CN109901383A - A kind of AC servo machinery driving device control method - Google Patents
A kind of AC servo machinery driving device control method Download PDFInfo
- Publication number
- CN109901383A CN109901383A CN201910155056.7A CN201910155056A CN109901383A CN 109901383 A CN109901383 A CN 109901383A CN 201910155056 A CN201910155056 A CN 201910155056A CN 109901383 A CN109901383 A CN 109901383A
- Authority
- CN
- China
- Prior art keywords
- current
- axis reference
- value
- controller
- particle
- 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
Links
Abstract
The present invention provides a kind of AC servo machinery driving device control methods, comprising: S1, control AC servo motor is opened, and given rotating speed is arranged；S2 detects the rotor-position signal and phase current of AC servo motor；S3 is converted phase current to obtain d shaft current, and sets d axis reference current, obtains d axis reference voltage by current inner loop PI controller；S4, angular speed deviation is calculated according to rotor-position signal and given rotating speed, q axis reference voltage is obtained by speed outer ring PI controller and current inner loop PI controller, wherein speed outer ring PI controller, which is used, carries out parameter tuning based on multiple agent Chaos particle swarm optimization algorithm；S5 generates the SVPWM signal for driving AC servo motor according to d axis reference voltage and q axis reference voltage.Control method of the invention can not only quick response electric current, inhibit current spike, can also make AC servo machinery driving system have stronger anti-interference, improve the stability of system.
Description
Technical field
The present invention relates to motor control technology fields, and in particular to a kind of AC servo machinery driving device control method.
Background technique
In recent years, servomotor and its servo-control system are widely used in every field.Either advanced numerical control machine
The fields such as bed, industrial robot and factory automation, office automation, all be unable to do without servomotor and its SERVO CONTROL system
System.Servo motor has been widely used for various industrial production machines, such as the rigid printing machine of silk, numerically controlled small lathe, wood carving
Machine, laser cutting machine.Servo motor is also applied to the fields such as space flight and aviation, military affairs.
AC servo machinery driving device is the core control portions of servo electrical machinery system, and performance directly affects whole system
Performance.AC servo machinery driving device uses vector controlled, including electric current loop, speed ring regulating system, and electric current loop is located at control
Internal layer processed, is responsible for current follow-up control, and speed ring is in outer layer, plays a decisive role to the speed regulation of whole system, needs stronger
Both anti-interference uses PI controller, the change of driver control parameter, to the kinetic characteristic of system, such as stationarity is moved
State response and control precision can all produce bigger effect.Mature available parameter tuning side there is no at present for PI parameter tuning
Method.
Domestic and foreign scholars have done a large amount of in-depth studies in terms of driver PI control parameter adjusting.Such as Wang Lina, Zhu
Letter is happy et al. to publish thesis " speed-adjustment System of Permanent PI attitude conirol method " at " electrotechnics journal ",
In this article, derivation process considers the influence of inverter, dead zone, delay, feedback filter and other non-ideal factors, and ties
It is practical to close engineering, has clearly defined the open-loop cut-off frequency of electric current loop and speed ring and the reasonable value range of phase margin.Root
According to system performance requirements, desired electric current loop and speed ring open-loop cut-off frequency and phase margin are set, by proposed method,
Can analytical Calculation PI controller parameter, but this method still falls within conventional PI control device scope, but with AC servo driver work
Make the complexity and required precision of environment, is badly in need of exploring new PI attitude conirol method.
Summary of the invention
The present invention is to solve PI controller traditional at present to be difficult to meet the complexity of AC servo driver working environment
And the technical issues of required precision, provide a kind of AC servo machinery driving device control method.
The technical solution adopted by the invention is as follows:
A kind of AC servo machinery driving device control method, comprising: S1 controls the AC servo motor and opens, and sets
Set given rotating speed；S2 detects the rotor-position signal and phase current of the AC servo motor；S3 carries out the phase current
Transformation obtains d shaft current, and sets d axis reference current, obtains d axis reference voltage by current inner loop PI controller；S4, according to
The rotor-position signal and the given rotating speed calculate angular speed deviation, pass through speed outer ring PI controller and the electric current
Inner ring PI controller obtains q axis reference voltage, wherein the speed outer ring PI controller, which uses, is based on multiple agent chaotic particle
Colony optimization algorithm carries out parameter tuning；S5 is generated according to the d axis reference voltage and q axis reference voltage for driving the friendship
SVPWM (Space Vector Pulse Width Modulation, space vector pulse width modulation) signal of flow servo motor.
