CN107942658A - A kind of large circle machine swing circle Forecasting Methodology and system using sef-adapting filter - Google Patents
A kind of large circle machine swing circle Forecasting Methodology and system using sef-adapting filter Download PDFInfo
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Abstract
The present invention relates to a kind of large circle machine swing circle Forecasting Methodology and system using sef-adapting filter, for the unstable phenomenon of swing circle present in textile industry large circle machine actual production, the wave filter combines the advantage of RLS period forecastings algorithm and single step period forecasting algorithm, it can efficiently and accurately predict the swing circle in large circle machine actual production, the present invention combines closely with defect detection vision system, predetermined period can fast and accurately be obtained by the real-time analysis and the processing of adaptive-filtering of rs 232 serial interface signal, predetermined period can be used for the rotary speed for calculating large circle machine, positioning defect detection vision system needs the picture position captured, and position of the positioning fault in cloth by the gross, to realize that efficient embryo cloth detecting system is laid a good foundation.The present invention fast and accurately can be predicted the large circle machine cycle, reduce the accurate of defect detection vision system and capture difficulty, improve embryo cloth fault positional accuracy.
Description
Technical Field
The invention relates to the field of production measurement and control of circular knitting machines in the textile industry, in particular to a circular knitting machine rotation period prediction method and a circular knitting machine rotation period prediction system adopting an adaptive filter, which are applied to a defect detection system with complex production environment and unstable circular knitting machine rotation state.
Background
In the traditional production of a circular knitting machine, the defect cloth in production is often monitored by adopting a manual detection mode, but the traditional method is easily interfered by the subjectiveness of detection workers and has low efficiency. Along with the popularization of industrial video monitoring and the development of video image processing technology, more and more researches on intelligent defect detection based on machine vision appear in the visual field of people. The conventional intelligent defect detection research is performed around improving the accuracy of a defect detection algorithm and reducing the time complexity of the algorithm, but the application of the algorithm to the actual industrial production is still limited by the production environment. In a common defect detection system, video image information is acquired in real time by adopting frame-by-frame analysis and timing snapshot, and the image information is rapidly analyzed to obtain an analysis result. The defect information of the gray cloth has low occurrence frequency, a large amount of image information can be wasted by adopting a frame-by-frame analysis method, and due to the influence of the actual production environment of the circular knitting machine and the limitation of the long-term production service life condition of the circular knitting machine, the setting of proper time by adopting a timing snapshot method becomes extremely difficult.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method and a system for predicting the rotation period of a circular knitting machine by adopting a self-adaptive filter, which have the advantages of high prediction speed, high prediction accuracy, strong field adaptability and self-learning capability; the actual period is obtained by analyzing the specific serial port signal, and the actual period is used as a learning object of the self-adaptive filter, so that the filter parameters which accord with the current operation rule of the circular knitting machine are self-learned, and the rotation period of the circular knitting machine can be effectively predicted according to different working environments of the circular knitting machine.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a method for predicting the rotation period of a circular knitting machine by adopting an adaptive filter comprises the following steps:
receiving serial port signals sent by the proximity switch in real time, and obtaining the actual rotation period RC of the large circular knitting machine by calculating the time difference of the two latest effective serial port signals i (ii) a Wherein i represents the rotation times of the circular knitting machine, and the iteration cycle times are represented in a filter;
using RLS periodic predictionThe measurement algorithm is used for filter parameter learning and calculating the prediction period PC of the next turn i ；
Judging whether the current filter parameter reaches a learning standard condition;
if the current filter parameter does not reach the learning standard condition, the single step period prediction algorithm is adopted to obtain the prediction period PC of the next turn i And jumping out of the filter iteration process, and waiting for the (i + 1) th iteration;
if the current filter parameter reaches the learning standard condition, the RLS periodic prediction algorithm is adopted to calculate to obtain the PC i Predicted period PC as next revolution i And jumping out of the filter iteration process, and waiting for the (i + 1) th iteration.
