CN114859735A - Self-adaptive control method and system for speed of hydraulic forging press - Google Patents

Self-adaptive control method and system for speed of hydraulic forging press Download PDF

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Publication number
CN114859735A
CN114859735A CN202210791370.6A CN202210791370A CN114859735A CN 114859735 A CN114859735 A CN 114859735A CN 202210791370 A CN202210791370 A CN 202210791370A CN 114859735 A CN114859735 A CN 114859735A
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parameters
speed
parameter
forging
adaptive
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Inventor
计鑫
潘高峰
王鑫
高晓勇
杨莎
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Tianjin Tianduan Press Group Co ltd
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Tianjin Tianduan Press Group Co ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

The invention discloses a speed self-adaptive control method and a speed self-adaptive control system for a hydraulic forging press, which belong to the field of speed control of hydraulic forging presses and comprise the following steps: s1, selecting sectional regulation of typical speed in the equipment debugging stage, performing closed-loop control through a discrete PID control algorithm, and determining the PID parameters of the basic working speeds of different speed sections; and S2, in the equipment production operation stage, automatically setting PID parameters according to the resistance of different forging hydraulic presses in producing different material products and the working speeds of different fire times of the same product. Firstly, determining a basic speed PID parameter in an equipment debugging stage; then, correcting and optimizing speed PID parameters of various products through a genetic algorithm in the process of putting equipment into production and running; finally, after long-term operation, a self-adaptive parameter library of various products is obtained, and the system can automatically match PID parameters according to the process parameters, so that the forging process speed is controlled more accurately.

Description

Self-adaptive control method and system for speed of hydraulic forging press
Technical Field
The invention belongs to the field of speed control of hydraulic forging presses, and particularly relates to a speed self-adaptive control method and system of a hydraulic forging press.
Background
The forging hydraulic machine belongs to a high-grade numerical control machine tool and a robot and plays an important role in guaranteeing national economy and national defense safety. The forging hydraulic press can be directly and widely applied to important fields of aerospace, nuclear power, new energy automobiles, ships and the like. With the improvement of the forging process level in China, higher requirements are provided for the intelligentization level and the process precision of forging equipment, so that the establishment of a speed self-adaptive control method and a speed self-adaptive control system of a hydraulic forging press is very important for realizing the accurate control of the forging process speed.
Disclosure of Invention
The invention provides a speed self-adaptive control method and a speed self-adaptive control system of a hydraulic forging press for solving the technical problems in the prior art, firstly, a basic speed PID parameter is determined in an equipment debugging stage; then, correcting and optimizing speed PID parameters of various products through a genetic algorithm in the process of putting equipment into production and running; finally, after long-term operation, a self-adaptive parameter library of various products is obtained, and the system can automatically match PID parameters according to the process parameters, so that the forging process speed is controlled more accurately.
The invention provides a speed self-adaptive control method of a hydraulic forging press, which comprises the following steps:
s1, in the equipment debugging stage, selecting the subsection adjustment of the typical speed, carrying out closed-loop control through a discrete PID control algorithm, and determining the basic working speed PID parameters of different speed sections:
Figure 346046DEST_PATH_IMAGE001
、
Figure 146292DEST_PATH_IMAGE002
and
Figure 508004DEST_PATH_IMAGE003
；
and S2, in the equipment production operation stage, automatically setting PID parameters according to the resistance of different forging hydraulic presses in producing different material products and the working speeds of different fire times of the same product.
