CN108333947B - Single-integer-coefficient prediction function control parameter setting method based on intelligent optimization - Google Patents
Single-integer-coefficient prediction function control parameter setting method based on intelligent optimization Download PDFInfo
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Abstract
The invention discloses a monotone integer coefficient prediction function control parameter setting method based on intelligent optimization. The prediction function control is an effective method for controlling a large-inertia large-delay object, but the design process is complex, parameters are difficult to set, and engineering application is not facilitated. The invention comprises the following steps: a first-order inertia link and pure delay object in the industrial process control system are used as a prediction control model, a step function is adopted to obtain a prediction function optimal control law, and a single-step optimal control law is obtained by setting the prediction step length to 1; obtaining an optimal control law of a single adjustment coefficient prediction function by using a single adjustment coefficient method; and (3) adopting a setting method of a single adjustment coefficient, namely adopting a genetic algorithm to optimize and set. According to the method, the optimal adjustment coefficient can be obtained for any first-order inertia and pure delay object, and the control of the prediction function is ensured to have stronger robustness.
Description
Technical Field
The invention relates to the setting of a prediction function control parameter, in particular to a monotone integer coefficient prediction function control parameter setting method based on intelligent optimization.
Background
The controlled object in the industrial process control system is typically a first-order inertia plus delay object, and when the inertia time and the delay time of the object are large, the conventional PID control cannot achieve a satisfactory control effect.
The prediction function control is an effective method for controlling a large-inertia large-delay object, but the design process is complex, parameters are difficult to set, and engineering application is not facilitated.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects in the prior art and provide a method for setting the control parameters of the prediction function of the single adjustment coefficient based on intelligent optimization, and the optimal adjustment coefficient can be obtained for any first-order inertia delay object according to the method so as to ensure that the control of the prediction function has stronger robustness.
Therefore, the invention adopts the following technical scheme: the method for setting the control parameter of the single-integer coefficient prediction function based on intelligent optimization comprises the following steps:
1) a first-order inertia link and pure delay object in the industrial process control system are used as a prediction control model, a step function is adopted to obtain a prediction function optimal control law, and a single-step optimal control law is obtained by setting the prediction step length to 1;
2) obtaining an optimal control law of a single adjustment coefficient prediction function on the basis of the step 1) by using a single adjustment coefficient method;
3) and (3) adopting a setting method of a single adjustment coefficient, namely adopting a genetic algorithm to optimize and set.
As a supplement to the above technical solution, in step 2), a filtering link is added after the optimal control law of the monotonic coefficient prediction function; in the step 3), a setting method of the filtering inertia time constant is adopted, namely, a genetic algorithm is also adopted for optimizing and setting.
As a supplement to the above technical solution, in step 1), the model of the first-order inertia plus pure delay object is:
in the formula, KmGain for object, TmIs the object inertia time, TdA delay time for the object;
when a step function is used
u(k+i)＝u(k),i＝1,2...H-1，
In the formula, u (k + i) is the controlled variable of the controlled object at the k + i time, u (k) is the controlled variable of the controlled object at the k time, and H is the prediction time domain;
when T isdWhen the value is 0, discretizing the object, and calculating the partial derivative of the performance index to obtain the optimal control law of the prediction function as follows:
wherein c (k + H) is the set value of the controlled object at the k + H time, c (k) is the set value of the controlled object at the k time, y (k) is the output of the controlled object at the k time, ym(k) For the output of the prediction control model at the k-th time,
TRrepresenting the set-point filter time constant, TsRepresents a sampling period;
let H be 1, then the above formula is obtained:
wherein e (k) ═ c (k) -y (k),
TRrepresenting the set-point filter time constant, TsRepresents a sampling period;
in step 2), let
Obtaining the optimal control law of the single-integer coefficient prediction function for the single-integer coefficient m:
in addition to the above technical solution, when T isdAnd when the coefficient is not equal to 0, the single adjustment coefficient m is obtained by optimizing through a genetic algorithm.
And as a supplement to the technical scheme, carrying out simulation verification on the optimized control law of the single-integer coefficient prediction function after the determination.
As a supplement to the above technical solution, in genetic algorithm optimization, an integral of an absolute error is selected as a performance index, and an objective function thereof is:
as a supplement to the technical scheme, when the genetic algorithm is adopted for optimization, the optimization range of the single integer coefficient m and the inertia time T of the objectm(ii) related;
when 0 is present<Tm<At 100 hours, the optimizing range of the single adjustment coefficient m is set to be 0-3, and the filter inertia time constant TfThe optimization range of (1) to (10);
when 100 is finished<Tm<At 1000 deg.C, the optimizing range of the single adjustment coefficient m is set to 0-15, and the filter inertia time constant TfThe optimization range of (1) to (10);
when 1000<Tm<10000 and Tm/Td<At 40 hours, the optimizing range of the single adjustment coefficient m is set to be 0-20, and the filter inertia time constant TfThe optimization range of (1) to (10).