Step S3 is specifically included:
The phase current of the AC servo motor is converted to obtain α, β shaft current i by Clarkeα、iβ, then by described α, β
Shaft current converts to obtain d, q shaft current i by Parkd、iq；
Set d axis reference current idref=0, and by the d shaft current idWith the d axis reference current idrefDescribed in input
Current inner loop PI controller obtains d axis reference voltage udref。
Parameter tuning is carried out to the speed outer ring PI controller using based on multiple agent Chaos particle swarm optimization algorithm,
It specifically includes:
1. construction Agent system grid environment: each Agent is equipped with 8 periphery neighbor particles and collectively forms and can hand over
The local environment of mutual communication, Initialize installation population number is 50 under the Agent system grid environment, maximum allowable iteration
Number=100, Inertia Weight ω=0.28, Studying factors c=1.5；And initialize position and speed of the particle in solution space；
2. calculating the adaptive value of each particle using following objective function J:
Wherein, ω1,ω2,ω3,ω4For weight, ω1,ω2,ω3Value range is (1,6), ω4Value range be (10,
20)；E (t) is Voltage Drop compensating instruction；U (t) is the output of PI controller；tuFor rise time, tuValue is 5ms；Y (t) is
Threephase load voltage ua,ub,uc, ey (t)=y (t)-y (t-1) is threephase load voltage variety；
3. each Agent is at war with and cooperates with 8 periphery neighbours respectively, and it is each adaptive to adapt to value function update as follows
Value:
Wherein, function variable αi∈ (- 2.56,2.56), n are dimension, value 10；
4. updating position and speed of each Agent particle in solution space:
It is iterated according to following PSO is iterative:
Wherein, ω is Inertia Weight, and being worth is 0.28, and parameter D indicates D dimension, i=1, and 2, L M, M are the sum of all particles,
r1And r2For the random number in [0,1] range, c1And c2Respectively the self study factor and social learning's factor, c1And c2Equal value
1.28For the nth iteration speed of particle i,For the iterative position of particle i n-th, piDHistory for particle i is optimal
Record, pgDFor current group optimal value；
5. calculating the adaptive value of each particle, 20% particle that performance is best in group is chosen, chaos office is carried out to it
Portion's search, and update whole extreme values in the individual extreme value and group of each particle；
6. search stops if algorithm meets optimization termination condition, optimal solution is exported；Otherwise, step is executed 7.；
7. shrinking region of search according to the following formula:
xminiD=max (xminiD,xgiD-r·(xmaxiD-xminiD))
xmaxiD=min (xmaxiD,xgiD+r·(xmaxiD-xminiD))
Wherein, xgiDVariate-value, x are tieed up for the D of current individual i extreme valueminiDFor individual i minimum, xmaxiDFor the individual pole i
Big value, random number r is in section (- 1,1)；
8. remaining 80% particle in group is randomly generated in space after shrinking, turns to step 2., recalculate suitable
It should be worth, i.e. target function value J,
By multiple agent Chaos particle swarm optimization algorithm iteration, individual extreme value and global extremum are constantly updated, is obtained so that mesh
The smallest one group of PI control parameter (k of offer of tender numerical value Jp, ki), and determine this group of PI control parameter (kp, ki) it is the ginseng optimized
Number.
Step S4 is specifically included:
The angular velocity omega of the AC servo motor is calculated according to the rotor-position signal θr, and according to described given
Reference angular velocities ω is calculated in revolving speedrref, and obtain the angular speed deviation
ω*=ωrref-ωr；
The angular speed deviation is inputted into the speed outer ring PI controller, obtains q axis reference current iqref；
By the q axis reference current iqrefWith the q shaft current iqThe current inner loop PI controller is inputted, the q is obtained
Axis reference voltage uqref。
Step S5 is specifically included:
By d, q axis reference voltage udref、uqrefThis is converted to obtain α, β axis reference voltage u against Parkαref、uβref,
And α, β axis reference voltage input SVPWM generator is generated into SVPWM waveform, it then will be by amplifying circuit amplification
SVPWM Waveform Input three-phase inverter realizes the driving to the AC servo motor.