Preferably, S2 is specifically:
step one, P, I and D parameters are calculated;
Figure 422870DEST_PATH_IMAGE004
；
Figure 553637DEST_PATH_IMAGE005
；
Figure 20390DEST_PATH_IMAGE006
；
wherein:
Figure 869398DEST_PATH_IMAGE007
representing P parameters of various die names at different speeds;
Figure 587955DEST_PATH_IMAGE008
representing the genetic algorithm calculation P parameter of various die names at different speeds; alpha represents the adaptive term coefficient of the P parameter;
Figure 573229DEST_PATH_IMAGE009
i parameters of various die names at different speeds are represented;
Figure 882987DEST_PATH_IMAGE010
representing the genetic algorithm calculation I parameters of various die names at different speeds; beta represents the adaptive term coefficient of the I parameter;
Figure 281607DEST_PATH_IMAGE011
d parameters of various die names at different speeds are represented;
Figure 600593DEST_PATH_IMAGE012
representing the genetic algorithm calculation D parameters of various die names at different speeds; gamma represents the adaptive term coefficient of the D parameter;
step two, pair
Figure 643636DEST_PATH_IMAGE008
、
Figure 124296DEST_PATH_IMAGE010
And
Figure 947895DEST_PATH_IMAGE012
carrying out encoding;
Figure 867310DEST_PATH_IMAGE008
the parameter coding range is
Figure 827175DEST_PATH_IMAGE013
;
Figure 947578DEST_PATH_IMAGE014
The parameter coding range is
Figure 727315DEST_PATH_IMAGE015
；
Figure 388104DEST_PATH_IMAGE012
The parameter coding range is
Figure 31837DEST_PATH_IMAGE016
；
Figure 119879DEST_PATH_IMAGE008
、
Figure 386912DEST_PATH_IMAGE017
And
Figure 789074DEST_PATH_IMAGE012
represented by three binary code strings of length 10 bits, thus forming a binary code string of length 30 bits, the middle 10 bits being
Figure 723532DEST_PATH_IMAGE018
Binary coded string with the last 10 bits being
Figure 44792DEST_PATH_IMAGE012
A binary encoding string;
step three, establishing a speed control evaluation function:
Figure 64701DEST_PATH_IMAGE019
；
t1 represents peak time, t2 represents adjusting time, s represents overshoot, e represents steady-state error, the four parameters are calculated values related to the real-time speed of the hydraulic press for each forging, a represents a peak time coefficient, b represents an adjusting time coefficient, c represents an overshoot coefficient, d represents a steady-state error coefficient, and the four parameters are fixed values set for debugging;
step four, performing one genetic algorithm operation in each working process of the equipment, wherein a proportion selection operator is adopted in the selection operation, a single-point interchange operator is adopted in the interchange operation, a basic bit mutation operator is adopted in the mutation operation, and the following parameters of the genetic algorithm are set: the population size, the termination of evolution algebra, the interchange probability and the mutation probability;
step five, pair
Figure 4975DEST_PATH_IMAGE008
、
Figure 528360DEST_PATH_IMAGE010
And
Figure 223784DEST_PATH_IMAGE012
after parameter coding and population initialization, calculating the fitness of each individual according to the working parameters and the speed evaluation function of the forging hydraulic press, if the termination condition is not met, carrying out genetic operation to update the population, and if the termination condition is met, carrying out parameter decoding and finishing parameter optimization.
A second object of the present invention is to provide a speed adaptive control system of a hydraulic forging press, comprising:
debugging mouldBlock (2): in the equipment debugging stage, the sectional regulation of typical speed is selected, closed-loop control is carried out through a discrete PID control algorithm, and the PID parameters of the basic working speed of different speed sections are determined:
Figure 793305DEST_PATH_IMAGE020
、
Figure 334008DEST_PATH_IMAGE021
and
Figure 180741DEST_PATH_IMAGE022
；
an operation module: in the equipment production operation stage, the PID parameters are automatically adjusted according to the resistance of different forging hydraulic presses in the production of workpieces made of different materials and the working speeds of different fire numbers of the same workpiece.
