CN117519067A - Multi-frame control performance evaluation method in continuous rolling process - Google Patents
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- G05B23/00—Testing or monitoring of control systems or parts thereof
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- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention provides a multi-rack control performance evaluation method in a continuous rolling process, and relates to the technical field of metal rolling. Firstly, constructing a multi-rack multi-variable time sequence in a continuous rolling process; processing the data by using a trend fluctuation analysis algorithm to respectively solve s values corresponding to the time sequence so as to solve a Hurst index of the control system; and evaluating the performance state of the controller at the moment by using the multi-machine frame control performance grade evaluation index. The performance evaluation method provided by the invention has low model dependence, can omit the calculation of the structure of the control system, does not need to calculate the time delay and complex correlation matrix of the process, has simple scheme implementation, can be directly realized on a computer through programming, is a multi-frame control performance evaluation method suitable for the continuous rolling process of actual complex and variable working conditions, and can be widely popularized to the continuous rolling production process of multiple frames.
Description
Technical Field
The invention relates to the technical field of metal rolling, in particular to a multi-rack control performance evaluation method in a continuous rolling process.
Background
In the field of steel rolling, the performance state of a control system can be mastered in time by evaluating the rolling control system. In the multi-frame continuous rolling process, strip steel tension forms interaction with strip steel thickness at each frame roll gap to form a Multiple-Input Multiple-Output (MIMO) system. In the actual strip steel production process, the working condition is complex, the continuous rolling control system operates for a long time, and the performance of the continuous rolling control system is reduced after the continuous rolling control system is put into production for a period of time. For a continuous rolling control system, the rolling control system is evaluated in time, and a control system loop with poor performance is found to be particularly important; in addition, the control system is adjusted in time, the state of the control system before and after the technical transformation is objectively evaluated, and it is important to ensure that the control system operates well after the transformation and to keep the high efficiency of the rolling process. Therefore, the performance evaluation of the multi-frame continuous rolling control system is an important field.
In order to evaluate the performance of various types of control systems, various researchers have proposed different research methods. The Chinese patent CN115933594A is a subspace projection-based MIMO control system performance evaluation method, which adopts a method for constructing a multivariate time delay matrix based on historical data to filter output data, and the performance index of a projection calculation system of the filtered data on the original data; the Chinese patent CN112099467A establishes a system model based on a minimum variance control method of performance evaluation of a water jet propulsion steering control system, adopts measured data to perform parameter identification on the system, and determines delay time and the like. Further evaluating the performance of the controller by utilizing the ratio of the minimum variance of the system model to the output variance of the actual control system; the Chinese patent CN105068530A is a performance evaluation method and an evaluation system of a multivariable multi-time-varying disturbance system, wherein the performance of the system is evaluated and controlled by calculating a multivariable disturbance transfer function, carrying out a lost-graph decomposition on the multivariable disturbance transfer function to obtain a lost-graph equation, substituting the lost-graph equation into the transfer function, and obtaining the minimum variance according to a minimum variance control criterion; chinese patent CN111624978A is a method and a device for evaluating the performance of a water supply flow controller, wherein the method and the device acquire the delay time of the water supply flow controller, acquire the reference performance of the controller, and further acquire the performance evaluation result of the water supply flow controller according to the given evaluation index; the performance evaluation method and device of a control system for time sequence output in China patent CN106774267A acquires stable time sequence, creates a fitting model, and obtains a control system performance index based on the fitting model and control system time delay; the performance evaluation method of the automatic driving automobile course tracking controller of the Chinese patent CN108268022A utilizes actual course measurement data of an automobile to obtain tracking loop delay, calculates expected performance of the course tracking controller according to automobile design experience, and divides the expected performance by the actual performance to obtain a performance evaluation index.
The performance evaluation of the control system is referred to by a definite evaluation reference index, and the performance evaluation of the control system is performed based on the reference index. As can be seen from the above study, the conventional control system performance evaluation method generally needs to be based on the structure of the control system itself, so that more Minimum Variance (MV) levels are currently used in the control theory field, and in the solving process, the calculation process time delay is needed, and for the MIMO system, the correlation matrix is also needed to be obtained. However, due to the limiting conditions in the practical industry, the structure of the control system is difficult to consider, the delay time and the correlation matrix are difficult to accurately calculate, the evaluation reference index of the traditional control system is difficult to accurately calculate, and a multi-stand control performance evaluation method suitable for the continuous rolling process of actual complex and variable working conditions is lacking.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a multi-stand control performance evaluation method in the continuous rolling process aiming at the defects of the prior art, and the evaluation standard is more accurately obtained based on the Hurst index and the control system structure.