Preferably, the RLS periodic prediction algorithm is used for filter parameter learning, and the learning process is as follows:
a. calculating the periodic error e i The following are:
e i ＝RC i -PC i-1 (1)
wherein e is i The periodic error of the ith iteration is represented by an error feedback signal of an RLS filter, and the periodic error of the ith iteration is represented by two parameters RC i And a PC i-1 Subtracting to obtain the result; RC (resistor-capacitor) capacitor i Expressed as the actual period of the ith iteration, for calculating the period error e as the ith iteration i A first parameter of (a); PC (personal computer) i-1 Outputting the obtained prediction period for the i-1 th iteration to calculate the period error e as the i-th iteration i A second parameter of (1);
b. computing a gain vector k i (m), as follows:
wherein k is i (m) represents the gain vector of the ith iteration of size m x 1; λ is a constant, representing a forgetting factor; m is a constant and represents the RLS adaptive filter order; p i-1 (m) an inverse matrix of the i-1 st iteration of size m x m; u. of i-1 (m) an input matrix representing the i-1 th iteration of size m x 1;
c. computing an inverse matrix P of an iteration i (m) is as follows
Wherein, P i (m) an inverse matrix of the ith iteration of size m x m; t represents a matrix transposition;
d. calculating a weight vector w i (m) as follows:
w i (m)＝w i-1 (m)+k i (m)e i (4)
wherein w i (m) represents a weight vector representing the i-th iteration of size m x 1; w is a i-1 (m) representing vector values of the weight vectors representing the i-1 th iteration of size m x 1;
e. updating the input matrix u i (m), as follows:
u i (m)＝[RC i ；u i-1 (1:m-1)] (5)
wherein u is i (m) represents the input matrix of the ith iteration of size m x 1 for the RC i Performing m-order sampling constitution, and updating u for each iteration i (m) the update mode is an in-and-out queue mode, i.e. a new RC is input at the head of the queue for each iteration i And the u vector values from 1 to m-1 of the i-1 iteration are pushed to the tail of the queue, and the tail of the queue u i Value of (m) is represented by u i (m-1) covering;
f. calculating the predicted period PC of the next revolution i The following are:
PC i ＝w i T (m)u i (m) (6)
wherein, PC i The prediction period obtained for the output of the ith iteration, i.e. the output of the RLS filter, is used as the period error e for the i +1 th iteration i+1 The second parameter of (2) has the value of the weight vector w of the ith iteration i Transposed vector of (m) and input vector u of order m i (m) obtaining the vector product。
Preferably, in the single-step cycle prediction algorithm, the actual rotation cycle RC is determined i Set to the prediction period PC of the next revolution i 。
Preferably, the judging whether the current filter parameter meets the learning criterion condition includes:
if the error value | e calculated by the RLS period prediction algorithm is adopted in the continuous t periods i I and the single step period prediction error value e i When the proportion theta of the difference eta of negative values obtained by subtracting the' I is larger than or equal to a preset value, the learning standard condition is met; wherein e is i '＝RC i -RC i-1 。
Preferably, t is equal to 10 and the preset value is equal to 60%. Namely, in 10 continuous periods, if the eta value is measured for 6 times and is a negative value, the current filter parameter is judged to reach the learning standard condition.
Preferably, the method for judging the validity of the serial port signal comprises the following steps: and judging whether the value of the serial port signal designated field is equal to a set value or not, and if so, judging that the received serial port signal is valid.
The invention also provides a system for predicting the rotation period of the circular knitting machine by adopting the adaptive filter, and the method for predicting the rotation period of the circular knitting machine based on the adaptive filter comprises the following steps: the device comprises a lower computer, an upper computer and a serial port transmission line connected between the lower computer and the upper computer; the lower computer comprises a circular knitting machine and a proximity switch; the proximity switch comprises an infrared probe fixed on the inner wall of a machine table of the circular knitting machine and a circumferential outer wall nut capable of performing circumferential motion along with the circular knitting machine; and when the circular knitting machine works for each circle, the proximity switch generates a serial port signal and sends the serial port signal to the upper computer through the serial port transmission line, and the upper computer performs rotation period prediction according to the circular knitting machine rotation period prediction method.