Realize the dsp chip model TMS320F2806 of the AC Servo Motor Control.
Beneficial effects of the present invention:
The present invention is used by speed outer ring PI controller and is based on multiple agent Chaos particle swarm optimization algorithm setting parameter,
Current inner loop PI controller uses conventional method setting parameter, utilizes multiple agent Chaos particle swarm optimization algorithm fast convergence rate
The advantages that, can not only quick response electric current, inhibit current spike, AC servo machinery driving system can also be made to have relatively strong
Anti-interference, improve the stability of system.
Detailed description of the invention
Fig. 1 is the hardware block diagram of the AC servo machinery driving device of one embodiment of the invention；
Fig. 2 is the flow chart of the AC servo machinery driving device control method of the embodiment of the present invention；
Fig. 3 is the AC servo machinery driving device control principle drawing of one embodiment of the invention；
Fig. 4 is the velocity equivalent closed-loop control block diagram of one embodiment of the invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
In one embodiment of the invention, as shown in Figure 1, AC servo machinery driving device includes and AC servo motor
Connected three-phase inversion drive module, power module, power conversion module, current detection module, position detecting module, protection electricity
Road etc..As shown in Figure 1, realizing the dsp chip model TMS320F2806 of AC Servo Motor Control, the power supply of the dsp chip
Pin is connected to power conversion module, and power module connects three-phase inversion drive module, can power for AC servo drive system,
The power supply that power conversion module can provide power module is converted to the power supply suitable for dsp chip, to realize to dsp chip
Power supply.As shown in Figure 1, current detection module respectively with the three-phase input end of AC servo motor, dsp chip ADC pin and
Circuit is protected to be connected, current detection module can detect the phase current to AC servo motor, and input by ADC pin
Dsp chip, protection circuit is also connected to the PDPINTA pin of dsp chip, for realizing the overcurrent protection of AC servo motor；
Position detecting module is connected with the CAP pin of AC servo motor and dsp chip respectively, for detecting turning for AC servo motor
Sub- position signal simultaneously inputs dsp chip by CAP pin.As shown in Figure 1, by host computer, such as PC machine is connected to dsp chip
SCI pin, for the setting of given rotating speed, by by the I/O mouth for being keyed to dsp chip, for controlling AC servo electricity
The start and stop of machine are connected to the I/O mouth of dsp chip, the display for AC servo motor speed by charactron.Amplifying circuit mould
Block input terminal is connected with the PWM pin of dsp chip, and output end is connected with three-phase inversion drive module, for amplifying DSP generation
Pwm signal level, and isolation dsp chip and three-phase inversion drive module, three-phase inversion drive module and AC servo motor
Three-phase input end is connected, and realizes the adjusting to AC servo motor output power for the pwm signal level based on input.
As shown in Fig. 2, the AC servo machinery driving device control method of the embodiment of the present invention the following steps are included:
S1, control AC servo motor is opened, and given rotating speed is arranged.
S2 detects the rotor-position signal and phase current of AC servo motor.
S3 is converted phase current to obtain d shaft current, and sets d axis reference current, passes through current inner loop PI controller
Obtain d axis reference voltage.
Specifically, referring to Fig. 3, the phase current of AC servo motor can be converted to obtain α, β shaft current i by Clarkeα、
iβ, then α, β shaft current are passed through into Park and convert to obtain d, q shaft current id、iq；Set d axis reference current idref=0, and by d axis
Electric current idWith d axis reference current idrefInput current inner ring PI controller obtains d axis reference voltage udref。
Wherein, current inner loop PI controller carries out parameter tuning using conventional method.
S4 calculates angular speed deviation according to rotor-position signal and given rotating speed, by speed outer ring PI controller and
Current inner loop PI controller obtains q axis reference voltage, wherein speed outer ring PI controller, which uses, is based on multiple agent chaotic particle
Colony optimization algorithm carries out parameter tuning.
