CN110568761A - Fuzzy control-based feeding speed online optimization method - Google Patents

Fuzzy control-based feeding speed online optimization method Download PDF

Info

Publication number
CN110568761A
CN110568761A CN201910954545.9A CN201910954545A CN110568761A CN 110568761 A CN110568761 A CN 110568761A CN 201910954545 A CN201910954545 A CN 201910954545A CN 110568761 A CN110568761 A CN 110568761A
Authority
CN
China
Prior art keywords
fuzzy
power
error
output
control
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201910954545.9A
Other languages
Chinese (zh)
Inventor
吴宝海
张阳
郑志阳
夏卫红
罗明
张莹
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Northwestern Polytechnical University
Northwest University of Technology
Original Assignee
Northwest University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Northwest University of Technology filed Critical Northwest University of Technology
Priority to CN201910954545.9A priority Critical patent/CN110568761A/en
Publication of CN110568761A publication Critical patent/CN110568761A/en
Pending legal-status Critical Current

Links

Classifications

    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention discloses a fuzzy control-based feeding speed online optimization method, which is used for solving the technical problem of poor practicability of the existing feeding speed online control method. The method has the advantages that the constant power fuzzy controller is manufactured, the fuzzy control algorithm is applied, and the constant power control of the cutting process is realized on a control system which is complicated and changeable and is difficult to express by an accurate mathematical model.