The invention has the following beneficial effects:
(1) In order to accurately snapshot images by adopting a timing snapshot method in the actual production environment of a circular knitting machine, not only the environmental influence of complex actual production is overcome, but also the influence of the service life of the circular knitting machine is overcome, therefore, the invention provides a circular knitting machine rotation period prediction method adopting an adaptive filter, and the adaptive filter suitable for the rotation period prediction of the industrial production of the circular knitting machine is realized by combining an RLS (least square method) period prediction algorithm and a single-step period prediction algorithm; experiments prove that the method for predicting the rotation period of the circular knitting machine running by the adaptive filter can quickly and accurately analyze and process the characteristic serial port signals of the circular knitting machine and predict the rotation period of the circular knitting machine, and can be suitable for different working environments of the circular knitting machine, and if abnormal serial port signals are detected, the signals are not used as learning objects of the adaptive filter;
(2) The RLS period prediction algorithm has certain self-learning capacity, can achieve certain accuracy after the actual rotation period of the circular knitting machine is learned and trained for a period of time, and can keep trained parameters to predict the subsequent rotation period after the actual rotation period of the circular knitting machine is learned and trained, but the RLS period prediction algorithm has certain learning buffer period, and the period predicted by the RLS period prediction algorithm is far away from the actual period during the period, so that the RLS period prediction algorithm cannot be directly used for the timing snapshot operation of a defect detection vision system; the single-step period prediction algorithm assumes that the predicted rotation period of the circular knitting machine is approximately equal to the actual rotation period of the previous rotation, and because the number of times of fluctuation changes of the previous rotation and the next rotation in the actual work of the circular knitting machine is small, the assumed conditions are basically met, the single-step period prediction algorithm can be directly used for the timing snapshot operation of the defect detection visual system, but the algorithm has certain assumed conditions, has no self-adaptive capacity, has no strong period prediction accuracy, and is not suitable for the complex production environment of the circular knitting machine because the RLS period prediction algorithm is not strong. Based on the method, the method and the system for predicting the rotation period of the circular knitting machine by adopting the self-adaptive filter are provided by combining the RLS period prediction algorithm and the single-step period prediction algorithm, have self-adaptive capacity and are suitable for the complex working environment of the circular knitting machine;
(3) The invention is closely combined with a defect detection visual system, can quickly and accurately acquire a prediction period through the real-time analysis of serial port signals and the processing of self-adaptive filtering, and the prediction period can be used for calculating the rotation speed of a circular knitting machine, positioning the image position of the defect detection visual system to be snapshoted and positioning the positions of defects in the whole batch of cloth, thereby laying a foundation for realizing an efficient gray cloth detection system.
The present invention will be described in further detail with reference to the drawings and embodiments, but the method and system for predicting the rotation period of a circular knitting machine using an adaptive filter according to the present invention are not limited to the embodiments.
Drawings
FIG. 1 is a schematic diagram of the operation of the adaptive filter RLS period prediction algorithm of the present invention;
FIG. 2 is a block diagram of an update input vector u of the present invention i (m) a queue pattern diagram;
FIG. 3 is a core diagram of the RLS cycle prediction algorithm of the present invention;
FIG. 4 is a block diagram of a circular machine spin cycle prediction system employing an adaptive filter in accordance with the present invention;
FIG. 5 is a sample graph of data for an actual cycle of the present invention;
FIG. 6 is a diagram of the adaptive filter operating mechanism of the present invention;
FIG. 7 is a diagram of the filter error weight (Power) distribution of the present invention;
FIG. 8 is a single step PC error plot of the filter of the present invention;
FIG. 9 is a 16 th order PC error plot for the filter of the present invention;
FIG. 10 is the difference η between the absolute value of the 16 th order error of the cycle (70-79) and the absolute value of the single step error of the present invention;
FIG. 11 is the difference η between the absolute value of the 16 th order error and the absolute value of the single step error of the present invention;
FIG. 12 is the statistics of the difference η between the absolute value of the 16 th order error and the absolute value of the single step error of the present invention.
Detailed Description
Specific implementations of the present invention are further described in detail below with reference to the accompanying drawings.
Referring to fig. 1, 2 and 3, the working principle of the RLS period prediction algorithm in the adaptive filter is described as follows.