Preferably, the implementation process of the running module is as follows:
step one, P, I and D parameters are calculated;
Figure 515908DEST_PATH_IMAGE023
；
Figure 510409DEST_PATH_IMAGE024
；
Figure 917119DEST_PATH_IMAGE025
；
wherein:
Figure 680676DEST_PATH_IMAGE026
representing P parameters of various die names at different speeds;
Figure 452323DEST_PATH_IMAGE027
representing the genetic algorithm calculation P parameter of various die names at different speeds; alpha represents the adaptive term coefficient of the P parameter;
Figure 606224DEST_PATH_IMAGE028
i parameters of various die names at different speeds are represented;
Figure 754308DEST_PATH_IMAGE029
representing the genetic algorithm calculation I parameters of various die names at different speeds; beta represents the adaptive term coefficient of the I parameter;
Figure 933223DEST_PATH_IMAGE030
d parameters of various die names at different speeds are represented;
Figure 141351DEST_PATH_IMAGE031
representing the genetic algorithm calculation D parameters of various die names at different speeds; gamma represents the adaptive term coefficient of the D parameter;
step two, pair
Figure 579285DEST_PATH_IMAGE027
、
Figure 203165DEST_PATH_IMAGE032
And
Figure 941314DEST_PATH_IMAGE031
carrying out encoding;
Figure 382659DEST_PATH_IMAGE027
the parameter coding range is
Figure 307890DEST_PATH_IMAGE033
；
Figure 797777DEST_PATH_IMAGE032
The parameter coding range is
Figure 62536DEST_PATH_IMAGE034
；
Figure 612466DEST_PATH_IMAGE035
The parameter coding range is
Figure 352889DEST_PATH_IMAGE036
；
Figure 380888DEST_PATH_IMAGE027
、
Figure 828050DEST_PATH_IMAGE032
And
Figure 220985DEST_PATH_IMAGE031
represented by three binary code strings of length 10 bits, thus forming a binary code string of length 30 bits, the middle 10 bits being
Figure 386387DEST_PATH_IMAGE032
Binary coded string with the last 10 bits being
Figure 280394DEST_PATH_IMAGE037
A binary encoding string;
step three, establishing a speed control evaluation function:
Figure 582062DEST_PATH_IMAGE038
；
t1 represents peak time, t2 represents adjusting time, s represents overshoot, e represents steady-state error, the four parameters are calculated values related to the real-time speed of the hydraulic press for each forging, a represents a peak time coefficient, b represents an adjusting time coefficient, c represents an overshoot coefficient, d represents a steady-state error coefficient, and the four parameters are fixed values set for debugging;
step four, performing one genetic algorithm operation in each working process of the equipment, wherein a proportion selection operator is adopted in the selection operation, a single-point interchange operator is adopted in the interchange operation, a basic bit mutation operator is adopted in the mutation operation, and the following parameters of the genetic algorithm are set: the population size, the termination of evolution algebra, the interchange probability and the mutation probability;
step five, pair
Figure 208216DEST_PATH_IMAGE039
、
Figure 798597DEST_PATH_IMAGE040
And
Figure 433978DEST_PATH_IMAGE041
after parameter coding and population initialization, calculating the fitness of each individual according to the working parameters and the speed evaluation function of the forging hydraulic press, if the termination condition is not met, carrying out genetic operation to update the population, and if the termination condition is met, carrying out parameter decoding and finishing parameter optimization.
The invention has the advantages and positive effects that:
firstly, because the resistance of different forging hydraulic presses is different when producing titanium alloy, high-temperature alloy, powder alloy and aluminum alloy products, the working speeds of different fire numbers of the same product are different, and the working speed of each section of each product cannot be completely verified in the debugging stage.
The invention provides an initial value of the PID parameter at the debugging stage of the equipment, and different optimized PID parameters are provided for different die names and different pressing speeds by adopting a genetic algorithm, so that the robustness and the accuracy of the system are improved.
Drawings
FIG. 1 is a schematic flow diagram of a preferred embodiment of the present invention.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings:
the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by those skilled in the art without creative efforts based on the technical solutions of the present invention belong to the protection scope of the present invention.
Please refer to fig. 1.
A self-adaptive control method for the speed of a hydraulic forging press comprises the following steps of firstly, determining a basic speed PID parameter in an equipment debugging stage; then, correcting and optimizing speed PID parameters of various products through a genetic algorithm in the equipment production and operation process; finally, after long-term operation, a self-adaptive parameter library of various products is obtained, and the system can automatically match PID parameters according to the process parameters, so that the forging process speed is controlled more accurately.
