CN107239643A - The parameter identification apparatus and method of super-magnetostrictive drive magnetic hysteresis nonlinear model - Google Patents
The parameter identification apparatus and method of super-magnetostrictive drive magnetic hysteresis nonlinear model Download PDFInfo
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Abstract
The present invention discloses a kind of parameter identification apparatus and method of super-magnetostrictive drive magnetic hysteresis nonlinear model, comprises the steps of：The current signal that current signal and power supply that displacement transducer exports 4 20mA are exported each is converted into 0 3.3V voltage signal by current/voltage module；The two-way voltage signal for being exported current/voltage module by signal wire is through ADC synchronous transfer to DSP Processor；DSP Processor carries out parameter identification using population and artificial fish-swarm mixing identification algorithm according to the signal measured to hysteresis model, the parameter of correction model while measurement data is constantly obtained；Said process is repeated, requirement is met until picking out parameter.The present invention utilizes the input current value and output displacement value of ADC synchronous acquisition super-magnetostrictive drive, and the parameter of its hysteresis model is recognized based on DSP Processor and population and artificial fish-swarm mixing identification algorithm, to improve the precision of its output displacement model, be conducive to error compensation control.
Description
Technical field
It is a kind of super-magnetostrictive drive magnetic hysteresis nonlinear model specifically the present invention relates to field of signal processing
Parameter real-time identification technology, it is adaptable to data signal relevant parameter and super-magnetostrictive drive magnetic hysteresis nonlinear model
Parameter recognized, available for control device, such as automation, precision optical machinery.
Background technology
Super-magnetostrictive drive (Giant Magnetostrictive Actuator, write a Chinese character in simplified form GMA) is ultra-magnetic telescopic
One of main application device of material, with fast response time, the excellent properties such as energy conversion efficiency is high, output loading is big,
The fields such as precision actuation, Precision Machining and precision positioning are with a wide range of applications, but due to giant magnetostrictive material tool
There is the magnetic hysteresis nonlinear characteristic of ferromagnetism functional material saturation, cause the input current of super-magnetostrictive drive developed and defeated
Go out between displacement to exist that magnetic hysteresis is non-linear, the hysterisis error of output displacement is up to 20% or so, it is impossible to which meet precision positioning will
Ask.To expand applications of the GMA in precision actuation field, the positioning precision in the urgent need to improving GMA, accordingly, it would be desirable to GMA
The magnetic hysteresis nonlinear model of output displacement carries out error compensation, and the premise of error compensation is to need to set up accurate GMA outputs
Displacement model.The magnetic hysteresis that Jiles-Atherton models are capable of accurate description GMA is non-linear, but comprising multiple unknown in model
Physical parameter, using different parameter identification apparatus and method, the precision of resulting model is also different, wherein, Jia Zhenyuan etc. is adopted
Parameter identification is carried out with least square method, reliable parametric results are obtained, has the advantages that method is easy；Meng Aihua etc.
Parameter identification is carried out using modified particle swarm optiziation so that model error is 5%.
Developing a GMDS with superiority such as fast response time, energy conversion efficiency are high, output loading is big herein
On the basis of, the output displacement model of driver is set up using Jiles-Atherton models, and propose it is a kind of by population and
The optimized algorithm of artificial fish-swarm mixing, calculated with model obtained by the square root of the difference of displacement that measures of output displacement and experiment
As the fitness function of the algorithm, six unknown parameters of model are recognized, to improve GMDS output displacement models
Precision, basis is provided for subsequent control GMDS positioning precision.
The content of the invention
In view of the shortcomings of the prior art, the present invention discloses a kind of parameter of super-magnetostrictive drive magnetic hysteresis nonlinear model
Device for identifying and method, can solve the problem that and set up super-magnetostrictive drive magnetic hysteresis currently with based on Jiles-Atherton models
The problem of nonlinear model parameter identification efficiency is low, accuracy is not high.
To realize object above, the present invention is achieved by the following technical programs：A kind of super-magnetostrictive drive magnetic
The parameter identification method of stagnant nonlinear model, is comprised the steps of：
The electric current that (1a) is exported current signal and power supply that displacement transducer exports 4-20mA by current/voltage module
Signal is each converted into the voltage signal between 0-3.3V；
The two-way voltage signal that (1b) is exported current/voltage module by signal wire is through ADC synchronous transfer to DSP
Processor；
The input current signal and displacement signal that (1c) is collected are super-magnetostrictive drive magnetic hysteresis nonlinear model respectively
The input signal and output signal of type, DSP microprocessors carry out magnetic hysteresis nonlinear model parameter identification, ginseng according to the signal measured
Number identification may include online real-time identification and off-line identification, and parameter identification uses population and artificial fish-swarm mixing identification algorithm,
The parameter of magnetic hysteresis nonlinear model is constantly corrected while measurement data is constantly obtained；
(1d) repeats said process, until picked out parameter meets required precision or is optimal.