As shown with reference to figure 1 of the drawings,to (5) forming a RLS period prediction iterative process according toThe sequence of the processes to (5) proceeds with the filter input being the actual period RC i The output is the predicted period PC i 。
- (1) - (2) process for completing ith iteration to solve periodic error e i Task of (2), input signal RC of process (1) i Expressed as the actual period and process of the circular knitting machineInput signal PC i Expressed as the prediction period, RC, of the previous iteration output i And PC i The difference is taken to obtain a period error e i 。
(4) The process completes the iteration of the input vector u i (m) update, the update mode adopts a queue mode, as shown in fig. 2, and a new RC is input into the filter every time the filter is iterated i RC at the ith iteration i Will be as u i The head (front) element of the (m) enters the queue, the m-1 time queue entering elements before the ith iteration move to the tail (rear) of the queue in sequence according to the queue order, and u i (m) will be updated continuously as the iteration progresses.
(3) And (5) the process constructs the core content of the RLS operation, i.e. solvingWeight vector w i (m) and calculating the prediction period PC i . (3) The process completes the iteration weight vector w i (m) update, w i (m) is the key to achieving periodic prediction of the RLS adaptive filter, which can be based on the input matrix u i (m) and actual period data RC i Continuously correcting w by self-learning i (m) weight vectors up to w i (m) tends to be stable; (5) the process completes the prediction period PC of the iteration i Output, its value is weight vector w i Transposed vector of (m) and input vector u i And (m) accumulating the vector products to obtain the final product.
Referring to fig. 3, "1", "2" and "3" in the figure respectively represent gain vectors k i (m), inverse matrix P i (m) sum of input vector u i (m) an update process. K according to RLS period prediction algorithm formula (2) i (m) the update requires the input vector u to be used before the update of the current iteration i-1 (m) and inverse matrix P i-1 (m), and a forgetting factor λ; p according to RLS period prediction algorithm formula (3) i (m) the update requires the inverse matrix P used before the current iterative update i-1 (m) sum of input vector u i-1 (m), updated gain vector k i (m), and a forgetting factor λ; according to RLS period prediction algorithm formula (4), w i (m) updating requires the weight vector w before the current iteration updating i-1 (m) updated gain vector k i (m) andperiodic error e calculated by the processes (1) to (2) i ；u i (m) the update then occurs at w i (m) after the update; and finally, according to the RLS period prediction algorithm formula (6), completing the process (5) to calculate to obtain the prediction period PC of the iteration i 。
The detailed operation of the prediction system for the rotation period of the circular knitting machine adopting the adaptive filter is described as follows.
Referring to fig. 4, the circle machine period prediction system is composed of an upper computer, a lower computer and a connecting line between the upper computer and the lower computer. Specifically, the lower computer comprises a circular knitting machine and a proximity switch, namely a hardware model of the circular knitting machine; the upper computer comprises a proximity switch serial port signal analysis module and an adaptive filter module, namely a software model of the circular knitting machine. In the lower computer, "1" and "2" form a proximity switch, "1" is the infrared probe, is fixed at the big circular knitting machine board inner wall, "2" is the circumference outer wall nut that can block infrared light and be fixed and can carry out circular motion along with big circular knitting machine.
Specifically, the proximity switch signal is a 9-byte serial port signal, the protocol of the proximity switch signal is shown in table 1, and byte information adopts a 16-system format, namely a 00-FF pattern. The 'header' adopts unified '005A' information, so that the uniformity and the safety of equipment are ensured; the user of the 'equipment address' can customize information according to the number of the circular knitting machines, so that unified equipment management is facilitated; the 'command' uniformly adopts '01' information, mainly for distinguishing command information except serial port signals; the "reserved" 4 bytes are temporarily left untreated; the checksum is the sum of the previous 8 bytes of information and can be used for the upper computer to verify the accuracy of the proximity switch signal.
TABLE 1
Every work circle of big circular knitting machine, proximity switch all can produce a serial signals to upload to the host computer through RS485/RS232 agreement, the host computer passes through USB commentaries on classics serial ports and receives serial signals, and then accomplishes serial signals analysis on the host computer, and acquires actual cycle RC data set process.
The detailed data acquisition process of the system is as follows.