PI controller expression formula built in dsp chip is as follows:
Wherein, e (t) is the input of PI controller, the revolving speed deviation ω of e (t) i.e. AC servo motor*；U (t) is PI
The output of controller, i.e. q axis reference current iqref；kpFor the proportional gain of PI controller；TiFor integration time constant, kiFor PI
The integral gain of controller, enables ki=kp/Ti。
Wherein, whole using parameter is carried out to speed outer ring PI controller based on multiple agent Chaos particle swarm optimization algorithm
It is fixed, it specifically includes:
1. construction Agent system grid environment: each Agent is equipped with 8 periphery neighbor particles and collectively forms and can hand over
The local environment of mutual communication, Initialize installation population number is 50 under the Agent system grid environment, maximum allowable iteration
Number=100, Inertia Weight ω=0.28, Studying factors c=1.5；And initialize position and speed of the particle in solution space；
2. calculating the adaptive value of each particle using following objective function J:
Wherein, ω1,ω2,ω3,ω4For weight, ω1,ω2,ω3Value range is (1,6), ω4Value range be (10,
20)；E (t) is Voltage Drop compensating instruction；U (t) is the output of PI controller；tuFor rise time, tuValue is 5ms；Y (t) is
Threephase load voltage ua,ub,uc, ey (t)=y (t)-y (t-1) is threephase load voltage variety；
3. each Agent is at war with and cooperates with 8 periphery neighbours respectively, and it is each adaptive to adapt to value function update as follows
Value:
Wherein, function variable αi∈ (- 2.56,2.56), n are dimension, value 10；
4. updating position and speed of each Agent particle in solution space:
It is iterated according to following PSO is iterative:
Wherein, ω is Inertia Weight, and being worth is 0.28, and parameter D indicates D dimension, i=1, and 2, L M, M are the sum of all particles,
r1And r2For the random number in [0,1] range, c1And c2Respectively the self study factor and social learning's factor, c1And c2Equal value
1.28For the nth iteration speed of particle i,For the iterative position of particle i n-th, piDHistory for particle i is optimal
Record, pgDFor current group optimal value；
5. calculating the adaptive value of each particle, 20% particle that performance is best in group is chosen, chaos office is carried out to it
Portion's search, and update whole extreme values in the individual extreme value and group of each particle；
6. search stops if algorithm meets optimization termination condition, optimal solution is exported；Otherwise, step is executed 7.；
7. shrinking region of search according to the following formula:
xminiD=max (xminiD,xgiD-r·(xmaxiD-xminiD))
xmaxiD=min (xmaxiD,xgiD+r·(xmaxiD-xminiD))
Wherein, xgiDVariate-value, x are tieed up for the D of current individual i extreme valueminiDFor individual i minimum, xmaxiDFor the individual pole i
Big value, random number r is in section (- 1,1)；
8. remaining 80% particle in group is randomly generated in space after shrinking, turns to step 2., recalculate suitable
It should be worth, i.e. target function value J,
By multiple agent Chaos particle swarm optimization algorithm iteration, individual extreme value and global extremum are constantly updated, is obtained so that mesh
The smallest one group of PI control parameter (k of offer of tender numerical value Jp, ki), and determine this group of PI control parameter (kp, ki) it is the ginseng optimized
Number.
Referring to Fig. 3, the angular velocity omega of AC servo motor can be calculated according to rotor-position signal θr, and turned according to given
Reference angular velocities ω is calculated in speedrref, and obtain angular speed deviation ω*=ωrref-ωr；Again by angular speed deviation
Input speed outer ring PI controller obtains q axis reference current iqref；Then by q axis reference current iqrefWith q shaft current iqInput
Current inner loop PI controller obtains q axis reference voltage uqref。
S5 generates the SVPWM signal for driving AC servo motor according to d axis reference voltage and q axis reference voltage.
It, can be by d, q axis reference voltage u referring to Fig. 3dref、uqrefThis is converted to obtain α, β axis reference voltage u against Parkαref、
uβref, and α, β axis reference voltage input SVPWM generator are generated into SVPWM waveform, it then will be by amplifying circuit amplification
SVPWM Waveform Input three-phase inverter realizes the driving to AC servo motor.