Description

Fuzzy control-based feeding speed online optimization method
Technical Field
the invention relates to a feeding speed online control method, in particular to a feeding speed online optimization method based on fuzzy control.
Background
In the era of rapid development of the current numerical control machining technology, optimization of machining process parameters is beneficial to exerting the optimal driving performance of a machine tool and the optimal cutting performance of a cutter, so that the machining quality is improved, the cost is reduced, and the energy efficiency is improved. In actual numerical control machining, the number of process parameters which can be optimized is not large, for example, the rotating speed or the feeding speed of the main shaft is adjusted to optimize the machining process, however, the rotating speed of the main shaft reflects the movement of the main shaft of the machine tool, and the machine tool is damaged due to frequent change. Therefore, the method for optimizing the process parameters by optimizing the feeding speed is the most direct and effective method for improving the processing efficiency.
The document "thin-wall part side milling deformation on-line control based on finite element numerical model and feed speed optimization, mechanical engineering report, 2017, Vol.53, No.21, p 190-199" proposes an on-line control method based on finite element numerical model and feed speed optimization, and according to the finite element simulation result of the thin-wall part cutting process, establishes numerical models among the feed speed, cutting force and workpiece cutting deformation of a numerical control machine tool, and further determines the optimal target cutting force for controlling deformation. The cutting force signal real-time acquisition, filtering function and feeding speed online optimization strategy are developed on an open type modularized numerical control system platform, the feeding speed of a machine tool is adjusted in real time in the machining process according to the filtered cutting force and a corresponding algorithm, and the cutting force is ensured to be gradually close to an optimal control target so as to realize online control of cutting deformation. The numerical model between the feeding speed and the cutting force established in the document is obtained through finite element simulation, so that the optimization effect depends on the accuracy of the numerical simulation, the numerical model does not consider the machining parameters such as the cutting width, the cutting depth and the like, the numerical model is not suitable for multi-axis numerical control machining of a complex curved surface, and the application of the numerical model is limited.
Disclosure of Invention
In order to overcome the defect that the existing online control method for the feeding speed is poor in practicability, the invention provides an online optimization method for the feeding speed based on fuzzy control. The method realizes the constant power control of the cutting process on a control system which is complex and changeable and is difficult to express by an accurate mathematical model by manufacturing a constant power fuzzy controller and applying a fuzzy control algorithm.
the technical scheme adopted by the invention for solving the technical problems is as follows: a fuzzy control-based feeding speed online optimization method is characterized by comprising the following steps:
step one, input and output of a fuzzy controller are determined. Setting a target cutting power Pobjcollecting the main shaft power of the machine tool in the machining process, and comparing the actual machining power with a target power value PobjComparing to obtain power error EPWhile differentiating the error to calculate the power error change rate C in a sampling periodPError in power EPAnd rate of change of power error CPAnd as an input variable of the fuzzy controller, taking the feed multiplying power of the numerical control machine as an output quantity.
and step two, performing input and output fuzzification processing. The input and output accurate value variable is correspondingly fuzzified and described by seven words, namely { negative large, negative medium, negative small, zero, positive small, positive medium, positive large }, and the input and output variables of the fuzzy control are set as follows:
The fuzzy set of Ep is { NB, NM, NS, N0, 0, PS, PM, PB };
The fuzzy set of Cp is { NB, NM, NS, N0, 0, PS, PM, PB };
the fuzzy set of Δ U is { NB, NM, NS, 0, PS, PM, PB };
The domains of Ep, Cp and delta U are { -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6 };
and step three, determining a membership function of the fuzzy subset. The input variable adopts a triangular membership function, and the output variable adopts a trapezoidal membership function.
Step four, formulating a fuzzy control rule. The conditional statement control rule of If … then is used.
and step five, converting the output fuzzy set into a determined numerical value for output by adopting a defuzzification method of a gravity center method, manufacturing a fuzzy lookup table, and obtaining corresponding output control quantity for given input through the lookup table.
and step six, determining parameters of the fuzzy controller. An input amount spindle power change interval, a spindle power change rate change interval and a feed magnification change interval as an output amount are determined. And simultaneously, reasonable scale factors and quantization factors are selected, when the discourse domain of the error is set to be [ -x, + x ], the fuzzy set discourse domain of the error is { -n, -n +1, …, 0, …, n-1, n }, and the quantization factor K of the error is expressed as the following formula:
Wherein n is the maximum value of the universe of discourse of the error fuzzy set, and x is the maximum value of the universe of discourse of the error.
And seventhly, manufacturing a fuzzy control look-up table. Manufacturing a simple fuzzy controller in a SIMULINK module of MATLAB software, loading the previously established fuzzy rule into a working space, mapping input and output variables to corresponding input and output of the fuzzy controller in a system test, running the test, storing a result, and performing format conversion on the result to obtain a corresponding fuzzy control query table.
and step eight, replacing the fuzzy controller in the online optimization process with the fuzzy control query table, establishing an online optimization model based on fuzzy control, and programming the online optimization model to be built in the numerical control machine.
And ninthly, optimizing and debugging on line. Real-time milling power data are collected through a numerical control milling workpiece, the feeding multiplying power is adaptively controlled by using an online optimization controller so as to adjust the feeding speed, and the adjusted power is fed back to a constant power controller to be continuously adjusted in an iterative manner until the power is constant.
