CN117891161B - Bicycle bottom bracket bearing forging process adjusting method based on self-adaptive control algorithm - Google Patents
Bicycle bottom bracket bearing forging process adjusting method based on self-adaptive control algorithm Download PDFInfo
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Abstract
The invention relates to the technical field of non-electric variable adjustment, in particular to a bicycle bottom bracket bearing forging process adjustment method based on a self-adaptive control algorithm, which comprises the following steps: acquiring a temperature change curve and a deformation stress curve of a center shaft of the bicycle; segmenting the temperature change curve to obtain a control intervention curve segment and a non-control intervention curve segment; thereby obtaining an intervention curve; acquiring a feedback error of a control intervention curve segment; obtaining a feedback error curve according to the feedback error and the control intervention curve section; decomposing the feedback error curve to obtain the compensation weight of the PID controller; acquiring a judgment coefficient; and controlling the temperature of a heating system of the hydraulic forging press according to the judging coefficient and the compensation weight. The invention compensates the problems of poor temperature regulation precision and oscillation of a control system caused by time lag errors of the temperature sensor, and greatly improves the stability and the production efficiency of the temperature environment of the metal workpiece of the center shaft of the bicycle in the forging process.
Description
Technical Field
The invention relates to the technical field of non-electric variable adjustment, in particular to a bicycle bottom bracket bearing forging process adjustment method based on a self-adaptive control algorithm.
Background
The bottom bracket of a bicycle is a component that connects the two bicycle shields, and typically includes left and right pedals and a bottom bracket that is required to have sufficient strength and durability to withstand the stresses and loads during riding. The forging of the middle shaft is a main plastic process in the production process of the middle shaft of the bicycle, and the deformation temperature has obvious influence on the plastic processing of the alloy, so that the control of the deformation temperature is particularly important to the plastic process of the middle shaft of the bicycle.
The self-adaptive PID controller combines the self-adaptive control idea with the PID controller, and generally performs control adjustment on the deformation temperature in the forging process through the combination of multi-sensor feedback and the self-adaptive PID controller so as to ensure the ideal forging temperature, but because the self-adaptive PID controller implements control logic in a mode of observing errors, the self-adaptive PID controller has extremely high dependence on sensors, and the metal has inertia and time lag in the heating and cooling processes in the forging process, which means that the actual temperature change may not be immediately reflected on the sensor reading, thus the control process may have the problem of error superposition and further influence the adjustment effect of the bicycle center shaft forging process.
Disclosure of Invention
In order to solve the problems, the invention provides a bicycle bottom bracket forging process adjusting method based on an adaptive control algorithm.
The invention discloses a bicycle center shaft forging process adjusting method based on a self-adaptive control algorithm, which adopts the following technical scheme:
One embodiment of the invention provides a bicycle bottom bracket forging process adjusting method based on an adaptive control algorithm, which comprises the following steps:
acquiring a temperature change curve and a deformation stress curve of a center shaft of the bicycle;
Segmenting the temperature change curve according to extreme points in the temperature change curve to obtain a plurality of temperature curve segments of the temperature change curve; obtaining the probability of PID control intervention in each temperature curve segment according to the slope corresponding to the temperature value in the temperature curve segment; obtaining a plurality of control intervention curve sections and a plurality of non-control intervention curve sections of the temperature change curve according to the probability of PID control intervention of the temperature curve sections;
Obtaining an intervention curve according to the control intervention curve section and the non-control intervention curve section; matching the intervention curve with the deformation stress curve to obtain an optimal matching stress value of each temperature value in each control intervention curve section on the intervention curve; obtaining a feedback error of each control intervention curve segment according to the control intervention curve segment and the optimal matching stress value; recording a set formed by feedback errors of all the control intervention curve segments as a feedback error set;
obtaining a feedback error curve according to the feedback error and a control intervention curve segment on the intervention curve; decomposing the feedback error curve to obtain the compensation weight of the proportional gain coefficient, the compensation weight of the differential gain coefficient and the compensation weight of the integral gain coefficient of the PID controller; obtaining a judgment coefficient according to the feedback error set and the control intervention curve segment; and controlling the temperature of a heating system of the hydraulic forging press according to the judging coefficient and the compensation weight.
Further, the probability of PID control intervention in each temperature curve segment is obtained according to the slope corresponding to the temperature value in the temperature curve segment, and the method comprises the following specific steps:
Acquiring a slope corresponding to each temperature value in a temperature change curve, and performing linear normalization processing on the slopes corresponding to all the temperature values in the temperature change curve to obtain a plurality of normalized slopes;
The first to change the temperature curve Maximum value of normalized slope corresponding to temperature value in each temperature curve segment and the first/> of temperature change curveThe average value of the minimum values of the normalized slopes corresponding to the temperature values in the temperature curve segments is recorded as a first average value; each time in the temperature change curve is recorded as a moment, and the first/>, of the temperature change curve is recordedThe median value at all times of each temperature curve segment is recorded as the first/>, of the temperature change curveThe median moment of each temperature curve segment will be the first/>, of the temperature change curveNormalized slope corresponding to temperature value at median time of each temperature curve segment is recorded as/>; Combine the first average with/>The absolute value of the difference value is recorded as a first difference value; temperature change curve/>Information entropy and/>, of normalized slopes corresponding to all temperature values in each temperature curve segmentIs expressed as a first ratio,/>Is the first/>, of the temperature change curveNumber of different normalized slopes in normalized slopes corresponding to all temperature values in each temperature curve segment,/>Is a logarithmic function with a base of 2; the product of the first average value and the first ratio is recorded as a first product, and the difference value of 1 minus the first product is used as the first/>There is a probability that a PID control will interfere with each temperature curve segment.