The invention has the following beneficial effects: the invention provides an effective parameter setting method for the control of a single-adjustment-coefficient prediction function, which can obtain the optimal adjustment coefficient for any first-order inertia delay object, ensures that the prediction function control has stronger robustness, and is simple in parameter setting and convenient for engineering application.
Drawings
FIG. 1 is a schematic diagram of the present invention using genetic algorithm to optimize the control of a monotonic coefficient prediction function (in the figure, sp is a set value, sp)rTo perturb the setpoint, pv is the controlled quantity, Gr(s) disturbance model, G(s) actual controlled object, Gm(s) is a predicted controlled model, m is a single adjustment coefficient, km is a predicted controlled model gain, TfS represents the laplacian in the frequency domain for the filtering inertia time constant).
FIG. 2 is a flow chart of the genetic algorithm of the present invention.
Fig. 3 is a graph of the control step response of the present invention based on the intelligent optimization-based monotonic coefficient prediction function when the inertia time of the object is greater than 2 times the delay time (in the figure, a is a set value, and B is a response curve).
Fig. 4 is a graph of the control step response of the present invention based on the intelligent optimization-based monotonic coefficient prediction function when the inertia time of the object is less than the delay time (in the figure, a is a set value, and B is a response curve).
Detailed Description
The invention is further described with reference to the drawings and the detailed description.
Intelligent optimization-based single integer coefficient prediction function control parameter setting method
The prediction controlled model controlled by the prediction function selects a first-order inertia link and a pure delay object, namely:
in the formula, KmGain for object, TmIs the object inertia time, TdFor the object delay time, s represents the laplacian in the frequency domain.
When a step function is used
u(k+i)＝u(k),i＝1,2...P-1 (2)
In the formula, u (k + i) is a control amount of the controlled object at the k + i-th time, u (k) is a control amount of the controlled object at the k-th time, and H is a prediction time domain.
When T isdWhen the value is 0, discretizing the object, and calculating the partial derivative of the performance index to obtain the optimal control law as follows:
wherein c (k + H) is the set value of the controlled object at the k + H time, c (k) is the set value of the controlled object at the k time, y (k) is the output of the controlled object at the k time, ym(k) The output of the controlled model is predicted for the kth moment,
TRrepresenting the set-point filter time constant, TsRepresents a sampling period;
when H is 1, formula (3) can be given as follows:
in formula (4), e (k) ═ c (k) -y (k),
TRrepresenting the set-point filter time constant, TsWhich represents the period of the sampling,
order to
For the adjustment coefficient m, a single adjustment coefficient prediction function control law is obtained:
when T isdWhen the coefficient is not equal to 0, the single adjustment coefficient m in the formula (5) can be obtained by optimizing a genetic algorithm; in order to obtain better control effect, the invention adds a filtering link on the basis of the formula (5), and sets the inertia time constant as TfThe parameters are also obtained by simultaneously optimizing by adopting a genetic algorithm, and a specific schematic diagram is shown in FIG. 2.
The principle of optimizing the control of the monotonic coefficient prediction function using a genetic algorithm is shown in FIG. 1.
According to fig. 2, the genetic algorithm is an iterative process based on a fitness function (objective function) to perform structural reorganization of individuals in a population by applying genetic manipulation to the individuals to achieve population optimization. In this process, the population (solution to the problem) is progressively optimized and approaches the optimal solution, one generation at a time.
The general steps of the genetic algorithm are as follows:
step1, selecting a target function, determining a variable definition domain and coding precision, and forming a coding scheme;
step2, randomly generating a population GA with the size of N (namely the population contains N individuals);
step3, carrying out cross operation on the individuals selected to enter the matching pool to form a new population GB;
step4, selecting individuals from the population GB with small probability to perform mutation operation to form a new population GC;
step5, calculating the fitness value of each individual;
step6, selecting N new individuals to form a new population GD according to the fitness probability;
step7, checking an ending condition, if the ending condition is met, ending the algorithm, and solving the individual with the highest fitness value in the current population; otherwise go to Step 3.
Selecting performance index and parameter range
The quality of the adjustment of the system is best when the error objective function is minimal, using a certain function of the difference between the desired function response and the actual system response as the objective function. In genetic algorithm optimization, the invention selects integral of absolute error as a performance index, and the target function is as follows:
the performance index is an easily applied index, and when the performance index is optimal, the system has proper damping and satisfactory transient response, so the system has faster output response and slightly larger overshoot.