The invention has the beneficial effects that: the method realizes the constant power control of the cutting process on a control system which is complex and changeable and is difficult to express by an accurate mathematical model by manufacturing a constant power fuzzy controller and applying a fuzzy control algorithm.
the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
drawings
FIG. 1 is a flow chart of the fuzzy control-based feed speed online optimization method of the present invention.
FIG. 2 is a graph of membership functions for input and output variables in the method of the present invention.
FIG. 3 is a diagram of a fuzzy control-based simulation model for online optimization of feed speed in the method of the present invention.
FIG. 4 is a comparison graph of the simulation results of the fuzzy control-based on-line optimization of the feeding speed in the method of the present invention.
Detailed Description
reference is made to fig. 1-4. The invention relates to a fuzzy control-based on-line optimization method of a feeding speed, which comprises the following specific steps:
step one, input and output of a fuzzy controller are determined. Setting a target cutting power PobjCollecting the main shaft power of the machine tool in the machining process, and comparing the actual machining power with a target power value Pobjmaking a comparison to obtain a power error EPWhile differentiating the error to calculate the power error change rate C in a sampling periodPThus, the power error EPAnd rate of change of power error CPAnd as an input variable of the fuzzy controller, taking the feed multiplying power of the numerical control machine as an output quantity.
And step two, performing input and output fuzzification processing. The corresponding fuzzification processing is carried out on the input and output accurate value variable, and seven words are used for description, namely { negative large, negative medium, negative small, zero, positive small, positive medium, positive large }, so that the input and output variables of the fuzzy control are set as follows:
The fuzzy set of Ep is { NB, NM, NS, N0, 0, PS, PM, PB };
the fuzzy set of Cp is { NB, NM, NS, N0, 0, PS, PM, PB };
The fuzzy set of Δ U is { NB, NM, NS, 0, PS, PM, PB };
The domains of Ep, Cp and delta U are { -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6 };
and step three, determining a membership function of the fuzzy subset. The input variable adopts a triangular membership function, the shape is simple, the calculation workload is less, the storage space is saved, and the control sensitivity is high. The output variable uses a trapezoidal membership function, so that the fuzzy controller has good control performance on the nonlinear system.
step four, formulating a fuzzy control rule. The conditional statement control rule of If … then is used. When the error and the error change rate are large or large, the selected control quantity is mainly used for eliminating the error; when the error and the error change rate are small, the selected control quantity needs to be paid attention to prevent overshoot, and the stability of the system is taken as a main consideration; when the error and the error change rate are positive or both negative, the corresponding sign needs to be changed for the main purpose of eliminating the error.
And step five, converting the output fuzzy set into a determined numerical value for output by adopting a defuzzification method of a gravity center method, manufacturing a fuzzy lookup table, and obtaining corresponding output control quantity for given input through the lookup table.
And step six, determining parameters of the fuzzy controller. An input amount spindle power change interval, a spindle power change rate change interval and a feed magnification change interval as an output amount are determined. Meanwhile, reasonable scale factors and quantization factors need to be selected, when the domain of the error is set to be [ -x, + x ], the domain of the fuzzy set of the error is { -n, -n +1, …, 0, …, n-1, n }, and therefore the quantization factor K of the error is expressed as the following formula:
Where n is the maximum value of the universe of discourse of the error fuzzy set and x is the maximum value of the universe of discourse of the error. Similarly, the quantization factor of the error change rate and the scale factor of the output control amount can be obtained by the above method. However, the values of these control parameters need to be corrected to a certain extent according to the processing experiment to achieve a certain control effect.
And seventhly, manufacturing a fuzzy control look-up table. Manufacturing a simple fuzzy controller in a SIMULINK module of MATLAB software, loading the previously established fuzzy rule into a working space, mapping input and output variables to corresponding input and output of the fuzzy controller in a system test, running the test, storing a result, and performing format conversion on the result to obtain a corresponding fuzzy control query table.
And step eight, replacing the fuzzy controller in the online optimization process with the fuzzy control query table, establishing an online optimization model based on fuzzy control, and programming the online optimization model to be built in the numerical control machine.
And ninthly, optimizing and debugging on line. Real-time milling power data are collected through a numerical control milling workpiece, the feeding multiplying power is adaptively controlled by using an online optimization controller so as to adjust the feeding speed, and the adjusted power is fed back to a constant power controller to be continuously adjusted in an iterative manner until the power is constant.
application examples.
step one, collecting a main shaft load in a cutting process through a built-in sensor of the numerical control machine tool, and multiplying the main shaft load by a rated power to obtain a main shaft power value of the machine tool.
and step two, determining the input and the output of the fuzzy controller. Given target cutting power PobjThe collected processing power and the target power value Pobjmaking a comparison to obtain a power error EPwhile differentiating the error to calculate the power error change rate C in a sampling periodPthus, the power error EPand rate of change of power error CPAs input variables for the fuzzy controller. And taking the feeding multiplying power of the numerical control machine as an output quantity.
Step three, performing input and output fuzzification processing. The fuzzy quantity is described by { negative large, negative medium, negative small, zero, positive small, positive medium and positive large }, and input and output variables of fuzzy control are set as follows:
The fuzzy set of Ep is { NB, NM, NS, N0, 0, PS, PM, PB };
the fuzzy set of Cp is { NB, NM, NS, N0, 0, PS, PM, PB };
the fuzzy set of Δ U is { NB, NM, NS, 0, PS, PM, PB };
the domains of Ep, Cp and delta U are { -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6 };
and step four, referring to fig. 2, the input and output membership function graph is shown, the input variable of the graph adopts a triangular membership function, the shape is simple, the calculation workload is less, the storage space is saved, and the control sensitivity is high. The output variable uses a trapezoidal membership function, so that the fuzzy controller has good control performance on the nonlinear system. Using the fuzzy logic tool box of Matlab software to complete the design of the required fuzzy logic controller, editing the previously defined E under the fuzzy logic editing windowP、CPAnd a membership function for Δ U. The two membership function selects triangle, the output membership function selects trapezoid, and the range of the membership function is defined as [ -6, 6]。
And step five, formulating a fuzzy control rule. The established fuzzy control rules are shown in table 1.
When the error is negative and the error change is positive, the change of the control quantity is negative;
When the error is negative and the error changes to positive or middle, the control quantity changes to negative or zero grade, the control quantity is not suitable to be increased too much, otherwise overshoot is caused, and positive error is generated;
When the error is negative-middle, the control quantity change is basically the same as that when the error is negative-big, in order to eliminate the error as soon as possible; fourthly, when the error is small in negative and the error change is negative, the change of the control quantity is large or medium in negative, and the error is restrained from changing towards the negative direction; when the error is negative and small, the error change is positive, the trend of the system is to eliminate the negative and small errors, and the change of the control quantity is positive and small or middle.
TABLE 1 fuzzy control rules Table
and sixthly, Defuzzification is carried out by adopting a gravity center method, a centroid is selected from a Defuzzification option in a fuzzy logic toolbox, and the system automatically adopts the gravity center method to complete Defuzzification of the control quantity.
and step seven, determining parameters of the fuzzy controller. If the domain of the assumed error is [ -x, + x ], and the domain of the fuzzy set of errors is { -n, -n +1, …, 0, …, n-1, n +1}, the quantization factor K of the error is:
Where n is the maximum value of the universe of discourse of the error fuzzy set and x is the maximum value of the universe of discourse of the error. Similarly, the quantization factor of the error variation and the scale factor of the output control amount can be obtained by the above method. Obtaining a power error quantization factor K after repeated debugging according to processing optimization10.045, power error rate of change quantization factor K20.034, outputting a feed multiplying power deblurring quantization factor K3＝0.3。
And step eight, manufacturing a fuzzy control look-up table. Manufacturing a simple fuzzy controller in a SIMULINK module of MATLAB software, loading the previously established fuzzy rule into a working space, mapping input and output variables to corresponding input and output of the fuzzy controller in a system test, running the test for 169 iterations, storing the result, and performing format conversion on the result to obtain a corresponding fuzzy control query table as shown in Table 2. A simple Fuzzy control model is newly built in Simulink, a Fuzzy Logic Controller with Ruleviewer part which is complex in operation originally is converted into a Lookup Table, and parameters of the Fuzzy control model are set as data of a Fuzzy rule query Table built in a Table 2, so that the operation efficiency is improved during Fuzzy operation, the effect is also equal to the result of operation by using a membership function, and the operation time is shortened for the operation of inputting a large amount of data.
TABLE 2 fuzzy control look-up table
and step nine, establishing an online optimization simulation model based on fuzzy control, and programming to build the online optimization simulation model in the numerical control machine tool. Mainly comprises three parts: the device comprises a power simulation module, a fuzzy control regulation module and a power feedback output module. In order to simulate the power change condition of the machine tool under the working condition of variable cutting depth, the input cutting depth is set to be in a piecewise function form, and the output power is mixed with a White noise signal (White noise) to obtain a power signal with fluctuation change through simulation. The fuzzy control regulation and control module firstly makes a difference between a power signal simulated by the power generator and a given target power value 350W, a power error and a power error change rate are pasted through a fuzzy quantization factor, the power feedback output module feeds the feeding multiplying power output by the fuzzy controller back to the power generator to generate an optimized power signal, the optimized power signal is fed back to the fuzzy control regulation and control module again after the process is completed until the power is constant, and meanwhile, a regulated feeding speed value is also output. The module belongs to the process of feedback adjustment in the machining process, constant power constraint is finally realized through continuous feedback on-line adjustment, milling is stably carried out, the machining time is shortened, and the machining efficiency is improved.
As can be seen from fig. 4, the feed speed optimization simulation model based on fuzzy control established in the above steps is used for performing a simulation experiment, and an oscilloscope is arranged in each link, so that the results of each link can be observed conveniently, and it can be seen that when the given target power value is 350W, the power at the position with a smaller power difference value is kept unchanged after optimization, the power at the position with a larger power difference value is also kept close to the target power value after optimization, and is kept relatively stable, and the power fluctuation error is kept within ± 10%, which indicates that the constant power constraint is realized on the processing power after the feed speed is optimized, so that the processing process is more stable, and the power after optimization is generally greater than the power before optimization, and indicates that the feed speed after optimization is greater than the original feed speed, the processing time is shortened when the feed speed is increased, and the processing efficiency is improved.