Wherein the second step and the third step specifically comprise the following steps:
firstly, in the equipment debugging stage, the sectional regulation of typical speed is selected, the control system carries out closed-loop control through a discrete PID control algorithm, and the PID parameters of the basic working speed of different speed sections are determined
Figure 324573DEST_PATH_IMAGE042
、
Figure 950989DEST_PATH_IMAGE043
And
Figure 825404DEST_PATH_IMAGE044
。
and secondly, in the equipment production operation stage, because resistance of different forging hydraulic presses is different when producing titanium alloy, high-temperature alloy, powder alloy and aluminum alloy products, the working speeds of different fire times of the same product are different, and the working speed of each section of each product cannot be completely verified in the debugging stage, so that the PID parameters need to be automatically set.
The PID parameters are automatically adjusted according to the die name (corresponding to the parts made of different materials) and the fire number (corresponding to different pressing speeds). The method comprises the following steps:
step one, P, I and calculation of the D parameter.
Figure 202159DEST_PATH_IMAGE045
；
Figure 212840DEST_PATH_IMAGE046
Representing P parameters of various die names at different speeds;
Figure 446375DEST_PATH_IMAGE047
representing the genetic algorithm calculation P parameter of various die names at different speeds;
α represents the adaptive term coefficient of the P parameter (the commissioning engineer set point, typically 5% to 20%).
Figure 870404DEST_PATH_IMAGE048
；
Figure 847587DEST_PATH_IMAGE049
And the I parameter of each type of die name at different speeds is shown.
Figure 916037DEST_PATH_IMAGE050
And (3) representing the calculation of I parameters of the genetic algorithm of various die names at different speeds.
Beta represents the adaptive term coefficient of the I parameter (the commissioning engineer set value, typically 5% to 20%).
Figure 54894DEST_PATH_IMAGE051
；
Figure 903902DEST_PATH_IMAGE052
And D parameters of various types of die names at different speeds are shown.
Figure 481514DEST_PATH_IMAGE053
And (3) representing the calculation of the D parameter of the genetic algorithm of various die names at different speeds.
Gamma denotes the adaptive term coefficient of the D parameter (the commissioning engineer set point, typically 5% to 20%).
Step two,
Figure 732366DEST_PATH_IMAGE054
、
Figure 245387DEST_PATH_IMAGE055
And
Figure 316111DEST_PATH_IMAGE053
the coding method of (1).
Figure 635097DEST_PATH_IMAGE056
The parameter coding range is
Figure 802773DEST_PATH_IMAGE057
；
Figure 549013DEST_PATH_IMAGE055
The parameter coding range is
Figure 44716DEST_PATH_IMAGE058
；
Figure 901814DEST_PATH_IMAGE053
The parameter coding range is
Figure 861679DEST_PATH_IMAGE059
；
Figure 357250DEST_PATH_IMAGE056
Parameter taking
Figure 668145DEST_PATH_IMAGE060
As the maximum value of the encoding, there is,
Figure 63355DEST_PATH_IMAGE061
as the minimum value of the encoding.
Figure 815410DEST_PATH_IMAGE055
Parameter taking
Figure 169031DEST_PATH_IMAGE062
As a maximum value of the encoding, there is,
Figure 763960DEST_PATH_IMAGE063
as the minimum value of the encoding.
Figure 228440DEST_PATH_IMAGE053
Parameter taking
Figure 631739DEST_PATH_IMAGE052
As the maximum value of the encoding, there is,
Figure 93945DEST_PATH_IMAGE064
as the minimum value of the encoding.
Figure 113853DEST_PATH_IMAGE056
、
Figure 178761DEST_PATH_IMAGE055
And
Figure 967726DEST_PATH_IMAGE053
represented by three binary code strings with the length of 10 bits, thereby forming a binary code string with the length of 30 bits, namely the first 10 bits of the binary code string with the length of 30 bits are
Figure 600832DEST_PATH_IMAGE065
Binary code string with 10 bits in the middle
Figure 842458DEST_PATH_IMAGE066
Two-inMaking code string with the last 10 bits as
Figure 711057DEST_PATH_IMAGE053
A binary encoded string.