Step one, generating and uploading a proximity switch serial port signal. When the '2' approaches to the '1', an interrupt pulse signal can be generated, the signal can generate a 9-byte proximity switch signal in the lower computer, and the serial port signal can be uploaded to the upper computer through a serial port transmission line (RS 485/RS 232) and a USB (universal serial bus) transfer serial port;
and step two, analyzing the proximity switch serial port signal. After the upper computer receives the serial port signal, the serial port signal is checked and analyzed, and whether the current serial port signal is correct or not is judged, so that the upper computer is used for eliminating wrong serial port signals uploaded by the lower computer due to interference of environmental factors. Under the condition that the proximity switches can work normally, when the upper computer receives a second proximity switch signal, the circular knitting machine is considered to rotate for a circle, at the moment, the upper computer can calculate the rotation period of one rotation of the circular knitting machine according to the time of the received two proximity switch signals, the rotation period is marked as RC, the current second proximity switch signal is used as a first proximity switch signal of the next rotation, and the RC of each rotation can be calculated in sequence according to the process;
and step three, collecting and manufacturing an RC data set. According to the first step and the second step, the RC collection is carried out on the textile production operation in the real circular knitting machine environment of the textile factory by the design software tool, due to the fact that in the real textile environment, a plurality of sudden factors exist in the textile production operation, such as broken needles, broken threads, manual shutdown of workers and the like can cause unreliable influences on RC data sets, and absolute influences are caused on the size of the RC data sets, the experimental selection is carried out on the experimental circular knitting machine for multiple times, and then the RC data set with the size of 4578 is selected through comparative analysis, and the comparison and analysis are shown in figure 5.
In order to construct a working mechanism of a circular knitting machine adaptive filter, the following two part of experiments are designed in the embodiment, namely an experiment for finding the optimal order m of an RLS period prediction algorithm; and experiment II, comparing the RLS period prediction algorithm for confirming the order with the single-step period prediction algorithm. The experimental data were actual period RC data samples of size 4578.
The RLS period prediction algorithm needs to be applied to P 0 (m) an initial value is given, and the value range of the RLS forgetting factor is 0.9 according to the literature<λ&And (1) repeatedly testing, setting lambda =0.98 for any one group of RC data 0 (m) = (1/λ) × eye, eye represents sizeAn identity matrix of m x m.
In the first experiment, in order to find the optimal order of the RLS period prediction algorithm, an experiment that the RLS error weight (Power) changes along with the order N is designed. Error weight (Power) takes the periodic error e i Is expressed as a square number of (i.e. Power = e) i *e i . The order N ranges from 2 to 30. The experimental contents are that 4578 iterations of RLS period prediction algorithm are designed, and along with the iteration, e i And (4) the error weight tends to be in a stable state, then the Power value is calculated, the iteration process is repeated according to different values of the order N, and the relation change of the error weight Power and the order N can be obtained.
In experiment two, the single-step cycle prediction is to predict the actual cycle RC of the ith iteration i Prediction period PC as the i-1 st iteration i Finally, 4578 sets of prediction periods are obtained; in order to confirm the RLS period prediction of the order, a program can be designed on a Matlab platform according to the working principle of an RLS period prediction algorithm in an adaptive filter, an RC data set is tested, and 4578 groups of prediction periods are obtained. The experimental results shown in fig. 8, 9, 10, 11 and 12 can be plotted according to the above experimental contents.
The experimental results and analyses were as follows:
according to experiment one, see fig. 7, a distribution diagram of error weights (powers) of the RLS filter according to the order N is given, and since the 1 st order PC can be approximately regarded as a single step PC, the 1 st order PC is not given in the distribution diagram. Along with the increase of the N order, the Power value is presented, the distribution situation that the Power value firstly drops rapidly and then rises slowly can be found, a minimum value N =16 can be used as the minimum value of the Power, namely, the optimal order of the RLS period prediction algorithm is considered to be 16 orders, namely m = Max N =16. Experiment two was performed on a 16 th order RLS filter.
According to the second experiment, the advantages and the disadvantages of the RLS period N-order prediction algorithm and the single-step period prediction algorithm are analyzed from different angles respectively in the steps of FIG. 8, FIG. 9, FIG. 10, FIG. 11 and FIG. 12, and the conclusion that the adaptive filter can be used for industrial field production of the circular knitting machine is given.
