) technical scheme
Based on the technical problem, the invention provides a trend event driven fuzzy control method for a zinc smelting roasting process, which comprises the following steps:
s1, setting a temperature set value and a control period N;
s2, calculating temperature deviation and extracting temperature trend according to a set value and a real-time sampling temperature value of a sensor, screening out the temperature trend and the temperature deviation when the working condition changes or reaches a preset control period based on an event-driven strategy of the temperature trend, and timely inputting the temperature trend and the temperature deviation to a fuzzy control unit;
and S3, setting rules based on expert experience, fuzzifying the input temperature deviation and temperature trend, performing fuzzy reasoning, performing defuzzification, outputting an adjustment value of the feeding amount, and adjusting the feeding amount in the roasting process by an actuator.
Further, the event-driven strategy based on the temperature trend in step S2 includes the following steps:
s2.1, inputting a given control period N and a time window (t)1,ti) Temperature data of the inside;
s2.2, constructing a trend model;
s2.3, judging whether the temperature trend changes, if not, entering a step S2.4, and if so, entering a step S2.6;
s2.4, determining whether i +1 is equal to N, if so, proceeding to step S2.6;
s2.5, if the result of the S2.4 is negative, returning to the step S2.2;
s2.6, calculating a first derivative of the trend model at the current moment;
s2.7, sending the first derivative and the temperature deviation of the current moment to a fuzzy controller;
s2.8, judging whether to finish control, if so, pausing, otherwise, stopping from ti+1A new time window is started from time to time and the process proceeds to step S2.2.
Further, the step of determining whether the temperature trend changes as described in step S2.3 comprises the steps of:
s2.3.1 calculating the prediction error ei+1And a firstClass threshold th1,iDetermine whether | ei+1|≤th1,iIf yes, go to step S2.3.4;
s2.3.2, if the result of S2.3.1 is negative, then the current temperature y is judgedi+1Whether or not it is an outlier, i.e. for a time window
All time instants t inj，i+1≤j≤i+lthCalculating its corresponding prediction error ejAnd a first class threshold th1,j-1And determining whether all | ej|≥th1,j-1If not, then yi+1Is an outlier, proceed to step S2.3.4;
s2.3.3, if S2.3.2 results in yes, then the current temperature yi+1Not an outlier, the temperature trend has changed;
s2.3.4, calculating the cumulative error cusum (t)i+1) And a threshold th of the second kind2,i. Judging whether | cusum (t)i+1)|≤th2,iIf yes, the temperature trend is not changed, and if no, the temperature trend is changed.
Further, the time window (t) described in step S2.11,ti) Has a length of i, lthI is not less than i and not more than N, the control period N is the maximum length of the time window lthIs the minimum time window length.
Further, the trend model in step S2.2 is:
wherein, Yi＝[y1,y2···yi]T，
Are the parameters of the model and are,
in order to estimate the deviation of the noise,
as output of the model, i.e. temperature prediction, ykAs measured temperature value, tkIs a time value, k is more than or equal to 1 and less than or equal to i and less than or equal to N, and m is more than or equal to 1 and less than or equal to 3.
Further, the prediction error ei+1Threshold th of the first class1,iCumulative error cusum (t)i+1) And a threshold th of the second kind2,iRespectively as follows:
cusum(ti+1)＝cusum(ti)+ei+1
ai＝((Ti TTi)-1(Ti)T)T[1(ti+1-t1)(ti+1-t1)2]T
bi+1＝[(ai+bi)T 1]T
wherein,
representing the t distribution, alpha is the confidence level of the t distribution,
further, the first derivative of the current trend model described in step S2.6 is:
further, the blurring described in step S3 is described as:
the fuzzification of the temperature deviation is described as: very high VH, slightly high LH, suitably Z, slightly low LL and very low VL; describing VH by adopting an S-type membership function, describing VL by using a Z-type membership function, and describing the rest by using a bell-shaped membership function;
the blurring of the temperature trend is described as: very large HP, slightly larger LP, stable S, slightly smaller LN, and very small HN; describing HP by adopting an S-type membership function, HN by using a Z-type membership function, and describing the others by using bell-shaped membership functions;
the fuzzification of the adjustment values for the feed rates is described as: positive big PB, positive PM, positive small PS, zero O, negative small NS, negative middle NM and negative big NB; and the PB is described by adopting an S-type membership function, the NB is described by a Z-type membership function, and the others are described by bell-shaped membership functions.