In order to solve the technical problems, the invention adopts the following technical scheme:
a multi-rack control performance evaluation method in a continuous rolling process comprises the steps of utilizing output data of a multi-rack control system in the rolling process to construct a multi-rack multi-variable time sequence in the continuous rolling process; processing the data according to a trend-removing fluctuation analysis algorithm to respectively solve an intermediate value s corresponding to the time sequence, and further solving a Hurst index of the control system; and determining a control system evaluation index according to the range of the Hurst index, and evaluating the performance of the multi-stand control system in the continuous rolling process.
Further, the method comprises the steps of:
step 1: constructing a multi-frame multi-variable time sequence in the continuous rolling process;
step 2: respectively solving slope k values of a fitting curve by using a least square method in the double logarithmic graph solved by the trend fluctuation algorithm of each group of time sequences;
step 3: determining an intermediate value s by utilizing the slope k value, and solving a Hurst index of the control system;
step 4: defining intervals of Hurst indexes, and respectively solving confidence upper bounds H of Hurst indexes upper And the confidence lower bound H of Hurst index lower Calculating the reliability y of the Hurst index;
step 5: defining an evaluation index of a multi-machine frame control system;
step 6: and evaluating the control performance of the multiple machine frames in the actual production process of continuous rolling by using the evaluation index of the control system of the multiple machine frames.
Further, the specific method of the step 1 is as follows:
step 1.1: collecting strip steel production data in a multi-frame rolling process;
step 1.2: the collected strip steel production data are respectively manufactured into time sequences with fixed sampling frequency and fixed data point number.
Further, the specific method of the step 2 is as follows:
step 2.1: determining an autocorrelation sequence Y (j):
wherein y (i) is the original time series,y (j) is its autocorrelation sequence, which is its average value; n is the number of time sequences;
step 2.2: dividing the autocorrelation sequence Y (j) into d windows with length of n, and the m-th window is Y m (i) M=1, …, d, a first order least squares fit is performed on the data within each window, fitting the trend of the time series accumulation value for each window as shown in the following formula:
y m (i)＝a m i+b m
wherein y is m (i) Fitting a curve for least squares within the window; a, a m 、b m The slope and intercept of the least squares fit curve are respectively;
step 2.3: calculating the mean variance of each window after trending
Wherein Y is m (i) For the time series of the original window data,fitting a curve for least squares in a corresponding window, wherein n is the window length;
step 2.4: root mean square fluctuations are calculated as a function F (n) when the window length is n:
repeating the steps 2.1-2.4 under the condition of changing the window length n, and obtaining a plurality of F (n) values;
step 2.5: drawing log F (n) and log n points on a double-log graph, fitting by first-order least square, and calculating the slope of a curve, wherein the slope k of the straight line is obtained by the following steps:
further, the specific method of the step 3 is as follows:
step 3.1: obtaining an intermediate value s;
since the time series of the control system is smooth and bounded, the k value falls within the range 0,1]In which an intermediate value s is defined, and p time series s values are obtained as follows 1 ,s 2 ,…,s p ：
Step 3.2: let s be 1 ＞s 2 ＞…＞s p The Hurst index defining the MIMO control system is as follows:
wherein H is the Hurst index of the control system.
Further, the upper confidence bound H of the Hurst index in the step 4 upper And confidence lower bound H lower The solution is respectively carried out according to the following two modes:
the Hurst index confidence level y is calculated as follows:
substituting H into the formula to obtain a y value, and if the y value is in the range of [0.5,1], indicating that the H value is credible, wherein the reliability is higher when the y value is closer to 1.
Further, the evaluation index of the multi-rack control system in step 5 determines the corresponding performance level of the control system according to the range of the Hurst index H: the range of the Hurst index H is [0.9,1], and the corresponding performance grade is excellent; the range of the Hurst index H is [0.6,0.9 ], and the corresponding performance grade is good; the range of Hurst index H is [0.4,0.6 ], corresponding performance grades are medium; the Hurst index H ranges from [0,0.4 ], with a corresponding performance rating of poor.
The beneficial effects of adopting above-mentioned technical scheme to produce lie in: according to the multi-rack control performance evaluation method in the continuous rolling process, the Hurst index is calculated through the thickness tension time series data output in the multi-rack continuous rolling process, the mathematical model for solving the controlled object is avoided, the calculated amount is reduced, the uncertainty in the calculating process is reduced, and the accuracy is higher. The performance evaluation method provided by the invention has low model dependence, can omit the calculation of the structure of the control system, does not need to calculate the time delay and complex correlation matrix of the process, has simple scheme implementation, can be directly realized on a computer through programming, is a multi-frame control performance evaluation method suitable for the continuous rolling process of actual complex and variable working conditions, and can be widely popularized to the continuous rolling production process of multiple frames.