Further, the method obtains a plurality of control intervention curve sections and a plurality of non-control intervention curve sections of the temperature change curve according to the probability of PID control intervention in the temperature curve sections, and comprises the following specific steps:
A first threshold value is preset, a temperature curve section with the PID control intervention probability larger than the first threshold value is used as a control intervention curve section of a temperature change curve, and a temperature curve section with the PID control intervention probability smaller than or equal to the first threshold value is used as a non-control intervention curve section of the temperature change curve.
Further, the method for obtaining the intervention curve according to the control intervention curve section and the non-control intervention curve section comprises the following specific steps:
And (3) classifying the temperature values of all the non-control intervention curve sections in the temperature change curve into 0, keeping the temperature values of all the control intervention curve sections in the temperature change curve unchanged, obtaining an adjusted temperature change curve, and recording the temperature change curve as an intervention curve.
Further, the matching of the intervention curve and the deformation stress curve to obtain the best matching stress value of each temperature value in each control intervention curve section on the intervention curve comprises the following specific steps:
Performing DTW matching on the intervention curve and the deformation stress curve to obtain a plurality of matching stress values corresponding to each temperature value in each control intervention curve section on the intervention curve in the deformation stress curve, marking any one temperature value in any one control intervention curve section on the intervention curve as a target temperature value, obtaining the Euclidean distance between the target temperature value and each matching stress value, and taking the matching stress value corresponding to the minimum Euclidean distance as the optimal matching stress value of the target temperature value.
Further, the feedback error of each control intervention curve segment is obtained according to the control intervention curve segment and the best matching stress value, and the method comprises the following specific steps:
on the intervention curve Control intervention curve segment No.)Time corresponding to each temperature value minus the/>The time corresponding to the best matching stress value of each temperature value in the deformation stress curve is recorded as a first difference value, and the square value of the first difference value is recorded as the first/>A second difference value of each control intervention curve segment is obtained to obtain the first difference value on the intervention curveAll second difference values of each control intervention curve segment, will intervene on the curve at the first/>The average of all second difference values of each control intervention curve segment is taken as the/>, on the intervention curveThe individual controls intervene in the feedback error of the curve segment.
Further, the feedback error curve is obtained according to the feedback error and the control intervention curve segment on the intervention curve, and the specific steps are as follows:
recording each time in the intervention curve as a moment, and recording the first time on the intervention curve The median value of all moments of each control intervention curve segment is recorded as the/>, on the intervention curveThe median moment of each control intervention curve segment is used for carrying out the/>, on the intervention curveThe median moment of each control intervention curve segment is taken as the/>The abscissa of each control intervention curve segment controls the/>, on the intervention curveFeedback error of each control intervention curve segment as the/>The ordinate of each control intervention curve segment; acquiring the abscissa and the ordinate of each control intervention curve segment, constructing a scatter diagram according to the abscissa and the ordinate of all control intervention curve segments, and marking the scatter diagram as a feedback error scatter diagram; and fitting the feedback error scatter diagram to obtain a fitting curve, and recording the fitting curve as a feedback error curve.
Further, the decomposing the feedback error curve to obtain the compensation weight of the proportional gain coefficient, the compensation weight of the differential gain coefficient and the compensation weight of the integral gain coefficient of the PID controller comprises the following specific steps:
ICA decomposition is carried out on the feedback error curve, wherein the number of decomposed components is 3, and three error components of the feedback error curve are obtained; the error component comprises a number of component magnitudes; and accumulating and summing all the component amplitude values in each error component to obtain a component amplitude accumulated value of each error component, arranging the component amplitude accumulated values of all the error components in sequence from large to small, and sequentially taking the component amplitude accumulated values as the compensation weight of the proportional gain coefficient, the compensation weight of the differential gain coefficient and the compensation weight of the integral gain coefficient of the PID controller.
Further, the method for obtaining the decision coefficient according to the feedback error set and the control intervention curve segment comprises the following specific steps:
obtaining the range of the temperature value on each control intervention curve segment, and recording a set formed by the range of the temperature values on all control intervention curve segments as a temperature compensation quantity set;
And acquiring pearson correlation coefficients of the feedback error set and the temperature compensation quantity set, and taking square values of the pearson correlation coefficients of the feedback error set and the temperature compensation quantity set as judgment coefficients.