In AC servo motor actual motion, the time constant of inverter and the coefficient of friction of motor can be ignored, and will
Electric current loop regards first order inertial loop as.Assuming that can be approximately G in the case where electric current loop perfect trackingc(s)=1.It can obtain as a result,
Velocity equivalent closed-loop control block diagram out after decoupling control is as shown in Figure 4.K in Fig. 4p+ki/ s is the biography of speed outer ring PI controller
Delivery function, keFor torque constant, the closed loop transfer function, of AC servo motor is calculated according to Fig. 4 are as follows:
AC servo machinery driving device control method according to an embodiment of the present invention is used by speed outer ring PI controller
Based on multiple agent Chaos particle swarm optimization algorithm setting parameter, current inner loop PI controller uses conventional method setting parameter,
The advantages that using multiple agent Chaos particle swarm optimization algorithm fast convergence rate, can not only quick response electric current, inhibit electric current
Spike can also make AC servo machinery driving system have stronger anti-interference, improve the stability of system.
In the description of the present invention, term " first ", " second " are used for description purposes only, and should not be understood as instruction or dark
Show relative importance or implicitly indicates the quantity of indicated technical characteristic.The feature of " first ", " second " is defined as a result,
It can explicitly or implicitly include one or more of the features.The meaning of " plurality " is two or more, unless
Separately there is clearly specific restriction.
In the present invention unless specifically defined or limited otherwise, term " installation ", " connected ", " connection ", " fixation " etc.
Term shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or integral；It can be mechanical connect
It connects, is also possible to be electrically connected；It can be directly connected, can also can be in two elements indirectly connected through an intermediary
The interaction relationship of the connection in portion or two elements.It for the ordinary skill in the art, can be according to specific feelings
Condition understands the concrete meaning of above-mentioned term in the present invention.
In the present invention unless specifically defined or limited otherwise, fisrt feature in the second feature " on " or " down " can be with
It is that the first and second features directly contact or the first and second features pass through intermediary mediate contact.Moreover, fisrt feature exists
Second feature " on ", " top " and " above " but fisrt feature be directly above or diagonally above the second feature, or be merely representative of
First feature horizontal height is higher than second feature.Fisrt feature can be under the second feature " below ", " below " and " below "
One feature is directly under or diagonally below the second feature, or is merely representative of first feature horizontal height less than second feature.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, schematic expression of the above terms are not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be in office
It can be combined in any suitable manner in one or more embodiment or examples.In addition, without conflicting with each other, the skill of this field
Art personnel can tie the feature of different embodiments or examples described in this specification and different embodiments or examples
It closes and combines.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of variations, modification, replacement can be carried out to these embodiments without departing from the principles and spirit of the present invention by understanding
And modification, the scope of the present invention is defined by the appended.
Claims (6)
1. a kind of AC servo machinery driving device control method characterized by comprising
S1 controls the AC servo motor and opens, and given rotating speed is arranged；
S2 detects the rotor-position signal and phase current of the AC servo motor；
S3 is converted the phase current to obtain d shaft current, and sets d axis reference current, passes through current inner loop PI controller
Obtain d axis reference voltage；
S4 calculates angular speed deviation according to the rotor-position signal and the given rotating speed, is controlled by speed outer ring PI
Device and the current inner loop PI controller obtain q axis reference voltage, wherein the speed outer ring PI controller, which uses, is based on more intelligence
It can body Chaos particle swarm optimization algorithm progress parameter tuning；
S5 is generated according to the d axis reference voltage and q axis reference voltage for driving the SVPWM of the AC servo motor to believe
Number.