Step three, speed control evaluation function expression:
Figure 620107DEST_PATH_IMAGE067
；
wherein t1 represents the peak time, t2 represents the adjustment time, s represents the overshoot, e represents the steady-state error, and the above four parameters are calculated values related to the real-time speed of the hydraulic press for each forging. a represents a peak time coefficient, b represents an adjusting time coefficient, c represents an overshoot coefficient, d represents a steady-state error coefficient, and the four parameters are set fixed values for debugging personnel.
And step four, performing operation of a genetic algorithm once in each working process of the equipment. The selection operation adopts a proportion selection operator, the interchange operation adopts a single-point interchange operator, and the mutation operation adopts a basic bit mutation operator. Parameters of the genetic algorithm: the population size is 60, the number of evolution generations is 100, the interchange probability is 0.5, and the mutation probability is 0.05.
Step five, pair
Figure 158536DEST_PATH_IMAGE054
、
Figure 887457DEST_PATH_IMAGE055
And
Figure 231851DEST_PATH_IMAGE053
after parameter coding and population initialization, calculating the fitness of each individual according to the working parameters and the speed evaluation function of the forging hydraulic press, if the termination condition is not met, carrying out genetic operation to update the population, and if the termination condition is met, carrying out parameter decoding and finishing parameter optimization.
A speed adaptive control system for a hydraulic forging press, comprising:
a debugging module: in the equipment debugging phase, selectingAnd (3) regulating the typical speed in sections, performing closed-loop control through a discrete PID control algorithm, and determining basic working speed PID parameters of different speed sections:
Figure 559189DEST_PATH_IMAGE068
、
Figure 330836DEST_PATH_IMAGE069
and
Figure 547054DEST_PATH_IMAGE070
；
an operation module: in the equipment production operation stage, the PID parameters are automatically adjusted according to the resistance of different forging hydraulic presses in the production of workpieces made of different materials and the working speeds of different fire numbers of the same workpiece.
The implementation process of the operation module is as follows:
step one, P, I and D parameter calculation:
Figure 632822DEST_PATH_IMAGE071
；
Figure 250885DEST_PATH_IMAGE072
representing P parameters of various die names at different speeds;
Figure 521329DEST_PATH_IMAGE073
representing the genetic algorithm calculation P parameters of various die names at different speeds;
α represents the adaptive term coefficient of the P parameter (the commissioning engineer set point, typically 5% to 20%).
Figure 959264DEST_PATH_IMAGE074
；
Figure 848722DEST_PATH_IMAGE075
And the I parameter of each type of die name at different speeds is shown.
Figure 321292DEST_PATH_IMAGE076
And (3) representing the calculation of I parameters of the genetic algorithm of various die names at different speeds.
Beta represents the adaptive term coefficient of the I parameter (the commissioning engineer set value, typically 5% to 20%).
Figure 434741DEST_PATH_IMAGE077
；
Figure 687868DEST_PATH_IMAGE078
And D parameters of various types of die names at different speeds are shown.
Figure 177755DEST_PATH_IMAGE053
And (3) representing the calculation of the D parameter of the genetic algorithm of various die names at different speeds.
Gamma denotes the adaptive term coefficient of the D parameter (the commissioning engineer set point, typically 5% to 20%).
Step two,
Figure 504831DEST_PATH_IMAGE054
、
Figure 992445DEST_PATH_IMAGE055
And
Figure 670551DEST_PATH_IMAGE053
the coding method of (1).
Figure 760866DEST_PATH_IMAGE056
The parameter coding range is
Figure 942449DEST_PATH_IMAGE079
；
Figure 663280DEST_PATH_IMAGE055
The parameter coding range is
Figure 766366DEST_PATH_IMAGE080
；
Figure 598055DEST_PATH_IMAGE053
The parameter coding range is
Figure 460576DEST_PATH_IMAGE081
；
Figure 352308DEST_PATH_IMAGE056
Parameter taking
Figure 739427DEST_PATH_IMAGE060
As the maximum value of the encoding, there is,
Figure 46912DEST_PATH_IMAGE082
as the minimum value of the encoding.