Further, the temperature control of the heating system of the hydraulic forging press according to the judging coefficient and the compensation weight comprises the following specific steps:
The product of the judging coefficient and the compensation weight of the proportional gain coefficient is recorded as an initial adjustment weight of the proportional gain coefficient, and the sum of the initial adjustment weight of the proportional gain coefficient and 1 is recorded as an adjustment weight of the proportional gain coefficient; the product of the judging coefficient and the compensating weight of the differential gain coefficient is recorded as the initial adjusting weight of the differential gain coefficient, and the sum of the initial adjusting weight of the differential gain coefficient and 1 is recorded as the adjusting weight of the differential gain coefficient; the product of the judging coefficient and the compensation weight of the integral gain coefficient is recorded as the initial adjustment weight of the integral gain coefficient, and the sum of the initial adjustment weight of the integral gain coefficient and 1 is recorded as the adjustment weight of the integral gain coefficient;
The last time in the temperature change curve is recorded as the final time, the next time of the final time is recorded as the current time, and three default control coefficients of the PID controller at the current time are obtained, wherein the three default control coefficients comprise: proportional gain coefficient at the current moment, differential gain coefficient at the current moment and integral gain coefficient at the current moment; taking the product of the adjusting weight of the proportional gain coefficient and the proportional gain coefficient at the current moment as the final proportional gain coefficient at the current moment; taking the product of the adjustment weight of the differential gain coefficient and the differential gain coefficient at the current moment as the final differential gain coefficient at the current moment; taking the product of the adjusting weight of the integral gain coefficient and the integral gain coefficient at the current moment as the final integral gain coefficient at the current moment, and controlling the temperature at the current moment of the heating system of the hydraulic forging press by utilizing a PID controller according to the final proportional gain coefficient, the final differential gain coefficient and the final integral gain coefficient at the current moment.
The technical scheme of the invention has the beneficial effects that: according to the invention, after the temperature change curve and the deformation stress curve of the bicycle center shaft are acquired, the temperature change curve is segmented to obtain a plurality of temperature curve sections of the temperature change curve, the process during forging the bicycle center shaft can be better regulated through local analysis, the probability of PID control intervention in each temperature curve section is acquired through the corresponding slope of the temperature value in the temperature curve section, wherein the probability of intervention represents the intervention degree of PID control in the temperature curve section, then the plurality of control intervention curve sections and the plurality of non-control intervention curve sections of the temperature change curve are obtained according to the probability of PID control intervention in the temperature curve section, further the intervention curve is obtained, the feedback error of each control intervention curve section is obtained through matching the intervention curve and the deformation stress curve, a feedback error set is obtained, the feedback error curve is obtained and is decomposed and analyzed to obtain a judgment coefficient, finally the temperature control is carried out on a heating system of the hydraulic press through the judgment coefficient, and the problem of poor temperature regulation precision and vibration of a control system caused by time lag errors of a temperature sensor is solved, and the stability and the production efficiency of a metal workpiece of the bicycle center shaft in the forging process are greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of a method for adjusting a bicycle bottom bracket forging process based on an adaptive control algorithm according to one embodiment of the present invention;
FIG. 2 is a flow chart of temperature control of a heating system of a hydraulic forging press provided in accordance with one embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of the adjusting method of the bicycle bottom bracket forging process based on the self-adaptive control algorithm according to the invention, which is provided by the invention, with reference to the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the bicycle bottom bracket forging process adjusting method based on the self-adaptive control algorithm provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for adjusting a forging process of a bicycle bottom bracket axle based on an adaptive control algorithm according to an embodiment of the present invention is shown, the method includes the following steps:
And S001, acquiring a temperature change curve and a deformation stress curve of a center shaft of the bicycle.
Specifically, a temperature sensor and a deformation sensor are arranged on the hydraulic forging press and used for monitoring the forging process of a metal workpiece and feeding back the temperature value to the self-adaptive PID controller, the temperature value of the surface of the metal workpiece of the middle shaft of the bicycle is collected through the temperature sensor, the stress value of the metal workpiece of the middle shaft of the bicycle is collected through the deformation sensor, the collection frequency of the temperature sensor and the deformation sensor is once every 1 second, the curve formed by all the temperature values in the last hour is recorded as a temperature change curve of the middle shaft of the bicycle, and the curve formed by all the stress values in the last hour is recorded as a deformation stress curve of the middle shaft of the bicycle.
The temperature change curve and the deformation force curve are two-dimensional curves, the horizontal axis of the temperature change curve is time, and the vertical axis is a temperature value of the surface of the metal workpiece corresponding to the time.
The temperature sensor is arranged on the surface of a die of the hydraulic forging press so as to be in contact with a metal workpiece being forged, so that the temperature value of the surface of the metal workpiece is collected; the deformation sensor may be a strain gauge or other measuring device such as a strain patch.
So far, the temperature change curve and the deformation stress curve of the center shaft of the bicycle are collected.
Step S002, segmenting the temperature change curve according to extreme points in the temperature change curve to obtain a plurality of temperature curve segments of the temperature change curve; and obtaining a plurality of control intervention curve sections and a plurality of non-control intervention curve sections of the temperature change curve according to the slopes corresponding to the temperature values in the temperature curve sections.
It should be noted that, in the forging process, the heating system provides energy for deformation of the metal workpiece to enable the deformation of the metal workpiece to generate temperature fluctuation change, but due to influences of factors such as blank shape of the metal workpiece, feedback error when feeding back to the self-adaptive PID controller, machine performance and the like, participation degree of the self-adaptive PID controller of the metal workpiece in each bicycle center shaft is inconsistent in the forging process, in order to obtain time lag errors of the temperature sensor under different forging processes of the metal workpiece, analysis is needed to be performed on collected temperature change curves of the bicycle center shaft so as to determine all control nodes of the self-adaptive PID controller involved in adjustment.