Claims (1)

1. A fuzzy control-based feed speed online optimization method is characterized by comprising the following steps:
Step one, determining input and output of a fuzzy controller; setting a target cutting power PobjCollecting the main shaft power of the machine tool in the machining process, and comparing the actual machining power with a target power value Pobjcomparing to obtain power error EPWhile differentiating the error to calculate the power error change rate C in a sampling periodPError in power EPAnd rate of change of power error CPAs an input variable of the fuzzy controller, taking the feed multiplying power of the numerical control machine as an output quantity;
Step two, performing input and output fuzzification processing; the input and output accurate value variable is correspondingly fuzzified and described by seven words, namely { negative large, negative medium, negative small, zero, positive small, positive medium, positive large }, and the input and output variables of the fuzzy control are set as follows:
The fuzzy set of Ep is { NB, NM, NS, N0, 0, PS, PM, PB };
The fuzzy set of Cp is { NB, NM, NS, N0, 0, PS, PM, PB };
the fuzzy set of Δ U is { NB, NM, NS, 0, PS, PM, PB };
the domains of Ep, Cp and delta U are { -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6 };
Step three, determining a membership function of the fuzzy subset; the input variable adopts a triangular membership function, and the output variable adopts a trapezoidal membership function;
step four, formulating a fuzzy control rule; adopting a conditional statement control rule of If … then;
Converting the output fuzzy set into a determined numerical value for output by adopting a defuzzification method of a gravity center method, manufacturing a fuzzy lookup table, and obtaining corresponding output control quantity for given input through the lookup table;
Step six, determining parameters of a fuzzy controller; determining an input main shaft power change interval, a main shaft power change rate change interval and a feed multiplying factor change interval as an output; and simultaneously, reasonable scale factors and quantization factors are selected, when the discourse domain of the error is set to be [ -x, + x ], the fuzzy set discourse domain of the error is { -n, -n +1, …, 0, …, n-1, n }, and the quantization factor K of the error is expressed as the following formula:
Wherein n is the maximum value of the discourse domain of the error fuzzy set, and x is the maximum value of the error discourse domain;
Seventhly, manufacturing a fuzzy control look-up table; manufacturing a simple fuzzy controller in a SIMULINK module of MATLAB software, loading a previously established fuzzy rule into a working space, mapping input and output variables to corresponding input and output of the fuzzy controller in a system test, running the test, storing a result, and performing format conversion on the result to obtain a corresponding fuzzy control query table;
Replacing a fuzzy controller in the online optimization process with a fuzzy control query table, establishing an online optimization model based on fuzzy control, and programming the online optimization model to be built in a numerical control machine;
Step nine, optimizing and debugging on line; real-time milling power data are collected through a numerical control milling workpiece, the feeding multiplying power is adaptively controlled by using an online optimization controller so as to adjust the feeding speed, and the adjusted power is fed back to a constant power controller to be continuously adjusted in an iterative manner until the power is constant.
CN201910954545.9A 2019-10-09 2019-10-09 Fuzzy control-based feeding speed online optimization method Pending CN110568761A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910954545.9A CN110568761A (en) 2019-10-09 2019-10-09 Fuzzy control-based feeding speed online optimization method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910954545.9A CN110568761A (en) 2019-10-09 2019-10-09 Fuzzy control-based feeding speed online optimization method

Publications (1)

Publication Number Publication Date
CN110568761A true CN110568761A (en) 2019-12-13

Family

ID=68784115

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910954545.9A Pending CN110568761A (en) 2019-10-09 2019-10-09 Fuzzy control-based feeding speed online optimization method

Country Status (1)