Described population and artificial fish-swarm mixing identification algorithm is comprised the steps of：
(2a) sets population scale N, acceleration parameter c1, c2And c3, Inertia Weight w, visible range visual, step-length Step,
Maximum sounds out number of times try_number, crowding δ, maximum iteration Maxgen, error e；
(2b) divides population N for the equal population pop of 2 quantity1And pop2, pop1According to the fitness of particle cluster algorithm
Function calculates each individual fitness value, obtains optimal value pg1；pop2According to the fitness function meter of artificial fish-swarm algorithm
Each individual fitness value is calculated, optimal value pg is obtained2, compare optimal value pg1And pg2Size, optimal value be assigned to bulletin
Plate pg；
(2c)pop1Pg is obtained according to particle cluster algorithm1_ new and new population pop1_new；
(2d)pop2New optimal solution pg is obtained according to artificial fish-swarm algorithm2_ new and new population pop2_new；
(2e) compares pg1_ new and pg2_ new fitness value, pg on optimal value pg_new and bulletin board is compared,
Such as it is better than bulletin board, then updates bulletin board, on the contrary bulletin board is constant；
(2f) repeats (2c)~(2e) steps, until iterations d reaches the maximum iteration Maxgen or public affairs of setting
Untill the optimal solution on plate is accused in error e circle of setting；
(2g) output optimal solution (the individual state pg i.e. on bulletin board).
A kind of parameter identification device of super-magnetostrictive drive magnetic hysteresis nonlinear model, at input module, signal
Module and output module are managed, the signal processing module includes data storage cell, parameter identification unit and fuzzy-adaptation PID control list
Member, parameter identification unit is made up of fitness function and Identification of parameter, including parameter off-line identification part and parameter it is online
Two parts are recognized, wherein Identification of parameter uses population and artificial fish-swarm mixing identification algorithm, constantly collection measurement number
It is modified according to the parameter simultaneously simultaneously to identification；
The input module includes current/voltage module, keyboard input module and ADC；The current/voltage module
The current signal of current signal and power supply output for displacement transducer to be exported each is converted into voltage signal；The button
Input module is used for parameter off-line identification and the switching of on-line parameter identification both of which；The ADC be used for gather electric current/
The voltage signal of voltage module output and the temperature signal of temperature sensor output；
The output module includes energy supply control module, DAC module and screen display module；The energy supply control module is used
In the current signal of control power supply output；The screen display module is used for display parameters identification result and Identification Errors value.
The present invention discloses a kind of parameter identification apparatus and method of super-magnetostrictive drive magnetic hysteresis nonlinear model, passes through
Signal wire arrives the current signal that 0-3.3V voltage signal and super-magnetostrictive drive are inputted through ADC synchronous transfer
DSP microprocessors, DSP microprocessors carry out magnetic hysteresis nonlinear model parameter identification according to the signal that measures, using population and
Artificial fish-swarm mixing identification algorithm carries out parameter identification, and constantly amendment magnetic hysteresis is non-linear while measurement data is constantly obtained
The parameter of model；It is GMA so as to improve the Efficiency and accuracy of super-magnetostrictive drive magnetic hysteresis nonlinear model parameter identification
The magnetic hysteresis nonlinear compensation of output displacement provides basis.
Brief description of the drawings
Implement in order to illustrate more clearly of the present invention or technical scheme of the prior art, below by embodiment or existing skill
The accompanying drawing used required in art description, which is done, simply to be introduced, it should be apparent that, drawings in the following description are only the present invention
Some embodiments, for those of ordinary skill in the art, on the premise of not paying creative work, can also basis
These accompanying drawings obtain other accompanying drawings.
Fig. 1 is a kind of parameter identification apparatus and method of super-magnetostrictive drive magnetic hysteresis nonlinear model in the present invention
Schematic flow sheet；
Fig. 2 is the schematic flow sheet of population and artificial fish-swarm mixing identification algorithm in the present invention；
Fig. 3 shows for a kind of square of the parameter identification device of super-magnetostrictive drive magnetic hysteresis nonlinear model in the present invention
It is intended to；
Fig. 4 shows the method for work of Fig. 3 shown devices.
Embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
A part of embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art exist
The every other embodiment obtained under the premise of creative work is not made, the scope of protection of the invention is belonged to.
As shown in figure 1, the present invention discloses a kind of parameter identification method of super-magnetostrictive drive magnetic hysteresis nonlinear model,
Comprise the steps of：Step s1, the current signal and power supply that displacement transducer is exported into 4-20mA by current/voltage module is defeated
The current signal gone out is each converted into the voltage signal between 0-3.3V；Step s2, it is by signal wire that current/voltage module is defeated
The two-way voltage signal gone out is through ADC synchronous transfer to DSP Processor；Step s3, DSP Processor is according to the signal pair measured
Magnetic hysteresis nonlinear model carries out parameter identification, and parameter identification uses population and artificial fish-swarm mixing identification algorithm, constantly obtained
The parameter of correction model while taking measurement data；Step s4, repeats said process, required precision is met until picking out parameter
Or be optimal.