Further, the Z-type membership function Zp(x) S type membership function Sp(x) Sum-bell membership function
Rule setting based on expert experience is as follows:
wherein, ap、bp、cp、dp、
V and v are parameter values of a membership function, and p is 1,2 and 3; q ═ 2, ·,2, p · 1,2,3 represent the membership function belonging to temperature deviation, temperature trend and feed rate, respectively; q represents different cases of bell-shaped functions.
Further, the inverse fuzzy method in step S3 is a center of gravity method, and its expression is:
wherein u is*As an adjustment value of the feed quantity, i.e. the output value of the fuzzy control, ufThe adjustment value of the feeding amount corresponding to the membership function is obtained, n is the number of the membership functions of the adjustment value of the feeding amount, and n is 7.
(III) advantageous effects
The technical scheme of the invention has the following advantages:
(1) the method comprises the steps of acquiring a temperature trend in real time, carrying out fuzzy control according to the temperature trend and temperature deviation in time by utilizing a trend driving strategy based on the temperature trend when a working condition changes or reaches a preset control period, and further adjusting the feeding amount, so that the temperature of the system is stable, the sampling data is richer, and the input data of the fuzzy control is screened, so that the whole control process is more timely and more accurate and the system is more stable;
(2) the trend driving strategy based on the temperature trend considers two conditions of temperature trend change, no temperature trend change and gradual temperature deviation, so that the working condition evaluation is more accurate, the adjustment is carried out through fuzzy control, the influence of the working condition transition on the system is processed, and the control performance of the fuzzy control is better;
(3) the input quantity of the fuzzy control adopts temperature deviation and temperature trend instead of temperature deviation and deviation increment, conforms to the dynamic characteristics of the roasting process, has smaller overshoot, has quicker regulation time when the working condition is changed, has more stable temperature control and less influence by the change of the working condition;
(4) the method judges the abnormal value, so that the possibility of misjudgment is reduced;
(5) the method adopts fuzzy control, makes up the defects of a mathematical model to a certain extent, and is more visual and reasonable because the center-of-gravity method is adopted for defuzzification.
Drawings
The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:
FIG. 1 is a structural block diagram of a fuzzy control method for zinc smelting roasting based on trend event driving in an embodiment of the present invention;
FIG. 2 is a flow chart illustrating an event-driven strategy based on temperature trend according to an embodiment of the present invention;
FIG. 3 is a table of membership functions for temperature deviation and temperature trend with adjusted values for feed rate in accordance with the present invention;
FIG. 4 is a fuzzy inference rule between temperature deviation and temperature trend and adjustment of feed rate in accordance with the present invention;
FIG. 5 is a graph comparing control performance according to the first embodiment of the present invention;
FIG. 6 is a table comparing control performance indicators according to a first embodiment of the present invention;
FIG. 7 is a graph comparing control performances according to a second embodiment of the present invention;
FIG. 8 is a table comparing control performance indicators according to a second embodiment of the present invention;
in the figure: 1: a setting unit; 2: a trend event driven policy unit; 3: a fuzzy control unit.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The invention discloses a trend event driven fuzzy control method for a zinc smelting roasting process, which is shown in a structural block diagram of a control method shown in figure 1 and comprises the following steps:
s1, setting unit 1 sets the temperature set value of the roasting process and the control period N of the fuzzy controller.
S2, the trend event driving strategy unit 2 calculates temperature deviation and extracts temperature trend according to the set value and the real-time sampling temperature value of the sensor, screens out the temperature trend and the temperature deviation when the working condition changes or reaches a preset control period based on the event driving strategy of the temperature trend, and inputs the temperature trend and the temperature deviation into the fuzzy control unit 3 in time for fuzzy control.