Country Link
CN (1) CN110568761A (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111930075A (en) * 2020-07-31 2020-11-13 深圳吉兰丁智能科技有限公司 Self-adaptive machining control method and non-volatile readable storage medium
CN112065359A (en) * 2020-09-21 2020-12-11 北京三一智造科技有限公司 Drilling control method and rotary drilling rig
CN112835326A (en) * 2020-12-30 2021-05-25 天津重型装备工程研究有限公司 Intelligent method and system for processing large-scale casting and forging
CN113433990A (en) * 2021-08-25 2021-09-24 深圳市中科先见医疗科技有限公司 Rapid temperature control method and system based on single chip microcomputer

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5828574A (en) * 1997-05-19 1998-10-27 General Electric Company Adaptive feedrates from geometry modeling for NC machining
CN101025620A (en) * 2006-02-24 2007-08-29 同济大学 Hierarchical fuzzy control method for numerical control milling process
CN103197596A (en) * 2013-03-14 2013-07-10 天津大学 Numerical control machining parameter adaptive fuzzy control rule optimization method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5828574A (en) * 1997-05-19 1998-10-27 General Electric Company Adaptive feedrates from geometry modeling for NC machining
CN101025620A (en) * 2006-02-24 2007-08-29 同济大学 Hierarchical fuzzy control method for numerical control milling process
CN103197596A (en) * 2013-03-14 2013-07-10 天津大学 Numerical control machining parameter adaptive fuzzy control rule optimization method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
徐剑 等: "基于开放式数控***的恒功率自适应控制研究", 《制造技术与机床》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111930075A (en) * 2020-07-31 2020-11-13 深圳吉兰丁智能科技有限公司 Self-adaptive machining control method and non-volatile readable storage medium
CN112065359A (en) * 2020-09-21 2020-12-11 北京三一智造科技有限公司 Drilling control method and rotary drilling rig
CN112065359B (en) * 2020-09-21 2023-05-16 北京三一智造科技有限公司 Drilling control method and rotary drilling rig
CN112835326A (en) * 2020-12-30 2021-05-25 天津重型装备工程研究有限公司 Intelligent method and system for processing large-scale casting and forging
CN113433990A (en) * 2021-08-25 2021-09-24 深圳市中科先见医疗科技有限公司 Rapid temperature control method and system based on single chip microcomputer

Similar Documents

Publication Publication Date Title
CN110568761A (en) Fuzzy control-based feeding speed online optimization method
Jee et al. Adaptive fuzzy logic controller for feed drives of a CNC machine tool
Guo et al. Speed control of direct current motor using ANFIS based hybrid PID configuration controller
CN109343351A (en) A kind of switched reluctance machines moment controlling system of advanced PID control
CN101339404B (en) Aircraft posture kinetics simplified model gain switching proportion-differential control design method
CN112213945B (en) Improved robust prediction control method and system for electric vehicle participating in micro-grid group frequency modulation
CN110376969A (en) Adjust device and method of adjustment
CN111474898B (en) Method for optimizing processing technological parameters of free-form surface
CN110928241B (en) Control method of motion control system of numerical control machine tool
CN114063445A (en) Design method of fuzzy controller
CN107678276A (en) A kind of adaptive composite control method based on turning table control
CN111152213B (en) Mechanical arm vibration compensation method and device based on hybrid control
CN107491036A (en) Machine tooling consumption control method and machining tool
Huang et al. A self-organising fuzzy logic controller for a coordinate machine
Ivanova et al. Fuzzy logic control design based on the genetic algorithm for a modular servo system
CN111077771A (en) Self-tuning fuzzy PID control method
CN104238359A (en) Control method of large electromechanical mixed inertia system
Xu et al. An adaptive fuzzy controller for constant cutting force in end-milling processes
CN111181467B (en) Servo motor rotating speed adjusting method and system based on three-dimensional fuzzy control
Hrehova et al. Possibility of using Matlab application to propose fuzzy computer model
JP3009755B2 (en) Processing condition generator for electric discharge
CN110850710A (en) Hydroelectric generating set control optimization method based on model-free adaptive control
Hwang et al. Mixed H/sub 2//H/sub/spl infin//design for a decentralized discrete variable structure control with application to mobile robots
CN116111614B (en) Fuzzy PID-based method for participating in isolated network frequency modulation of electrolytic aluminum load
Osman et al. adding fuzzy lookup table type method to the comparable milling machining control design based on MAT LAB/SIMULINK

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
WD01 Invention patent application deemed withdrawn after publication
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20191213