Drawings
Fig. 1 is a flowchart of a method for evaluating multi-stand control performance in a continuous rolling process according to an embodiment of the present invention;
fig. 2 is a thickness data chart of the third stand output in the five-stand cold continuous rolling according to the embodiment of the present invention;
fig. 3 is a diagram of tension data outputted by a third stand in cold continuous rolling of five stands according to an embodiment of the present invention;
FIG. 4 is a thickness autocorrelation sequence provided by an embodiment of the present invention;
FIG. 5 is a diagram of a tension autocorrelation sequence provided by an embodiment of the present invention;
FIG. 6 is a fitting condition of data in each window when a thickness autocorrelation sequence diagram provided by an embodiment of the present invention is divided into 5 windows;
FIG. 7 is a fitting condition of data in each window when a tension autocorrelation sequence diagram provided by an embodiment of the present invention is divided into 5 windows;
FIG. 8 is a log-log graph corresponding to thickness data provided by an embodiment of the present invention;
fig. 9 is a log-log graph corresponding to tension data provided in an embodiment of the present invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
In the embodiment, a five-stand cold continuous rolling model predictive control (Model Predictive Control, MPC) system is used for explanation, data in the cold continuous rolling production process is adopted, a roll gap with a mean value of 0 and a variance of 0.01 and rolling speed random disturbance are given to a third stand, and performance of the third stand control system in the five-stand cold continuous rolling is evaluated.
As shown in fig. 1, the method of this embodiment is as follows.
Step 1: constructing a multi-frame multi-variable time sequence in the continuous rolling process.
Step 1.1: and collecting strip steel production data in the multi-frame rolling process, wherein the strip steel production data comprises strip steel inlet and outlet thickness and tension data of a third frame.
Step 1.2: the collected strip steel production data were made into 5000 data points, respectively, with a time series of 0.02s time interval, as shown in fig. 2 and 3.
Step 2: the corresponding k values of the two time sequences are respectively solved by adopting a trend removal fluctuation analysis algorithm, and the specific steps are as follows:
step 2.1: an autocorrelation sequence Y (j) is determined. The autocorrelation sequence is obtained by removing the average value from the noise term and cumulatively summing, mapping the given time sequence to its autocorrelation sequence as shown in fig. 4 and 5.
Step 2.2: a least squares curve fit is used. The autocorrelation sequence Y (j) is divided into 5 windows with equal length, first-order least square fitting is carried out on the data in each window, the local trend of the curve in each window is fitted, and the fitted curve is shown in fig. 6 and 7.
Step 2.3: calculating the mean variance of each window after trendingTrending the data in each window, removing the local trend of each interval, and obtaining the average variance in five windows in the sum of the fluctuation of the thickness of the strip steel according to a formula, wherein the average variance is respectively as follows:
f 1 2 ＝0.0159，
step 2.4: root mean square fluctuation was calculated as a value at a window length of 1000:
repeating the steps 2.1-2.4 under the condition of changing the window length n, wherein the value of n is an integer between 10 and 1250.
Step 2.5: drawing log F (n) and log points on a double logarithmic graph, fitting by first-order least squares, and calculating the slope of the curve, as shown in FIG. 8 and FIG. 9, to obtain Hurst indexes of thickness and tension, k respectively 1 ＝0.455、k 2 ＝0.468。
Step 3: and (3) calculating the Hurst index of the MPC control system of the third stand of the cold continuous rolling by using the k values of the time sequence:
s 1 ＝0.455、s 2 ＝0.468
step 4: solving the interval of the Hurst index and the confidence upper bound H of the Hurst index upper And confidence lower bound H lower The confidence level y is solved as follows:
and if the y value is 1, the H value has higher credibility.
Step 5: and comparing the performance grade evaluation indexes of the multi-rack control, wherein H=0.919 is between intervals [0.9,1], and evaluating that the performance grade of the current third rack control system is excellent.
The embodiment provides a multi-rack control performance evaluation method in a continuous rolling process, which has low model dependence, simple scheme implementation, no need of solving complex incidence matrixes, no need of solving time delay and more accurate solving.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions, which are defined by the scope of the appended claims.