Fig. 8 and 9 show the experimental results of filter single step PC error and filter 16 th order PC error, respectively. From fig. 8 it can be observed that: the filter single step PC error value can exhibit irregular fluctuations around the 0 axis, ranging from-170 to 120; the larger the fluctuation amplitude is, the farther the fluctuation amplitude is from the axis 0, the worse the single-step period prediction performance is, 107 periods are distributed in different period regions in the graph, and 7 times are distributed in different period regions in the graph, wherein the number of the periods exceeds 50; from fig. 9 it can be observed that: the filter 16-order PC error value also shows irregular fluctuation around the 0 axis, and the fluctuation range is-290-230; there are 20 cycles in excess of 50 in the figure, and 12/20 before the 80 th cycle, 4 in excess of 100, distributed at cycle numbers 2, 6, 7, 8. From the phenomenon, the distance between the point C and the point B relative to the point A is larger, the prediction result of the 16-order period is generally more accurate than that of the single-step period, the abnormal prediction value can be controlled in the stage of acquiring the standard learning period in the 80 th period, and the 16-order period prediction effect is better than that of the single-step period prediction.
See FIG. 10 for the difference between the absolute value of the 16 th order error and the absolute value of the single step error for the cycle (70-79), as observed in FIG. 10: in the periods 70-79, the number of positive values in 10 periods is 6, the requirement of the standard learning period is met, and the 80 th period can be selected to be switched from the single step prediction period to the 16-order period prediction algorithm. Fig. 11 shows the difference η between the absolute value of the 16 th order error and the absolute value of the single step error, positive values indicating that the absolute value of the 16 th order error is greater than the absolute value of the single step error, and negative values indicating that the absolute value of the 16 th order error is less than the absolute value of the single step error, as can be observed from fig. 11: overall, the positive value of η is much less than the number of negative values and more positive than negative values before 80 cycles, 80-300 cycles positive values are on the same order as the statistical number of negative values, and the 16 th order predicted cycle error is similar to the single step predicted cycle error. FIG. 12 shows the results of the positive and negative statistics of the difference between the absolute value of the error of 16 th order and the absolute value of the error of one step, where 1 indicates that the difference is positive and 2 indicates that the difference is negative. From fig. 12 it can be observed that: in 4575 total group data, positive values (1) η 1 were 1318 groups, negative values (2) η 2 were 3257 groups, and the positive-negative ratioThe prediction accuracy of the 16 th order cycle is more accurate than that of the single step cycleThe improvement of (2) is 40.46%.
Through the experimental analysis, the 16-order RLS period prediction algorithm can effectively and more accurately predict the rotation period of the circular knitting machine, but a self-learning process of the vector weight needs to be carried out through a buffer period of about 80 before the stable PC is predicted. The single-step period prediction algorithm is adopted, although self-learning cannot be achieved, and adaptability is not provided, the single-step period prediction algorithm can be used as a temporary prediction period in the RLS buffer period, and normal operation of timing snapshot work is guaranteed. Referring to the working mechanism of the circular knitting machine adaptive filter shown in fig. 6, the working mechanism can be used in a complex production environment of the circular knitting machine, namely, an RLS buffer period and an RLS prediction period are set, wherein the RLS buffer period is formed by a single step prediction period, and the RLS prediction period is formed by a prediction period obtained by an RLS period prediction algorithm with a determined order.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. A method for predicting the rotation period of a circular knitting machine by adopting an adaptive filter is characterized by comprising the following steps:
receiving serial port signals sent by the proximity switch in real time, and obtaining the actual rotation period RC of the large circular knitting machine by calculating the time difference of the two latest effective serial port signals i (ii) a Wherein i represents the rotation times of the circular knitting machine, and the iteration cycle times are represented in a filter;
filter parameter learning is carried out by adopting RLS period prediction algorithm, and the prediction period PC of the next turn is calculated i ；
Judging whether the current filter parameter reaches a learning standard condition;
if the current filter parameter does not reach the learning standard condition, the single step period prediction algorithm is adopted to obtain the prediction period PC of the next turn i And jumping out of the filter iteration process, and waiting for the (i + 1) th iteration;
if the current filter parameter reaches the learning targetQuasi-condition, calculating to obtain PC by adopting the RLS period prediction algorithm i Predicted period PC as next revolution i And jumping out of the filter iteration process, and waiting for the (i + 1) th iteration.