2. AC servo machinery driving device control method according to claim 1, which is characterized in that step S3 is specifically wrapped
It includes:
The phase current of the AC servo motor is converted to obtain α, β shaft current i by Clarkeα、iβ, then α, β axis is electric
Stream converts to obtain d, q shaft current i by Parkd、iq；
Set d axis reference current idref=0, and by the d shaft current idWith the d axis reference current idrefInput the electric current
Inner ring PI controller obtains d axis reference voltage udref。
3. AC servo machinery driving device control method according to claim 2, which is characterized in that using based on mostly intelligent
Body Chaos particle swarm optimization algorithm carries out parameter tuning to the speed outer ring PI controller, specifically includes:
1. construction Agent system grid environment: each Agent be equipped with 8 periphery neighbor particles collectively form can interact it is logical
The local environment of letter, Initialize installation population number is 50 under the Agent system grid environment, maximum allowable the number of iterations
=100, Inertia Weight ω=0.28, Studying factors c=1.5；And initialize position and speed of the particle in solution space；
2. calculating the adaptive value of each particle using following objective function J:
Wherein, ω1,ω2,ω3,ω4For weight, ω1,ω2,ω3Value range is (1,6), ω4Value range is (10,20)；e
It (t) is Voltage Drop compensating instruction；U (t) is the output of PI controller；tuFor rise time, tuValue is 5ms；Y (t) is negative for three-phase
Carry voltage ua,ub,uc, ey (t)=y (t)-y (t-1) is threephase load voltage variety；
3. each Agent is at war with and cooperates with 8 periphery neighbours respectively, and adapts to value function as follows and update each adaptation value:
Wherein, function variable αi∈ (- 2.56,2.56), n are dimension, value 10；
4. updating position and speed of each Agent particle in solution space:
It is iterated according to following PSO is iterative:
Wherein, ω is Inertia Weight, and being worth is 0.28, and parameter D indicates D dimension, i=1, and 2, L M, M are the sum of all particles, r1With
r2For the random number in [0,1] range, c1And c2Respectively the self study factor and social learning's factor, c1And c2Equal value 1.28,For the nth iteration speed of particle i,For the iterative position of particle i n-th, piDFor the optimal record of history of particle i,
pgDFor current group optimal value；
5. calculating the adaptive value of each particle, 20% particle that performance is best in group is chosen, chaos is carried out to it and is locally searched
Rope, and update whole extreme values in the individual extreme value and group of each particle；
6. search stops if algorithm meets optimization termination condition, optimal solution is exported；Otherwise, step is executed 7.；
7. shrinking region of search according to the following formula:
xmin iD=max (xmin iD,xgiD-r·(xmax iD-xmin iD))
xmax iD=min (xmax iD,xgiD+r·(xmax iD-xmin iD))
Wherein, xgiDVariate-value, x are tieed up for the D of current individual i extreme valuemin iDFor individual i minimum, xmax iDIt is very big for individual i
Value, random number r is in section (- 1,1)；
8. remaining 80% particle in group is randomly generated in space after shrinking, turns to step 2., recalculates adaptive value,
That is target function value J,
By multiple agent Chaos particle swarm optimization algorithm iteration, individual extreme value and global extremum are constantly updated, is obtained so that target letter
The smallest one group of PI control parameter (k of numerical value Jp, ki), and determine this group of PI control parameter (kp, ki) it is the parameter optimized.
4. AC servo machinery driving device control method according to claim 3, which is characterized in that step S4 is specifically wrapped
It includes:
The angular velocity omega of the AC servo motor is calculated according to the rotor-position signal θr, and according to the given rotating speed
Reference angular velocities ω is calculatedrref, and obtain the angular speed deviation ω*=ωrref-ω；
The angular speed deviation is inputted into the speed outer ring PI controller, obtains q axis reference current iqref；
By the q axis reference current iqrefWith the q shaft current iqThe current inner loop PI controller is inputted, the q axis ginseng is obtained
Examine voltage uqref。
5. AC servo machinery driving device control method according to claim 4, which is characterized in that step S5 is specifically wrapped
It includes:
By d, q axis reference voltage udref、uqrefThis is converted to obtain α, β axis reference voltage u against Parkαref、uβref, and by institute
It states α, β axis reference voltage input SVPWM generator and generates SVPWM waveform, then by the SVPWM waveform by amplifying circuit amplification
Three-phase inverter is inputted, realizes the driving to the AC servo motor.