Claims (7)
1. A multi-frame control performance evaluation method in a continuous rolling process is characterized in that: the method utilizes output data of a multi-rack control system in the rolling process to construct a multi-rack multivariable time sequence in the continuous rolling process; processing the data according to a trend-removing fluctuation analysis algorithm to respectively solve an intermediate value s corresponding to the time sequence, and further solving a Hurst index of the control system; and determining a control system evaluation index according to the range of the Hurst index, and evaluating the performance of the multi-stand control system in the continuous rolling process.
2. The continuous rolling process multi-stand control performance evaluation method according to claim 1, characterized in that: the method comprises the following steps:
step 1: constructing a multi-frame multi-variable time sequence in the continuous rolling process;
step 2: respectively solving slope k values of a fitting curve by using a least square method in the double logarithmic graph solved by the trend fluctuation algorithm of each group of time sequences;
step 3: determining an intermediate value s by utilizing slope k values corresponding to each group of time sequences, and solving a Hurst index of the control system;
step 4: defining intervals of Hurst indexes, and respectively solving confidence upper bounds H of Hurst indexes upper And the confidence lower bound H of Hurst index lower Calculating the reliability y of the Hurst index;
step 5: defining an evaluation index of a multi-machine frame control system;
step 6: and evaluating the control performance of the multiple machine frames in the actual production process of continuous rolling by using the evaluation index of the control system of the multiple machine frames.
3. The continuous rolling process multi-stand control performance evaluation method according to claim 2, characterized in that: the specific method of the step 1 is as follows:
step 1.1: collecting strip steel production data in a multi-frame rolling process;
step 1.2: the collected strip steel production data are respectively manufactured into time sequences with fixed sampling frequency and fixed data point number.
4. A method for evaluating multi-stand control performance in a continuous rolling process according to claim 3, wherein: the specific method of the step 2 is as follows:
step 2.1: determining an autocorrelation sequence Y (j):
wherein the method comprises the steps ofY (i) is the original time series,y (j) is its autocorrelation sequence, which is its average value; n is the number of time sequences;
step 2.2: dividing the autocorrelation sequence Y (j) into d windows with length of n, and the m-th window is Y m (i) M=1, the combination of the first and second components, d, a first order least squares fit is performed on the data within each window, fitting the trend of the time series accumulation value of each window as shown in the following formula:
y m (i)＝a m i+b m
wherein y is m (i) Fitting a curve for least squares within the window; a, a m 、b m The slope and intercept of the least squares fit curve are respectively;
step 2.3: calculating the mean variance of each window after trending
Wherein Y is m (i) For the time series of the original window data,fitting a curve for least squares in a corresponding window, wherein n is the window length;
step 2.4: root mean square fluctuations are calculated as a function F (n) when the window length is n:
repeating the steps 2.1-2.4 under the condition of changing the window length n, and obtaining a plurality of F (n) values;
step 2.5: drawing log F (n) and log points on a double-log graph, fitting by first-order least square, and calculating the slope of a curve, wherein the slope k of the line is obtained by the following steps:
5. the method for evaluating the multi-stand control performance in the continuous rolling process according to claim 4, wherein: the specific method of the step 3 is as follows:
step 3.1: obtaining an intermediate value s;
since the time series of the control system is smooth and bounded, the k value falls within the range 0,1]In which an intermediate value s is defined, and p time series s values are obtained as follows 1 ,s 2 ,…,s p ：
Step 3.2: let s be 1 ＞s 2 ＞…＞s p The Hurst index defining the MIMO control system is as follows:
wherein H is the Hurst index of the control system.
6. The continuous rolling process multi-stand control performance evaluation method according to claim 5, characterized in that: the upper confidence bound H of the Hurst index in the step 4 upper And confidence lower bound H lower The solution is respectively carried out according to the following two modes:
the Hurst index confidence level y is calculated as follows:
substituting H into the formula to obtain a y value, and if the y value is in the range of [0.5,1], indicating that the H value is credible, wherein the reliability is higher when the y value is closer to 1.
7. The method for evaluating the multi-stand control performance of the continuous rolling process according to claim 6, wherein: and (3) determining the corresponding performance level of the control system according to the range of the Hurst index H by using the evaluation index of the multi-machine frame control system in the step (5): the range of the Hurst index H is [0.9,1], and the corresponding performance grade is excellent; the range of the Hurst index H is [0.6,0.9 ], and the corresponding performance grade is good; the range of Hurst index H is [0.4,0.6 ], corresponding performance grades are medium; the Hurst index H ranges from [0,0.4 ], with a corresponding performance rating of poor.
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| 王印松;李士哲;张涛;田靖雨;: "基于协方差指标的火电机组负荷控制系统性能评价", 热力发电, no. 04, 25 April 2015 (2015-04-25) * |
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