2. The method of claim 1, wherein the RLS period prediction algorithm is used for learning filter parameters, and the learning process is as follows:
a. calculating the period error e i The following are:
e i ＝RC i -PC i-1 (1)
wherein e is i The periodic error of the ith iteration is represented by an error feedback signal of an RLS filter, and the periodic error of the ith iteration is represented by two parameters RC i And PC i-1 Subtracting to obtain the result; RC (resistor-capacitor) capacitor i Expressed as the actual period of the ith iteration, for calculating the period error e as the ith iteration i A first parameter of (a); PC (personal computer) i-1 Outputting the obtained prediction period for the i-1 th iteration to calculate the period error e as the i-th iteration i A second parameter of (1);
b. computing a gain vector k i (m) as follows:
wherein k is i (m) denotes the gain vector of the i-th iteration of size m x 1, typically k i The larger the value (m) is, the stronger the correction capability of the RLS period prediction algorithm is; λ is a constant, is an empirical coefficient, and represents a forgetting factor; m is a constant, is an empirical coefficient, represents the RLS adaptive filter order, and can be obtained by calculation according to the collected actual rotation period of the current circular knitting machine; p i-1 (m) an inverse matrix of the i-1 st iteration of size m x m; u. u i-1 (m) an input matrix representing the i-1 th iteration of size m x 1;
c. computing an inverse matrix P of the iteration i (m) is as follows
Wherein, P i (m) an inverse matrix of the ith iteration of size m x m; t represents matrix transposition;
d. calculating weight vector w i (m), as follows:
w i (m)＝w i-1 (m)+k i (m)e i (4)
wherein, w i (m) represents a weight vector representing the i-th iteration of size m x 1; w is a i-1 (m) represents vector values of the weight vectors representing the i-1 th iteration of size m x 1;
e. updating an input matrix u i (m), as follows:
u i (m)＝[RC i ；u i-1 (1:m-1)] (5)
wherein u is i (m) represents the input matrix of the ith iteration of size m x 1, which is for the RC i Performing m-order sampling constitution, and updating u for each iteration i (m), the update mode is an in-out queue mode, i.e. a new RC is input at the head of the queue for each iteration i And the u vector values from 1 to m-1 of the i-1 iteration are pushed to the tail of the queue, and the tail of the queue u i Value of (m) is represented by u i (m-1) covering;
f. calculating the predicted period PC of the next revolution i The following:
PC i ＝w i T (m)u i (m) (6)
wherein, PC i The prediction period obtained for the output of the ith iteration, i.e. the output of the RLS filter, is used as the period error e of the (i + 1) th iteration i+1 The second parameter of (2) is the weight vector w of the ith iteration i Transposed vector of (m) and input vector u of order m i And (m) is obtained by taking the cross product of the two.
3. The method of claim 1, wherein the method for predicting the rotation period of the circular knitting machine using the adaptive filter is characterized in thatIn the single-step period prediction algorithm, the actual rotation period RC is used i Set to the prediction period PC of the next revolution i 。
4. The method as claimed in claim 1, wherein the determining whether the current filter parameter meets the learning criterion condition includes:
if the error value | e calculated by the RLS period prediction algorithm is adopted in the continuous t periods i I and the single step period prediction error value e i When the proportion theta of the negative value of the difference obtained by subtracting the' l is more than or equal to a preset value, the learning standard condition is met; wherein e is i '＝RC i -RC i-1 。
5. The method of claim 4, wherein t is equal to 10, and the predetermined value is equal to 60%.
6. The method for predicting the rotation period of the circular knitting machine by using the adaptive filter as claimed in claim 1, wherein the method for judging the effectiveness of the serial port signal comprises the following steps: and judging whether the value of the serial port signal designated field is equal to a set value or not, and if so, judging that the received serial port signal is valid.
7. A circular knitting machine rotation period prediction system adopting an adaptive filter is based on the circular knitting machine rotation period prediction method adopting the adaptive filter in any one of claims 1 to 6, and is characterized by comprising the following steps: the device comprises a lower computer, an upper computer and a serial port transmission line connected between the lower computer and the upper computer; the lower computer comprises a circular knitting machine and a proximity switch; the proximity switch comprises an infrared probe fixed on the inner wall of a machine table of the circular knitting machine and a circumferential outer wall nut capable of performing circumferential motion along with the circular knitting machine; and when the circular knitting machine works for each circle, the proximity switch generates a serial port signal and sends the serial port signal to the upper computer through the serial port transmission line, and the upper computer performs rotation period prediction according to the circular knitting machine rotation period prediction method.
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