6. AC servo machinery driving device control method according to claim 5, which is characterized in that realize that the exchange is watched
Take the dsp chip model TMS320F2806 of motor control.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910155056.7A CN109901383A (en) | 2019-03-01 | 2019-03-01 | A kind of AC servo machinery driving device control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910155056.7A CN109901383A (en) | 2019-03-01 | 2019-03-01 | A kind of AC servo machinery driving device control method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109901383A true CN109901383A (en) | 2019-06-18 |
Family
ID=66946026
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910155056.7A Pending CN109901383A (en) | 2019-03-01 | 2019-03-01 | A kind of AC servo machinery driving device control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109901383A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110989334A (en) * | 2019-11-03 | 2020-04-10 | 武汉光谷航天三江激光产业技术研究有限公司 | Dynamic adjusting device for control parameters of laser cutting wafer |
| CN114488779A (en) * | 2022-02-08 | 2022-05-13 | 中国科学院赣江创新研究院 | Power chain cascade feedforward control strategy and system device of gasoline power generation system |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103713516A (en) * | 2013-12-17 | 2014-04-09 | 华中科技大学 | PDFF-based AC servo driver control parameter self-tuning method |
| CN104601077A (en) * | 2015-02-09 | 2015-05-06 | 北京航空航天大学 | Space vector modulation based harmonic current compensation system for high-speed permanent magnet motor |
| WO2016118470A1 (en) * | 2015-01-20 | 2016-07-28 | Alstom Technology Ltd | Model-based controls for a furnace and method for controlling the furnace |
| CN106094722A (en) * | 2016-07-18 | 2016-11-09 | 江苏理工学院 | Intelligent lathe control method |
| CN106124205A (en) * | 2016-08-29 | 2016-11-16 | 孟玲 | A kind of decelerator health analysis system |
| CN106647283A (en) * | 2017-01-23 | 2017-05-10 | 无锡信捷电气股份有限公司 | Auto-disturbance rejection position servo system optimization design method based on improved CPSO |
| CN106788045A (en) * | 2017-02-17 | 2017-05-31 | 天津工业大学 | A kind of permagnetic synchronous motor model prediction PI changeable weight concurrency control methods |
| CN106816877A (en) * | 2017-03-10 | 2017-06-09 | 国网江苏省电力公司常州供电公司 | A kind of distribution network voltage containing photovoltaic falls detection compensation method |
| CN107689760A (en) * | 2017-11-02 | 2018-02-13 | 哈尔滨理工大学 | Based on the magneto of matrix converter without position vector control system and method |
| CN108493947A (en) * | 2018-04-12 | 2018-09-04 | 江苏理工学院 | Voltage dip compensation method and device |
| CN108540031A (en) * | 2018-04-24 | 2018-09-14 | 南京信息职业技术学院 | The method for estimating rotating speed and moment controlling system of bearingless synchronous reluctance motor |
| CN108649850A (en) * | 2018-05-15 | 2018-10-12 | 天津工业大学 | Improve the internal permanent magnet synchronous motor current control method of UDE |
-
2019
- 2019-03-01 CN CN201910155056.7A patent/CN109901383A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103713516A (en) * | 2013-12-17 | 2014-04-09 | 华中科技大学 | PDFF-based AC servo driver control parameter self-tuning method |
| WO2016118470A1 (en) * | 2015-01-20 | 2016-07-28 | Alstom Technology Ltd | Model-based controls for a furnace and method for controlling the furnace |
| CN104601077A (en) * | 2015-02-09 | 2015-05-06 | 北京航空航天大学 | Space vector modulation based harmonic current compensation system for high-speed permanent magnet motor |
| CN106094722A (en) * | 2016-07-18 | 2016-11-09 | 江苏理工学院 | Intelligent lathe control method |
| CN106124205A (en) * | 2016-08-29 | 2016-11-16 | 孟玲 | A kind of decelerator health analysis system |
| CN106647283A (en) * | 2017-01-23 | 2017-05-10 | 无锡信捷电气股份有限公司 | Auto-disturbance rejection position servo system optimization design method based on improved CPSO |
| CN106788045A (en) * | 2017-02-17 | 2017-05-31 | 天津工业大学 | A kind of permagnetic synchronous motor model prediction PI changeable weight concurrency control methods |
| CN106816877A (en) * | 2017-03-10 | 2017-06-09 | 国网江苏省电力公司常州供电公司 | A kind of distribution network voltage containing photovoltaic falls detection compensation method |