In the temperature change curve of the bicycle bottom bracket axle, there are curve sections with increased temperature and curve sections with reduced temperature, each curve section is not a smooth continuous curve, and a plurality of different slopes exist in the curve sections.
Specifically, the temperature change curve is segmented according to extreme points in the temperature change curve, so as to obtain a plurality of temperature curve segments of the temperature change curve, which are specifically as follows:
obtaining all extreme points in the temperature change curve, taking the extreme points in the temperature change curve as segmentation points of the temperature change curve, and segmenting the temperature change curve according to the segmentation points to obtain a plurality of temperature curve segments of the temperature change curve.
When the temperature error occurs, the self-adaptive PID controller intervenes in the heating system, so that a part of all the temperature curve sections belong to temperature change sections which the self-adaptive PID controller participates in regulation.
Further, the probability of PID control intervention in each temperature curve segment is obtained according to the slope corresponding to the temperature value in the temperature curve segment, and the method specifically comprises the following steps:
acquiring a slope corresponding to each temperature value in the temperature change curve, and performing linear normalization processing on the slopes corresponding to all the temperature values in the temperature change curve to obtain a plurality of normalized slopes.
In the method, in the process of the invention,Is the first/>, of the temperature change curveThe temperature values in the temperature curve segments correspond to the maximum value of the normalized slope; /(I)Is the first/>, of the temperature change curveThe temperature value in each temperature curve segment corresponds to the minimum value of the normalized slope,/>The specific acquisition method of (1) is as follows: each time in the temperature change curve is recorded as a moment, and the first/>, of the temperature change curve is recordedThe median value at all times of each temperature curve segment is recorded as the first/>, of the temperature change curveThe median moment of each temperature curve segment will be the first/>, of the temperature change curveNormalized slope corresponding to temperature value at median time of each temperature curve segment is recorded as/>; If you/>The number of moments of each temperature curve segment is even, and the first/> isselectedThe left moment in the middle two moments of the temperature curve segments is taken as the median moment; /(I)To take absolute value,/>Is the first/>, of the temperature change curveInformation entropy of normalized slope corresponding to all temperature values in each temperature curve segment,/>Is the first/>, of the temperature change curveNumber of different normalized slopes in normalized slopes corresponding to all temperature values in each temperature curve segment,/>As a logarithmic function with 2 as the base,/>Is the first/>, of the temperature change curveThere is a probability that a PID control will interfere with each temperature curve segment.
It should be noted that the number of the substrates,Represents the/>Average value of maximum and minimum normalized slopes in each temperature curve segment, when/>When the trend that all normalized slopes in each temperature curve segment show nonlinear attenuation becomes smaller, according to the Lagrange's median theorem, the normalized slope of the median point is equal to the average normalized slope, and then when/>Near 0, it represents the/>The more all normalized slopes in the individual temperature curve segments exhibit a tendency to non-linear decay; /(I)Represents the entropy limit,/>For/>The ratio of the information entropy of the normalized slope corresponding to all the temperature values in each temperature curve segment to the entropy limit, namely the first/>, is the entropy limitMaximum information entropy of normalized slope distribution of each temperature curve segment, equal probability of normalized slope distribution of all types under entropy limit, and/>The closer the entropy of the normalized slope distribution information of the individual temperature curve segments is to the entropy limit, i.eThe closer to 1 is the first/>All normalized slopes in the individual temperature curve segments do not exhibit a gradient-like distribution, and vice versaSmaller means the/>The more the normalized slopes in the temperature curve sections are distributed in a gradient manner; so that the smaller the product of the two multiplications is indicative of the/>The more likely there is PID control intervention in each temperature curve segment, and therefore the constant 1 is used to subtractObtain the/>There is a probability that a PID control will interfere with each temperature curve segment.
It should be noted that, because the reasons for generating the temperature error in the heating system include energy supply, transformation of thermal energy into deformation stress of the metal workpiece, transmission loss, etc., the temperature environment belongs to a dynamic environment, when the error occurs, the adaptive PID controller with fixed parameter setting cannot adapt to all dynamic errors, so the adaptive PID controller almost presents multi-section intervention to the temperature change curve, and because the proportional gain coefficient of the PID determines that when the error is larger, the adaptive PID controller increases the output of the heating system, when the error is smaller, the output of the heating system is reduced, and meanwhile, when the temperature change speed is very large, the differential gain coefficient also decreases the output, so the temperature error gradually decreases during multi-section intervention, the slope change in the temperature curve section gradually decreases as a trend of nonlinear attenuation, and the number distribution of slope types presents a gradient shape. The temperature curve segment meeting the characteristics can be judged to be the curve segment interfered by the self-adaptive PID controller.
Specifically, according to the probability of PID control intervention in the temperature curve section, a plurality of control intervention curve sections and a plurality of non-control intervention curve sections of a temperature change curve are obtained, and the method specifically comprises the following steps:
A first threshold value is preset, the first threshold value is used for describing, in this embodiment, a temperature curve segment with a probability of PID control intervention greater than the first threshold value is used as a control intervention curve segment of a temperature change curve, and a temperature curve segment with a probability of PID control intervention less than or equal to the first threshold value is used as a non-control intervention curve segment of the temperature change curve.