The event-driven strategy based on the temperature trend described in step S2, as shown in fig. 2, includes the following steps:
s2.1, inputting a given control period N and a time window (t)1,ti) Temperature data of the inside;
t1and tiRespectively as the starting point and the end point of the time window, the control period N is the maximum length of the time window, and in order to ensure the effect of fitting the model, a minimum length l of the time window must be setthI.e. lthI is not less than N, if i<lthThen wait for more temperature data until i ≧ lth。
S2.2, constructing a trend model;
for the time window (t)1,ti) The trend model is built by using the least square method for the internal temperature data:
wherein, Yi＝[y1,y2···yi]T，TiIs a vector, a sub-term thereof
Represents tkThe exponent power of m-1, the superscript m-1 is the exponent power after least square method fitting,
are the parameters of the model and are,
in order to estimate the deviation of the noise,
as output of the model, i.e. temperature prediction, ykAs measured temperature value, tkIs a time value, k is more than or equal to 1 and less than or equal to i and less than or equal to N, and m is more than or equal to 1 and less than or equal to 3.
And S2.3, judging whether the temperature trend changes, if not, entering a step S2.4, and if so, triggering a trend event, and entering a step S2.6.
The step of judging whether the temperature trend changes comprises the following steps:
s2.3.1 calculating the prediction error ei+1And a first class threshold th1,i：
ai＝((Ti TTi)-1(Ti)T)T[1(ti+1-t1)(ti+1-t1)2]T
Wherein,
representing the t distribution, and alpha is the confidence level of the t distribution;
determine whether | ei+1|≤th1,iIf yes, go to step S2.3.4;
s2.3.2, if the result of S2.3.1 is negative, then the current temperature y is judgedi+1Whether or not it is an outlier, i.e. for a time window
All time instants t inj，i+1≤j≤i+lthAre calculated by the formula in S2.3.1jAnd a first class threshold th1,j-1，
And determine whether all | ej|≥th1,j-1If not, then yi+1Is an abnormal value, if so, yi+1Is not an outlier;
s2.3.3, if yi+1If it is an abnormal value, go to step S2.3.4, if yi+1If not, the temperature trend has changed;
s2.3.4, calculating the cumulative error cusum (t)i+1) And a threshold th of the second kind2,i：
cusum(ti+1)＝cusum(ti)+ei+1
bi+1＝[(ai+bi)T 1]T
Wherein i is more than or equal to lth，
Determining whether or notcusum(ti+1)|≤th2,iIf yes, the temperature trend is not changed, and if no, the temperature trend is changed.
S2.4, determine whether i +1 is equal to N, i.e. whether the time window length has reached the given control period, if so, the process goes to step S2.6 if the period event is triggered.
S2.5, if the result of S2.4 is negative, determining the current temperature yi+1Added to the previous time window and returned to step S2.2.
S2.6, calculating a first derivative of the trend model at the current moment; in order to measure the current trend, the first derivative of the trend model at the current moment is used as a measurement index, and the calculation formula is as follows:
to generalize the method, a time window (t) is used1,ti) Normalized to [0,1]. At the end of each trend, its first derivative
The derivative value represents the trend of the previous temperature time series at the current moment, and can provide a more accurate reference for temperature control.
And S2.7, sending the first derivative and the temperature deviation of the current moment to the fuzzy controller, wherein no matter the triggering of the trend event of the step S2.3 or the triggering of the period event of the step S2.4 indicates that the working condition is changed, so that the fuzzy controller can control the roasting process according to the temperature deviation and the temperature trend.
S2.8, judging whether to finish control, if so, pausing, otherwise, stopping from ti+1A new time window is started from time to time and the process proceeds to step S2.2.
S3, the fuzzy control unit 3 fuzzifies the input temperature deviation and temperature trend based on the rule setting of expert experience, outputs the adjustment value of the feeding amount after fuzzy reasoning and defuzzification, and adjusts the feeding amount in the roasting process by an actuator.