Figure 468666DEST_PATH_IMAGE055
Parameter taking
Figure 593617DEST_PATH_IMAGE062
As the maximum value of the encoding, there is,
Figure 468032DEST_PATH_IMAGE083
as the minimum value of the encoding.
Figure 844787DEST_PATH_IMAGE053
Parameter taking
Figure 855468DEST_PATH_IMAGE052
As the maximum value of the encoding, there is,
Figure 89003DEST_PATH_IMAGE084
as the minimum value of the encoding.
Figure 513031DEST_PATH_IMAGE056
、
Figure 224635DEST_PATH_IMAGE055
And
Figure 558665DEST_PATH_IMAGE053
represented by three binary code strings with the length of 10 bits, thereby forming a binary code string with the length of 30 bits, namely the first 10 bits of the binary code string with the length of 30 bits are
Figure 963101DEST_PATH_IMAGE065
Binary code string with 10 bits in the middle
Figure 874425DEST_PATH_IMAGE066
Binary coded string with the last 10 bits being
Figure 124141DEST_PATH_IMAGE053
A binary encoded string.
Step three, speed control evaluation function expression:
Figure 374994DEST_PATH_IMAGE085
；
wherein t1 represents the peak time, t2 represents the adjustment time, s represents the overshoot, e represents the steady-state error, and the above four parameters are calculated values related to the real-time speed of the hydraulic press for each forging. a represents a peak time coefficient, b represents an adjusting time coefficient, c represents an overshoot coefficient, d represents a steady-state error coefficient, and the four parameters are set fixed values for debugging personnel.
And step four, performing operation of a genetic algorithm once in each working process of the equipment. The selection operation adopts a proportion selection operator, the interchange operation adopts a single-point interchange operator, and the mutation operation adopts a basic bit mutation operator. Parameters of the genetic algorithm: the population size is 60, the number of evolution generations is 100, the interchange probability is 0.5, and the mutation probability is 0.05.
Step five, pair
Figure 888015DEST_PATH_IMAGE054
、
Figure 224318DEST_PATH_IMAGE055
And
Figure 107086DEST_PATH_IMAGE053
after parameter coding and population initialization, calculating the fitness of each individual according to the working parameters and the speed evaluation function of the forging hydraulic press, if the termination condition is not met, carrying out genetic operation to update the population, and if the termination condition is met, carrying out parameter decoding and finishing parameter optimization.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.

Claims (10)

1. A self-adaptive speed control method for a hydraulic forging press is characterized by comprising the following steps:
s1, in the equipment debugging stage, selecting the sectional regulation of the typical speed, carrying out closed-loop control through a discrete PID control algorithm, and determining the basic working speed PID parameters of different speed sections:
Figure 283751DEST_PATH_IMAGE001
、
Figure 688187DEST_PATH_IMAGE002
and
Figure 333932DEST_PATH_IMAGE003
；
and S2, in the equipment production operation stage, automatically setting PID parameters according to the resistance of different forging hydraulic presses in producing different material products and the working speeds of different fire times of the same product.