Thus, a plurality of control intervention curve sections and a plurality of non-control intervention curve sections of the temperature change curve are obtained.
Step S003, obtaining an intervention curve according to the control intervention curve section and the non-control intervention curve section; and matching the intervention curve with the deformation stress curve to obtain a feedback error of each control intervention curve section, and obtaining a feedback error set.
It should be noted that, the control intervention curve segment includes response information of the adaptive PID controller after error is generated in the forging process, and the temperature error is generated in the change of energy provided by the heating system to deformation of the metal workpiece in the forging process, so that feedback errors of the temperature sensor caused by forging deformation of the metal workpiece can be analyzed through the control intervention curve segment and the deformation stress curve.
Specifically, an intervention curve is obtained according to the control intervention curve section and the non-control intervention curve section, and the method specifically comprises the following steps:
And (3) classifying the temperature values of all the non-control intervention curve sections in the temperature change curve into 0, keeping the temperature values of all the control intervention curve sections in the temperature change curve unchanged, obtaining an adjusted temperature change curve, and recording the temperature change curve as an intervention curve.
Further, the intervention curve and the deformation stress curve are matched to obtain an optimal matched stress value of each temperature value in each control intervention curve section on the intervention curve, and the optimal matched stress value is specifically as follows:
Performing DTW matching on the intervention curve and the deformation stress curve to obtain a plurality of matching stress values corresponding to each temperature value in each control intervention curve section on the intervention curve in the deformation stress curve, marking any one temperature value in any one control intervention curve section on the intervention curve as a target temperature value, obtaining the Euclidean distance between the target temperature value and each matching stress value, and taking the matching stress value corresponding to the minimum Euclidean distance as the optimal matching stress value of the target temperature value; it should be noted that, the euclidean distance between the target temperature value and each matching stress value is obtained by the existing method, which is not described in detail in this embodiment, only one optimal matching stress value of each temperature value is needed, and if there are a plurality of matching stress values (optimal matching stress values) corresponding to the minimum euclidean distance value, one of the matching stress values is randomly selected as the optimal matching stress value.
Further, according to the control intervention curve segments and the best matching stress values, the feedback error of each control intervention curve segment is obtained, and the method specifically comprises the following steps:
In the method, in the process of the invention, To intervene on the curve/>Control intervention curve segment No.)Time corresponding to each temperature value,/>To intervene on the curve/>Control intervention curve segment No.)Time corresponding to best matching stress value of each temperature value in deformation stress curve,/>To intervene on the curve/>Number of temperature values in each control intervention curve segment,/>To intervene on the curve/>The individual controls intervene in the feedback error of the curve segment.
It should be noted that, when deformation of the metal workpiece occurs, the heating system continuously provides energy, and accordingly temperature fluctuation occurs, and the adaptive PID controller also starts to compensate the temperature, so that a tandem response relationship exists between the deformation stress curve change and the control intervention curve section, and thereforeRepresents the/>Control intervention curve segment No.)The square difference between the time corresponding to each temperature value and the time corresponding to the best matching stress value, the obtained time difference represents the time lag error between deformation and temperature control adjustment,/>Represents the/>Average value of time lag errors of each control intervention curve segment as the/>The individual controls intervene in the feedback error of the curve segment.
Further, the set of feedback errors for all control intervention curve segments is denoted as the feedback error set.
Thus, a feedback error set is obtained.
Step S004, obtaining a feedback error curve according to the feedback error and a control intervention curve segment on the intervention curve; decomposing the feedback error curve to obtain compensation weights; obtaining a judgment coefficient according to the feedback error set and the control intervention curve segment; and controlling the temperature of a heating system of the hydraulic forging press according to the judging coefficient and the compensation weight.
It should be noted that the feedback error set includes errors caused by sensor time lags in all different temperature control processes of the temperature change curve, and the feedback errors have different degrees of influence on the adaptive PID controller; the feedback error needs to be compensated by compensating for the three control coefficients of the adaptive PID controller.
Specifically, according to the feedback error and the control intervention curve segment on the intervention curve, a feedback error curve is obtained, specifically as follows:
recording each time in the intervention curve as a moment, and recording the first time on the intervention curve The median value of all moments of each control intervention curve segment is recorded as the/>, on the intervention curveThe median moment of each control intervention curve segment is used for carrying out the/>, on the intervention curveThe median moment of each control intervention curve segment is taken as the/>The abscissa of each control intervention curve segment controls the/>, on the intervention curveFeedback error of each control intervention curve segment as the/>The ordinate of each control intervention curve segment; if you/>The number of moments of each control intervention curve segment is even, and the/> isselectedThe left moment in the middle two moments of the control intervention curve segments is taken as the median moment; acquiring the abscissa and the ordinate of each control intervention curve segment, constructing a scatter diagram according to the abscissa and the ordinate of all control intervention curve segments, and marking the scatter diagram as a feedback error scatter diagram; the feedback error scatter diagram is a two-dimensional scatter diagram, the horizontal axis is time, and the vertical axis is feedback error of the control intervention curve section; fitting the feedback error scatter diagram to obtain a fitting curve, and recording the fitting curve as a feedback error curve; it should be noted that, in this embodiment, the feedback error scatter diagram is fitted by using a least square method, where a fitted curve is a quintic polynomial curve, and the fitting is specifically performed by using the existing method, which is not described in detail in this embodiment.