In step S3, 3 membership functions are set based on expert experience rules: z-type membership function Zp(x) S type membership function Sp(x) Sum-bell membership function
Respectively as follows:
wherein, ap、bp、cp、dp、
(p ═ 1,2, 3; q ═ 2, ·,2) and ν are parameter values of a membership function, p ═ 1,2,3 represent that the membership function belongs to temperature deviation, temperature trend and feed quantity respectively; q represents the different cases of the bell-shaped membership function.
The temperature deviation is described by fuzzification as: very High (VH), slightly high (LH), moderate (Z), slightly low (LL) and Very Low (VL). VH is described by an S-type membership function, VL is described by a Z-type membership function, and the others are described by bell-shaped membership functions.
Likewise, the fuzzification of the temperature trend is described as: very large (HP), slightly Large (LP), stable (S), slightly small (LN), and very small (HN). For HP, an S-type membership function is used for description, for HN, a Z-type membership function is used for description, and for the others, a bell-shaped membership function is used for description.
In order to realize more accurate control, the classification of the adjustment value of the feeding amount is more detailed, and the fuzzy description is as follows: positive Big (PB), Positive Middle (PM), Positive Small (PS), zero (O), Negative Small (NS), Negative Middle (NM), and Negative Big (NB). And the PB is described by adopting an S-type membership function, the NB is described by a Z-type membership function, and the others are described by bell-shaped membership functions.
The selection of the membership functions for the temperature deviation and the temperature trend with the adjusted value of the feed quantity and the detailed parameters of the membership functions are shown in FIG. 3.
The fuzzy inference rule between the temperature deviation and the adjustment value of the temperature trend and the feeding quantity is given by figure 4, and the fuzzy inference rule is described by sentences as follows:
If TD＝VH and TT＝HP or LP or S then U＝NB；
If TD＝VH and TT＝LN or HN then U＝NM；
If TD＝LH and TT＝HP or LP then U＝NM；
If TD＝LH and TT＝S or LN or HN then U＝NS；
If TD＝Z and TT＝HP then U＝NS；
If TD＝Z and TT＝LP or S or LN then U＝O；
If TD＝Z and TT＝HN then U＝PS；
If TD＝LL and TT＝HP or LP or S then U＝PS；
If TD＝LL and TT＝LN or HN then U＝PM；
If TD＝VL and TT＝HP or LP then U＝PM；
If TD＝VL and TT＝S or LN or HN then U＝PB；
and TD is the fuzzification temperature deviation, TT is the fuzzification temperature trend, and U is the adjustment value of the fuzzification feeding amount.
In the method, the adopted defuzzification method is a gravity center method, and the expression is as follows:
wherein u is*As an adjustment value of the feed quantity, i.e. the output value of the fuzzy control, ufFor adjustment of the amount of feed in relation to the membership function, n being the adjustment of the amount of feedThe number of membership functions, where n is 7.
The first embodiment is as follows:
to demonstrate the effectiveness of the method, the set temperature value for the firing process was set to 910 ℃ under the same initial conditions, and the performance of the proposed fuzzy control method was compared with the conventional fuzzy control method. The conventional fuzzy control method has the same membership function and fuzzy inference rule as the proposed method, but is different in that the conventional method uses a change rate of temperature deviation and has no corresponding event-driven strategy.
For example, as shown in fig. 5, the overshoot amount of the fuzzy control method provided by the present method is 0.2706, and the adjustment time is 23 minutes, whereas the overshoot amount of the conventional fuzzy control method is 0.4829, and the adjustment time is 81 minutes, compared with the conventional fuzzy control method, the fuzzy control method provided by the present method has smaller overshoot amount and adjustment time, and the specific comparison is shown in fig. 6.
Example two:
to demonstrate that the proposed method can effectively handle the transition of the operating conditions, after the set value is 910 ℃ and both controllers reach steady state, a step signal with amplitude of 10 ℃ is added to the system to simulate the transition of the operating conditions.