| CN107689760A (en) * | 2017-11-02 | 2018-02-13 | 哈尔滨理工大学 | Based on the magneto of matrix converter without position vector control system and method |
| CN108493947A (en) * | 2018-04-12 | 2018-09-04 | 江苏理工学院 | Voltage dip compensation method and device |
| CN108540031A (en) * | 2018-04-24 | 2018-09-14 | 南京信息职业技术学院 | The method for estimating rotating speed and moment controlling system of bearingless synchronous reluctance motor |
| CN108649850A (en) * | 2018-05-15 | 2018-10-12 | 天津工业大学 | Improve the internal permanent magnet synchronous motor current control method of UDE |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110989334A (en) * | 2019-11-03 | 2020-04-10 | 武汉光谷航天三江激光产业技术研究有限公司 | Dynamic adjusting device for control parameters of laser cutting wafer |
| CN110989334B (en) * | 2019-11-03 | 2023-07-14 | 武汉光谷航天三江激光产业技术研究院有限公司 | Dynamic adjusting device for control parameters of laser cutting wafer |
| CN114488779A (en) * | 2022-02-08 | 2022-05-13 | 中国科学院赣江创新研究院 | Power chain cascade feedforward control strategy and system device of gasoline power generation system |
| CN114488779B (en) * | 2022-02-08 | 2023-05-05 | 中国科学院赣江创新研究院 | Power chain cascade feedforward control method and system device of gasoline power generation system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yin et al. | Recurrent neural network based adaptive integral sliding mode power maximization control for wind power systems | |
| Pan et al. | Variable pitch control on direct-driven PMSG for offshore wind turbine using Repetitive-TS fuzzy PID control | |
| CN103701368B (en) | The energy-conservation anti-backlash control method of bi-motor | |
| Swarnkar et al. | Effect of adaptation gain in model reference adaptive controlled second order system | |
| CN107070342A (en) | A kind of control system for permanent-magnet synchronous motor of bringing onto load state observer | |
| Qiang et al. | PMSM servo control system design based on fuzzy PID | |
| CN111173678A (en) | Wind power cabin suspension control method for RBF neural network pitch interference compensation | |
| CN107395040A (en) | Grid-connected converter complex vector PI controllers decouple and delay compensation method | |
| Lin et al. | Design and research of DC motor speed control system based on improved BAS | |
| CN109113929B (en) | Suspension control method for wind power magnetic suspension engine room | |
| CN109901383A (en) | A kind of AC servo machinery driving device control method | |
| Saihi et al. | Robust control H∞ fuzzy of a doubly fed induction generator integrated to wind power system | |
| Rodrigues et al. | Predictive incremental vector control for DFIG with weighted-dynamic objective constraint-handling method-PSO weighting matrices design | |
| Zheng et al. | Saturated adaptive-law-based backstepping and its applications to a quadrotor hover | |
| CN112523944A (en) | Self-adaptive dynamic surface control method for variable pitch system of wind driven generator | |
| Cheng et al. | Modeling and optimization control for aircraft AC generator brushless excitation system based on improved adaptive PSO | |
| Ahmadi et al. | Optimization of a self-tuning PID type fuzzy controller and a PID controller for an inverted pendulum | |
| Zhang et al. | An enhanced nonlinear ADRC speed control method for electric propulsion system: modeling, analysis, and validation | |
| Yin et al. | Implementation of various neural-network-based adaptive speed PI controllers for dual-three-phase PMSM | |
| CN112737435B (en) | Anti-interference system of stepping motor based on T-S fuzzy sliding mode control | |
| Xie et al. | Optimization research of FOC based on PSO of induction motors | |
| Huang et al. | Nonsingular terminal sliding mode control based on sensor-cloud system for permanent magnet in-wheel motor | |
| Yuan et al. | Intelligent adaptive jerk control with dynamic compensation gain for permanent magnet linear synchronous motor servo system | |
| Alzain et al. | Optimization of sliding mode control based on BAT-algorithm for the DFIG-WT | |
| CN114696700A (en) | Neural network for online setting parameters of active disturbance rejection controller of permanent magnet synchronous motor |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190618 |