Further, the feedback error curve is decomposed to obtain the compensation weight of the proportional gain coefficient, the compensation weight of the differential gain coefficient and the compensation weight of the integral gain coefficient of the PID controller, which are specifically as follows:
ICA decomposition is carried out on the feedback error curve, wherein the number of decomposed components is 3, and three error components of the feedback error curve are obtained; it should be noted that each error component includes a plurality of component magnitudes; and accumulating and summing all the component amplitude values in each error component to obtain a component amplitude accumulated value of each error component, arranging the component amplitude accumulated values of all the error components in sequence from large to small, and sequentially taking the component amplitude accumulated values as the compensation weight of the proportional gain coefficient, the compensation weight of the differential gain coefficient and the compensation weight of the integral gain coefficient of the PID controller.
It should be noted that, since the feedback errors all have a certain influence on the three control coefficients of the adaptive PID controller at the same time, and the influence degrees are different, the feedback error scatter diagram is fitted, then the independent components of the fitting curve are decomposed, that is, it is assumed that three error components exist for the three control coefficients in each feedback error, and the compensation weights of the three control coefficients are obtained by decomposition.
It should be noted that, however, since the feedback error affects the adaptive PID controller in a nonlinear manner, the control coefficient cannot be adjusted directly by using the compensation weight.
Specifically, a decision coefficient is obtained according to the feedback error set and the control intervention curve segment, and the decision coefficient is specifically as follows:
obtaining the range of the temperature value on each control intervention curve segment, and recording a set formed by the range of the temperature values on all control intervention curve segments as a temperature compensation quantity set; the temperature compensation amount set includes a plurality of extremely bad values (of temperature values).
Acquiring pearson correlation coefficients of a feedback error set and a temperature compensation quantity set, and taking square values of the pearson correlation coefficients of the feedback error set and the temperature compensation quantity set as judgment coefficients; it should be noted that, the decision coefficient represents the influence degree of the feedback error set and the temperature compensation set, and is subsequently used for correcting the compensation weight.
The response of the adaptive PID controller is delayed due to the time lag error of the sensor, so that the compensation of the control coefficient is forward compensation, and the response speed of the control system is improved.
Further, according to the judging coefficient, the temperature of the heating system of the hydraulic forging press is controlled, specifically as follows:
the product of the judging coefficient and the compensation weight of the proportional gain coefficient is recorded as an initial adjustment weight of the proportional gain coefficient, and the sum of the initial adjustment weight of the proportional gain coefficient and 1 is recorded as an adjustment weight of the proportional gain coefficient; the product of the judging coefficient and the compensating weight of the differential gain coefficient is recorded as the initial adjusting weight of the differential gain coefficient, and the sum of the initial adjusting weight of the differential gain coefficient and 1 is recorded as the adjusting weight of the differential gain coefficient; the product of the determination coefficient and the compensation weight of the integral gain coefficient is recorded as an initial adjustment weight of the integral gain coefficient, and the sum of the initial adjustment weight of the integral gain coefficient and 1 is recorded as an adjustment weight of the integral gain coefficient.
The last time in the temperature change curve is recorded as the final time, the next time of the final time is recorded as the current time, and three default control coefficients of the PID controller at the current time are obtained, wherein the three default control coefficients comprise: proportional gain coefficient at the current moment, differential gain coefficient at the current moment and integral gain coefficient at the current moment; taking the product of the adjusting weight of the proportional gain coefficient and the proportional gain coefficient at the current moment as the final proportional gain coefficient at the current moment; taking the product of the adjustment weight of the differential gain coefficient and the differential gain coefficient at the current moment as the final differential gain coefficient at the current moment; taking the product of the adjusting weight of the integral gain coefficient and the integral gain coefficient at the current moment as a final integral gain coefficient at the current moment, and controlling the temperature at the current moment of the heating system of the hydraulic forging press by utilizing a PID controller according to the final proportional gain coefficient, the final differential gain coefficient and the final integral gain coefficient at the current moment; referring to fig. 2, fig. 2 is a flow chart of temperature control of a heating system of a hydraulic forging press according to the present embodiment; it should be noted that, the method for obtaining three default control coefficients of the PID controller at the current moment is an existing method, which is not described in detail in this embodiment.
It should be noted that, through the adjustment of the three control coefficients of the PID controller, the problems of poor temperature adjustment precision and oscillation of the control system caused by time lag errors of the temperature sensor are compensated, and the stability and the production efficiency of the temperature environment of the metal workpiece in the forging process are greatly improved.