The control effect is shown in fig. 7, the overshoot of the fuzzy control method provided by the method is 1.0573, the adjustment time is 57 minutes, while the overshoot of the conventional fuzzy control method is 1.0147, the adjustment time is 207 minutes, and compared with the conventional fuzzy control method, the fuzzy control method provided by the method has smaller adjustment time and has no oscillation in the adjustment process when the working condition changes. Because the method has smaller steady state deviation, the overshoot of the system after adding the step signal is larger. A specific comparison of control performance after a shift in operating conditions is shown in FIG. 8.
In conclusion, the fuzzy control method for the zinc smelting roasting process based on trend event driving has the following advantages:
(1) the method comprises the steps of acquiring a temperature trend in real time, carrying out fuzzy control according to the temperature trend and temperature deviation in time by utilizing a trend driving strategy based on the temperature trend when a working condition changes or reaches a preset control period, and further adjusting the feeding amount, so that the temperature of the system is stable, the sampling data is richer, and the input data of the fuzzy control is screened, so that the whole control process is more timely and more accurate and the system is more stable;
(2) the trend driving strategy based on the temperature trend considers two conditions of temperature trend change, no temperature trend change and gradual temperature deviation, so that the working condition evaluation is more accurate, the adjustment is carried out through fuzzy control, the influence of the working condition transition on the system is processed, and the control performance of the fuzzy control is better;
(3) the fuzzy control input quantity temperature deviation and temperature trend of the invention, rather than the increment of the temperature deviation and deviation, conforms to the dynamic characteristics of the roasting process, has smaller overshoot, faster regulation time, quicker regulation time when the working condition is changed, more stable temperature control and less influence by the change of the working condition;
(4) the method judges the abnormal value, so that the possibility of misjudgment is reduced;
(5) the method adopts fuzzy control, makes up the defects of a mathematical model to a certain extent, and is more visual and reasonable because the center-of-gravity method is adopted for defuzzification.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (9)
1. A fuzzy control method for a zinc smelting roasting process based on trend event driving is characterized by comprising the following steps:
s1, setting a temperature set value and a control period N;
s2, calculating temperature deviation and extracting temperature trend according to a set value and a real-time sampling temperature value of a sensor, screening the temperature trend and the temperature deviation when the working condition changes or reaches a preset control period based on an event-driven strategy of the temperature trend, and inputting the temperature trend and the temperature deviation to a fuzzy controller in time;
s3, setting rules based on expert experience, fuzzifying the input temperature deviation and temperature trend, after fuzzy reasoning, outputting an adjustment value of the feeding amount after defuzzification, and adjusting the feeding amount in the roasting process by an actuator;
the event-driven strategy based on the temperature trend described in step S2 includes the following steps:
s2.1, inputting a given control period N and a time window (t)1,ti) Temperature data of the inside;
s2.2, constructing a trend model;
s2.3, judging whether the temperature trend changes, if not, entering a step S2.4, and if so, entering a step S2.6;
s2.4, determining whether i +1 is equal to N, if so, proceeding to step S2.6;
s2.5, if the result of the S2.4 is negative, returning to the step S2.2;
s2.6, calculating a first derivative of the trend model at the current moment;
s2.7, sending the first derivative and the temperature deviation of the current moment to a fuzzy controller;
s2.8, judging whether to finish control, if so, pausing, otherwise, stopping from ti+1A new time window is started from time to time and the process proceeds to step S2.2.
2. The fuzzy control method for the zinc smelting and roasting process based on trend event driving as claimed in claim 1, wherein the step of judging whether the temperature trend changes or not in step S2.3 comprises the following steps:
s2.3.1 calculating the prediction error ei+1And a first class threshold th1,iDetermine whether | ei+1|≤th1,iIf yes, go to step S2.3.4;
s2.3.2, if the result of S2.3.1 is negative, then the current temperature y is judgedi+1Whether or not it is an abnormal value, i.e. pairTime window
All time instants t inj，i+1≤j≤i+lthCalculating its corresponding prediction error ejAnd a first class threshold th1,j-1And determining whether all | ej|≥th1,j-1If not, then yi+1Is an abnormal value, if so, yi+1Not an abnormal value,/thIs the minimum time window length;
s2.3.3, if yi+1If it is an abnormal value, go to step S2.3.4, if yi+1If not, the temperature trend has changed;
s2.3.4, calculating the cumulative error cusum (t)i+1) And a threshold th of the second kind2,iJudging whether | cusum (t)i+1)|≤th2,iIf yes, the temperature trend is not changed, and if no, the temperature trend is changed.