The single adjustment coefficient prediction function control law (5) only contains one parameter m, and when genetic optimization is adopted, the optimization range of the parameter and the inertia time T in the model object (1)mRelated to when 0<Tm<When 100 hours are available, the optimization range of m is set to be 0-3, TfThe optimizing range of (1) to (10); when 100 is finished<Tm<Setting the optimization range of m to be 0-15 at 1000 hours; t isfThe optimizing range of (1) to (10); when 1000<Tm<10000 and Tm/Td<At 40 hours, the optimization range of m is set to be 0-20, TfThe optimization range of (1) to (10).
Third, simulation verification
The effectiveness of the intelligent optimization-based single integer coefficient prediction function control parameter setting method is verified through simulation experiments.
The actual controlled object is
The predictive controlled model is
The value range of m is set to be 0-15, the performance index shown in the formula (6) is adopted, the optimal m value obtained by genetic optimization is 5.78, and T isfThe value is 1.95 and the response curve is shown in figure 3.
The actual controlled object is
The predictive controlled model is
The value range of m is set to be 0-3, the performance index shown in the formula (6) is adopted, the optimal m value obtained by genetic optimization is 1.035, and T isfThe value was 3.02 and the response curve is shown in figure 4.
The above description is only for the purpose of illustrating the technical solutions of the present invention and not for the purpose of limiting the same, and other modifications or equivalent substitutions made by those skilled in the art to the technical solutions of the present invention should be covered within the scope of the claims of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (3)
1. The method for setting the control parameter of the single-integer-coefficient prediction function based on intelligent optimization is characterized by comprising the following steps of:
1) a first-order inertia link and pure delay object in the industrial process control system are used as a prediction control model, a step function is adopted to obtain a prediction function optimal control law, and a single-step optimal control law is obtained by setting the prediction step length to 1;
2) obtaining an optimal control law of a single adjustment coefficient prediction function on the basis of the step 1) by using a single adjustment coefficient method;
3) adopting a setting method of a single adjustment coefficient, namely adopting a genetic algorithm to optimize and set;
in step 2), a filtering link is added after the optimal control law of the single integer coefficient prediction function; in the step 3), a setting method of a filtering inertia time constant is adopted, namely, a genetic algorithm is also adopted for optimizing and setting;
in step 1), the prediction control model is:
in the formula, KmGain for object, TmIs the object inertia time, TdA delay time for the object;
when a step function is used
u(k+i)＝u(k),i＝1,2...H-1，
In the formula, u (k + i) is the controlled variable of the controlled object at the k + i time, u (k) is the controlled variable of the controlled object at the k time, and H is the prediction time domain;
when T isdWhen the value is 0, discretizing the object, and calculating the partial derivative of the performance index to obtain the optimal control law of the prediction function as follows:
wherein c (k + H) is the set value of the controlled object at the k + H time, c (k) is the set value of the controlled object at the k time, y (k) is the output of the controlled object at the k time, ym(k) For the output of the prediction control model at the k-th time,
TRrepresenting the set-point filter time constant, TsRepresents a sampling period;
let H be 1, then the above formula is obtained:
wherein e (k) ═ c (k) -y (k),
TRrepresenting the set-point filter time constant, TsRepresents a sampling period;
in step 2), let
Obtaining the optimal control law of the single-integer coefficient prediction function for the single-integer coefficient m:
in the genetic algorithm optimization, the integral of the absolute error is selected as a performance index, and the target function is as follows:
when the genetic algorithm is adopted for optimization, the optimization range of the single integer coefficient m and the inertia time T of the objectm(ii) related;
when 0 is present<Tm<At 100 hours, the optimizing range of the single adjustment coefficient m is set to be 0-3, and the filter inertia time constant TfThe optimization range of (1) to (10);
when 100 is finished<Tm<At 1000 deg.C, the optimizing range of the single adjustment coefficient m is set to 0-15, and the filter inertia time constant TfThe optimization range of (1) to (10);
when 1000<Tm<10000 and Tm/Td<At 40 hours, the optimizing range of the single adjustment coefficient m is set to be 0-20, and the filter inertia time constant TfThe optimization range of (1) to (10).
2. The intelligent optimization-based single integer coefficient prediction function control parameter tuning method of claim 1, wherein when T is greater than TdAnd when the coefficient is not equal to 0, the single adjustment coefficient m is obtained by optimizing through a genetic algorithm.
3. The intelligent optimization-based monotonic coefficient prediction function control parameter setting method according to claim 1, wherein simulation verification is performed on the set monotonic coefficient prediction function optimal control law.
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