So far, the control coefficient of the PID controller is adjusted to complete the forging process adjustment of the bicycle bottom bracket bearing of the self-adaptive control algorithm.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (8)
1. The bicycle bottom bracket bearing forging process adjusting method based on the self-adaptive control algorithm is characterized by comprising the following steps of:
acquiring a temperature change curve and a deformation stress curve of a center shaft of the bicycle;
Segmenting the temperature change curve according to extreme points in the temperature change curve to obtain a plurality of temperature curve segments of the temperature change curve; obtaining the probability of PID control intervention in each temperature curve segment according to the slope corresponding to the temperature value in the temperature curve segment; obtaining a plurality of control intervention curve sections and a plurality of non-control intervention curve sections of the temperature change curve according to the probability of PID control intervention of the temperature curve sections;
Obtaining an intervention curve according to the control intervention curve section and the non-control intervention curve section; matching the intervention curve with the deformation stress curve to obtain an optimal matching stress value of each temperature value in each control intervention curve section on the intervention curve; obtaining a feedback error of each control intervention curve segment according to the control intervention curve segment and the optimal matching stress value; recording a set formed by feedback errors of all the control intervention curve segments as a feedback error set;
Obtaining a feedback error curve according to the feedback error and a control intervention curve segment on the intervention curve; decomposing the feedback error curve to obtain the compensation weight of the proportional gain coefficient, the compensation weight of the differential gain coefficient and the compensation weight of the integral gain coefficient of the PID controller; obtaining a judgment coefficient according to the feedback error set and the control intervention curve segment; temperature control is carried out on a heating system of the hydraulic forging press according to the judging coefficient and the compensation weight;
The method comprises the following specific steps of:
Acquiring a slope corresponding to each temperature value in a temperature change curve, and performing linear normalization processing on the slopes corresponding to all the temperature values in the temperature change curve to obtain a plurality of normalized slopes;
The first to change the temperature curve Maximum value of normalized slope corresponding to temperature value in each temperature curve segment and the first/> of temperature change curveThe average value of the minimum values of the normalized slopes corresponding to the temperature values in the temperature curve segments is recorded as a first average value; each time in the temperature change curve is recorded as a moment, and the first/>, of the temperature change curve is recordedThe median value at all times of each temperature curve segment is recorded as the first/>, of the temperature change curveThe median moment of each temperature curve segment will be the first/>, of the temperature change curveNormalized slope corresponding to temperature value at median time of each temperature curve segment is recorded as/>; Combine the first average with/>The absolute value of the difference value is recorded as a first difference value; temperature change curve/>Information entropy and/>, of normalized slopes corresponding to all temperature values in each temperature curve segmentIs expressed as a first ratio,/>Is the first/>, of the temperature change curveNumber of different normalized slopes in normalized slopes corresponding to all temperature values in each temperature curve segment,/>Is a logarithmic function with a base of 2; the product of the first average value and the first ratio is recorded as a first product, and the difference value of 1 minus the first product is used as the first/>The probability of PID control intervention exists in each temperature curve segment;
The calculation formula of the normalized slope is as follows:
Wherein, Is the first/>, of the temperature change curveThe probability of PID control intervention exists in each temperature curve segment; /(I)Is the first/>, of the temperature change curveThe temperature values in the temperature curve segments correspond to the maximum value of the normalized slope; /(I)Is the first temperature change curveThe temperature value in each temperature curve segment corresponds to the minimum value of the normalized slope; /(I)Taking an absolute value; /(I)Is the first/>, of the temperature change curveInformation entropy of normalized slope corresponding to all temperature values in each temperature curve segment; /(I)Is the first/>, of the temperature change curveThe number of different normalized slopes in the normalized slopes corresponding to all the temperature values in the temperature curve segments; /(I)Is a logarithmic function with a base of 2;
the feedback error of each control intervention curve segment is obtained according to the control intervention curve segment and the best matching stress value, and the method comprises the following specific steps:
on the intervention curve Control intervention curve segment No.)Time corresponding to each temperature value minus the/>The time corresponding to the best matching stress value of each temperature value in the deformation stress curve is recorded as a first difference value, and the square value of the first difference value is recorded as the first/>A second difference value of each control intervention curve segment is obtained to obtain the/>, on the intervention curveAll second difference values of each control intervention curve segment, will intervene on the curve at the first/>The average of all second difference values of each control intervention curve segment is taken as the/>, on the intervention curveFeedback errors of the individual control intervention curve segments;
The calculation formula of the feedback error is as follows:
In the method, in the process of the invention, To intervene on the curve/>Control intervention curve segment No.)Time corresponding to each temperature value,/>To intervene on the curve/>Control intervention curve segment No.)Time corresponding to best matching stress value of each temperature value in deformation stress curve,/>To intervene on the curve/>Number of temperature values in each control intervention curve segment,/>To intervene on the curve/>The individual controls intervene in the feedback error of the curve segment.
2. The method for adjusting the middle shaft forging process of the bicycle based on the self-adaptive control algorithm according to claim 1, wherein the probability of PID control intervention exists according to the temperature curve section, a plurality of control intervention curve sections and a plurality of non-control intervention curve sections of the temperature change curve are obtained, and the method comprises the following specific steps:
A first threshold value is preset, a temperature curve section with the PID control intervention probability larger than the first threshold value is used as a control intervention curve section of a temperature change curve, and a temperature curve section with the PID control intervention probability smaller than or equal to the first threshold value is used as a non-control intervention curve section of the temperature change curve.
3. The method for adjusting the middle axle forging process of the bicycle based on the self-adaptive control algorithm according to claim 1, wherein the intervention curve is obtained according to the control intervention curve section and the non-control intervention curve section, and the method comprises the following specific steps:
And (3) classifying the temperature values of all the non-control intervention curve sections in the temperature change curve into 0, keeping the temperature values of all the control intervention curve sections in the temperature change curve unchanged, obtaining an adjusted temperature change curve, and recording the temperature change curve as an intervention curve.