3. A trend event driven fuzzy control method for zinc smelting roasting process according to claim 1, characterized by the time window (t) in step S2.11,ti) Has a length of i, lthI is not less than i and not more than N, the control period N is the maximum length of the time window lthIs the minimum time window length.
4. The fuzzy control method for the zinc smelting roasting process based on trend event driving as claimed in claim 1, wherein the trend model in step S2.2 is:
wherein, Yi＝[y1,y2···yi]T，
Are the parameters of the model and are,
in order to estimate the deviation of the noise,
as output of the model, i.e. temperature prediction, ykAs measured temperature value, tkIs a time value, k is more than or equal to 1 and less than or equal to i and less than or equal to N, and m is more than or equal to 1 and less than or equal to 3.
5. The fuzzy control method for the zinc smelting roasting process based on trend event driving as claimed in claim 4, characterized in that the prediction error e isi+1Threshold th of the first class1,iCumulative error cusum (t)i+1) And a threshold th of the second kind2,iRespectively as follows:
cusum(ti+1)＝cusum(ti)+ei+1
ai＝((Ti TTi)-1(Ti)T)T[1(ti+1-t1)(ti+1-t1)2]T
bi+1＝[(ai+bi)T 1]T
wherein,
representing the t distribution, alpha is the confidence level of the t distribution,
for the estimation of the noise variance, ti+1、ti、tkAll have different time values of subscript, k is more than or equal to 1 and less than or equal to i and less than or equal to N, lth≤i≤N，1≤m≤3，lthIs the minimum time window length.
6. The fuzzy control method for the zinc smelting roasting process based on trend event driving as claimed in claim 1, wherein the first derivative of the trend model at the current moment in step S2.6 is:
wherein,
is the parameter of the model, and t is the current time.
7. The fuzzy control method for the zinc smelting roasting process based on trend event driving as claimed in claim 1, wherein the fuzzification in the step S3 is described as follows:
the fuzzification of the temperature deviation is described as: very high VH, slightly high LH, suitably Z, slightly low LL and very low VL; describing VH by adopting an S-type membership function, describing VL by using a Z-type membership function, and describing the rest by using a bell-shaped membership function;
the blurring of the temperature trend is described as: very large HP, slightly larger LP, stable S, slightly smaller LN, and very small HN; describing HP by adopting an S-type membership function, HN by using a Z-type membership function, and describing the others by using bell-shaped membership functions;
the fuzzification of the adjustment values for the feed rates is described as: positive big PB, positive PM, positive small PS, zero O, negative small NS, negative middle NM and negative big NB; and the PB is described by adopting an S-type membership function, the NB is described by a Z-type membership function, and the others are described by bell-shaped membership functions.
8. The trend event driven fuzzy control method for zinc smelting roasting process according to claim 7, wherein said Z-type membership function Zp(x) S type membership function Sp(x) Sum-bell membership function
Rule setting based on expert experience is as follows:
wherein, ap、bp、cp、dp、
V and v are parameter values of a membership function, and p is 1,2 and 3; q ═ 2, ·,2, p · 1,2,3 represent the membership function belonging to temperature deviation, temperature trend and feed rate, respectively; q represents different cases of bell-shaped functions.
9. The fuzzy control method for the zinc smelting and roasting process based on trend event driving as claimed in claim 1, wherein the anti-fuzzy method in step S3 is a gravity center method, and the expression is:
wherein u is*As an adjustment value of the feed quantity, i.e. the output value of the fuzzy control, ufThe adjustment value of the feeding amount corresponding to the membership function is obtained, n is the number of the membership functions of the adjustment value of the feeding amount, and n is 7.
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