2. The adaptive speed control method for hydraulic forging presses as claimed in claim 1, wherein S2 is embodied as follows:
step one, P, I and D parameters are calculated;
Figure 114807DEST_PATH_IMAGE004
；
Figure 100080DEST_PATH_IMAGE005
；
Figure 613101DEST_PATH_IMAGE006
；
wherein:
Figure 683825DEST_PATH_IMAGE007
representing P parameters of various die names at different speeds;
Figure 566593DEST_PATH_IMAGE008
representing the genetic algorithm calculation P parameter of various die names at different speeds; alpha represents the adaptive term coefficient of the P parameter;
Figure 406373DEST_PATH_IMAGE009
i parameters of various die names at different speeds are represented;
Figure 152612DEST_PATH_IMAGE010
representing the genetic algorithm calculation I parameters of various die names at different speeds; beta represents the adaptive term coefficient of the I parameter;
Figure 913895DEST_PATH_IMAGE011
d parameters of various die names at different speeds are represented;
Figure 770992DEST_PATH_IMAGE012
representing the genetic algorithm calculation D parameters of various die names at different speeds; gamma represents the adaptive term coefficient of the D parameter;
step two, pair
Figure 730858DEST_PATH_IMAGE013
、
Figure 710315DEST_PATH_IMAGE014
And
Figure 755632DEST_PATH_IMAGE012
carrying out encoding;
Figure 354103DEST_PATH_IMAGE015
the parameter coding range is
Figure 434055DEST_PATH_IMAGE016
；
Figure 584413DEST_PATH_IMAGE014
The parameter coding range is
Figure 851447DEST_PATH_IMAGE017
；
Figure 315926DEST_PATH_IMAGE012
The parameter coding range is
Figure 188067DEST_PATH_IMAGE018
；
Figure 447010DEST_PATH_IMAGE015
、
Figure 529236DEST_PATH_IMAGE014
And
Figure 531827DEST_PATH_IMAGE012
represented by three binary code strings of length 10 bits, thus forming a binary code string of length 30 bits, the middle 10 bits being
Figure 320791DEST_PATH_IMAGE019
Binary coded string with the last 10 bits being
Figure 688319DEST_PATH_IMAGE020
A binary encoding string;
step three, establishing a speed control evaluation function:
Figure 195523DEST_PATH_IMAGE021
；
t1 represents peak time, t2 represents adjusting time, s represents overshoot, e represents steady-state error, the four parameters are calculated values related to the real-time speed of the hydraulic press for each forging, a represents a peak time coefficient, b represents an adjusting time coefficient, c represents an overshoot coefficient, d represents a steady-state error coefficient, and the four parameters are fixed values set for debugging;
step four, performing one genetic algorithm operation in each working process of the equipment, wherein a proportion selection operator is adopted in the selection operation, a single-point interchange operator is adopted in the interchange operation, a basic bit mutation operator is adopted in the mutation operation, and the following parameters of the genetic algorithm are set: the population size, the termination of evolution algebra, the interchange probability and the mutation probability;
step five, pair
Figure 736226DEST_PATH_IMAGE013
、
Figure 940549DEST_PATH_IMAGE014
And
Figure 806874DEST_PATH_IMAGE012
after parameter coding and population initialization, calculating the fitness of each individual according to the working parameters and the speed evaluation function of the forging hydraulic press, if the termination condition is not met, carrying out genetic operation to update the population, and if the termination condition is met, carrying out parameter decoding and finishing parameter optimization.
3. The adaptive speed control method for a forging hydraulic press according to claim 2, wherein the different material parts include titanium alloy, high temperature alloy, powder alloy and aluminum alloy parts.
4. The adaptive forging hydraulic press speed control method according to claim 2, wherein a range of α is 5% to 20%, a range of β is 5% to 20%, and a range of γ is 5% to 20%.
5. The adaptive control method for the speed of a forging hydraulic press according to claim 2, wherein in step four, the parameters of the genetic algorithm include: the population size is 60, the number of final evolutionary generations is 100, the interchange probability is 0.5, and the mutation probability is 0.05.
6. A speed adaptive control system for a hydraulic forging press, comprising:
a debugging module: in the equipment debugging stage, the sectional regulation of typical speed is selected, closed-loop control is carried out through a discrete PID control algorithm, and the PID parameters of the basic working speed of different speed sections are determined:
Figure 473479DEST_PATH_IMAGE022
、
Figure 817872DEST_PATH_IMAGE023
and
Figure 315850DEST_PATH_IMAGE024
；
an operation module: in the equipment production operation stage, the PID parameters are automatically adjusted according to the resistance of different forging hydraulic presses in the production of workpieces made of different materials and the working speeds of different fire numbers of the same workpiece.