4. The method for adjusting the bottom bracket forging process of the bicycle based on the self-adaptive control algorithm according to claim 1, wherein the matching of the intervention curve and the deformation stress curve to obtain the best matching stress value of each temperature value in each control intervention curve section on the intervention curve comprises the following specific steps:
Performing DTW matching on the intervention curve and the deformation stress curve to obtain a plurality of matching stress values corresponding to each temperature value in each control intervention curve section on the intervention curve in the deformation stress curve, marking any one temperature value in any one control intervention curve section on the intervention curve as a target temperature value, obtaining the Euclidean distance between the target temperature value and each matching stress value, and taking the matching stress value corresponding to the minimum Euclidean distance as the optimal matching stress value of the target temperature value.
5. The method for adjusting the middle axle forging process of the bicycle based on the self-adaptive control algorithm according to claim 1, wherein the feedback error curve is obtained according to the feedback error and the control intervention curve segment on the intervention curve, comprising the following specific steps:
recording each time in the intervention curve as a moment, and recording the first time on the intervention curve The median value of all moments of each control intervention curve segment is recorded as the/>, on the intervention curveThe median moment of each control intervention curve segment is used for carrying out the/>, on the intervention curveThe median moment of each control intervention curve segment is taken as the/>The abscissa of each control intervention curve segment controls the/>, on the intervention curveFeedback error of each control intervention curve segment as the/>The ordinate of each control intervention curve segment; acquiring the abscissa and the ordinate of each control intervention curve segment, constructing a scatter diagram according to the abscissa and the ordinate of all control intervention curve segments, and marking the scatter diagram as a feedback error scatter diagram; and fitting the feedback error scatter diagram to obtain a fitting curve, and recording the fitting curve as a feedback error curve.
6. The method for adjusting the center shaft forging process of the bicycle based on the self-adaptive control algorithm as set forth in claim 1, wherein the decomposing the feedback error curve to obtain the compensation weight of the proportional gain coefficient, the compensation weight of the differential gain coefficient and the compensation weight of the integral gain coefficient of the PID controller comprises the following specific steps:
ICA decomposition is carried out on the feedback error curve, wherein the number of decomposed components is 3, and three error components of the feedback error curve are obtained; the error component comprises a number of component magnitudes; and accumulating and summing all the component amplitude values in each error component to obtain a component amplitude accumulated value of each error component, arranging the component amplitude accumulated values of all the error components in sequence from large to small, and sequentially taking the component amplitude accumulated values as the compensation weight of the proportional gain coefficient, the compensation weight of the differential gain coefficient and the compensation weight of the integral gain coefficient of the PID controller.
7. The method for adjusting the middle shaft forging process of the bicycle based on the self-adaptive control algorithm according to claim 1, wherein the obtaining of the judgment coefficient according to the feedback error set and the control intervention curve segment comprises the following specific steps:
obtaining the range of the temperature value on each control intervention curve segment, and recording a set formed by the range of the temperature values on all control intervention curve segments as a temperature compensation quantity set;
And acquiring pearson correlation coefficients of the feedback error set and the temperature compensation quantity set, and taking square values of the pearson correlation coefficients of the feedback error set and the temperature compensation quantity set as judgment coefficients.
8. The method for adjusting the forging process of the bicycle bottom bracket bearing based on the self-adaptive control algorithm according to claim 1, wherein the temperature control of the heating system of the hydraulic forging press according to the judging coefficient and the compensation weight comprises the following specific steps:
The product of the judging coefficient and the compensation weight of the proportional gain coefficient is recorded as an initial adjustment weight of the proportional gain coefficient, and the sum of the initial adjustment weight of the proportional gain coefficient and 1 is recorded as an adjustment weight of the proportional gain coefficient; the product of the judging coefficient and the compensating weight of the differential gain coefficient is recorded as the initial adjusting weight of the differential gain coefficient, and the sum of the initial adjusting weight of the differential gain coefficient and 1 is recorded as the adjusting weight of the differential gain coefficient; the product of the judging coefficient and the compensation weight of the integral gain coefficient is recorded as the initial adjustment weight of the integral gain coefficient, and the sum of the initial adjustment weight of the integral gain coefficient and 1 is recorded as the adjustment weight of the integral gain coefficient;
The last time in the temperature change curve is recorded as the final time, the next time of the final time is recorded as the current time, and three default control coefficients of the PID controller at the current time are obtained, wherein the three default control coefficients comprise: proportional gain coefficient at the current moment, differential gain coefficient at the current moment and integral gain coefficient at the current moment; taking the product of the adjusting weight of the proportional gain coefficient and the proportional gain coefficient at the current moment as the final proportional gain coefficient at the current moment; taking the product of the adjustment weight of the differential gain coefficient and the differential gain coefficient at the current moment as the final differential gain coefficient at the current moment; taking the product of the adjusting weight of the integral gain coefficient and the integral gain coefficient at the current moment as the final integral gain coefficient at the current moment, and controlling the temperature at the current moment of the heating system of the hydraulic forging press by utilizing a PID controller according to the final proportional gain coefficient, the final differential gain coefficient and the final integral gain coefficient at the current moment.
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