7. The adaptive speed control system for hydraulic forging presses of claim 6, wherein the operation module is implemented by:
step one, P, I and D parameters are calculated;
Figure 415393DEST_PATH_IMAGE025
；
Figure 631610DEST_PATH_IMAGE026
；
Figure 717378DEST_PATH_IMAGE027
；
wherein:
Figure 69862DEST_PATH_IMAGE028
representing P parameters of various die names at different speeds;
Figure 277990DEST_PATH_IMAGE008
representing the genetic algorithm calculation P parameters of various die names at different speeds; alpha represents the adaptive term coefficient of the P parameter;
Figure 43820DEST_PATH_IMAGE029
i parameters of various die names at different speeds are represented;
Figure 995596DEST_PATH_IMAGE010
representing the genetic algorithm calculation I parameters of various die names at different speeds; beta represents the adaptive term coefficient of the I parameter;
Figure 405848DEST_PATH_IMAGE030
d parameters of various die names at different speeds are represented;
Figure 519298DEST_PATH_IMAGE012
representing the genetic algorithm calculation D parameters of various die names at different speeds; gamma represents the adaptive term coefficient of the D parameter;
step two, pair
Figure 710108DEST_PATH_IMAGE013
、
Figure 262312DEST_PATH_IMAGE014
And
Figure 854967DEST_PATH_IMAGE012
carrying out encoding;
Figure 77001DEST_PATH_IMAGE015
the parameter coding range is
Figure 489528DEST_PATH_IMAGE031
；
Figure 783106DEST_PATH_IMAGE014
The parameter coding range is
Figure 794050DEST_PATH_IMAGE032
；
Figure 514881DEST_PATH_IMAGE012
Parameter(s)The coding range is
Figure 149125DEST_PATH_IMAGE033
；
Figure 918498DEST_PATH_IMAGE015
、
Figure 485745DEST_PATH_IMAGE014
And
Figure 439795DEST_PATH_IMAGE012
represented by three binary code strings of length 10 bits, thus forming a binary code string of length 30 bits, the middle 10 bits being
Figure 561334DEST_PATH_IMAGE014
Binary coded string with the last 10 bits being
Figure 196715DEST_PATH_IMAGE034
A binary encoding string;
step three, establishing a speed control evaluation function:
Figure 556152DEST_PATH_IMAGE035
；
t1 represents peak time, t2 represents adjusting time, s represents overshoot, e represents steady-state error, the four parameters are calculated values related to the real-time speed of the hydraulic press for each forging, a represents a peak time coefficient, b represents an adjusting time coefficient, c represents an overshoot coefficient, d represents a steady-state error coefficient, and the four parameters are fixed values set for debugging;
step four, performing one genetic algorithm operation in each working process of the equipment, wherein a proportion selection operator is adopted in the selection operation, a single-point interchange operator is adopted in the interchange operation, a basic bit mutation operator is adopted in the mutation operation, and the following parameters of the genetic algorithm are set: the population size, the termination of evolution algebra, the interchange probability and the mutation probability;
step five, pair
Figure 618786DEST_PATH_IMAGE013
、
Figure 555518DEST_PATH_IMAGE014
And
Figure 463431DEST_PATH_IMAGE012
after parameter coding and population initialization, calculating the fitness of each individual according to the working parameters and the speed evaluation function of the forging hydraulic press, if the termination condition is not met, carrying out genetic operation to update the population, and if the termination condition is met, carrying out parameter decoding and finishing parameter optimization.
8. The adaptive speed control system for hydraulic forging presses of claim 7, wherein α ranges from 5% to 20%, β ranges from 5% to 20%, and γ ranges from 5% to 20%.
9. The adaptive speed control system for a hydraulic forging press according to claim 7, wherein the different material parts include titanium alloy, high temperature alloy, powder alloy and aluminum alloy parts.
10. The adaptive speed control system for hydraulic forging presses of claim 7, wherein in step four, the parameters of the genetic algorithm include: the population size is 60, the number of final evolutionary generations is 100, the interchange probability is 0.5, and the mutation probability is 0.05.
CN202210791370.6A 2022-07-07 2022-07-07 Self-adaptive control method and system for speed of hydraulic forging press Pending CN114859735A (en)

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