As shown in Fig. 2 described population and artificial fish-swarm mixing identification algorithm is comprised the steps of：Step a1, is set
Population scale N, acceleration parameter c1, c2And c3, Inertia Weight w, visible range visual, step-length Step, maximum exploration number of times try_
Number, crowding δ, maximum iteration Maxgen, error e；Step a2, population, N points are the equal population of 2 quantity
pop1And pop2, pop1Fitness function according to particle cluster algorithm calculates each individual fitness value, obtains optimal value
pg1；pop2Fitness function according to artificial fish-swarm algorithm calculates each individual fitness value, obtains optimal value pg2, than
Compared with optimal value pg1And pg2Size, optimal value is assigned to bulletin board pg；Step a3, pop1Pg is obtained according to particle cluster algorithm1_
New and new population pop1_new；Step a4, pop2New optimal solution pg is obtained according to artificial fish-swarm algorithm2_ new and new kind
Group pop2_new；Step a5, compares pg1_ new and pg2_ new fitness value, pg on optimal value pg_new and bulletin board is entered
Row compares, and is such as better than bulletin board, then updates bulletin board, otherwise bulletin board is constant；Step a6, repeats (a2)~(a5) steps, until
Iterations d reaches that the optimal solution on the maximum iteration Maxgen or bulletin board of setting is in error e circle of setting
Only；Step a7, output optimal solution (the individual state pg i.e. on bulletin board).
As shown in figure 3, the present invention discloses a kind of parameter identification device of super-magnetostrictive drive magnetic hysteresis nonlinear model,
Comprising input module 10, signal processing module 20 and output module 30, the signal processing module comprising data storage cell 21,
Parameter identification unit 22 and fuzzy-adaptation PID control unit 23, parameter identification unit 22 is by fitness function and Identification of parameter group
Into, including two parts in parameter off-line identification part and on-line parameter identification, wherein Identification of parameter is using population and people
The work shoal of fish mixes identification algorithm, and constantly parameter of the collection measurement data simultaneously simultaneously to identification is modified；The input module 10
Include current/voltage module 11, keyboard input module 12 and ADC 13；The current/voltage module 11 is used to pass displacement
The current signal of sensor output and the current signal of power supply output are each converted into voltage signal；The keyboard input module 12 is used
In parameter off-line identification and the switching of on-line parameter identification both of which；The ADC 13 is used for gathering current/voltage module
The voltage signal of 11 outputs and the temperature signal of temperature sensor output；The output module 30 comprising energy supply control module 31,
DAC module 32 and screen display module 33；The energy supply control module 31 is used for the current signal for controlling power supply to export；The screen
Curtain display module is used for display parameters identification result and Identification Errors value.
As shown in figure 4, further show in more detail the method for work of Fig. 3 shown devices, workflow is：Displacement signal and
--- --- parameter is stored and shown parameter identification --- identification result evaluation --- data storage power current signal synchronous acquisition
Show.The parameter identification control of super-magnetostrictive drive magnetic hysteresis nonlinear model, its core is the accuracy and ginseng of data acquisition
Number identification algorithm.
The super-magnetostrictive drive mathematical modeling and Identification of parameter principle of institute's foundation of the present invention be：
1. the output displacement model of super-magnetostrictive drive., can be by according to the operation principle of super-magnetostrictive drive
Three parts of its output displacement model, i.e., be converted into the process of magnetic field energy, referred to as magnetic field model by electric energy；It is converted into by magnetic field energy
The process of the intensity of magnetization, as magnetic hysteresis nonlinear model；The process of output displacement, referred to as magnetostriction are converted into by the intensity of magnetization
Model.Three kinds of model difference are as follows.
1) magnetic field model
Driving magnetic field that super-magnetostrictive drive is produced by inner coil using twin coil type of drive, i.e. magnetic field and outer
The superposition for the bias magnetic field that layer line circle is produced.It can be obtained according to electromagnetic field knowledge：
H (t)=Hq+Hp=fqI(t)+fpIp (1)
In formula, the driving magnetic field of H (t) --- drive system, A/m；
Hq--- the driving magnetic field that inner coil is produced, A/m；
Hp--- the bias magnetic field that outer coil is produced, A/m；
fq--- inner coil magnetic field coefficient；
fp--- outer coil magnetic field coefficient；
I (t) --- driving coil electric current；
Ip--- bias coil electric current.
2) magnetic hysteresis nonlinear model
Jiles-Atherton models are that domain wall of two physicists of Jiles and Atherton based on ferromagnetic material is theoretical
The hysteresis model of foundation, in the model, it is necessary to determine the relation between externally-applied magnetic field H and magnetization M in terms of 5.
In formula, He--- the effective magnetic field of magnetic material, A/m；
Man--- anhysteretic intensity, A/m；
Mirr--- irreversible magnetization intensity, A/m；
Mrev--- reversible magnetization intensity, A/m；
M --- total magnetization intensity, A/m；
H --- externally-applied magnetic field, A/m；
Hσ--- prestressing force σ0The induction magnetic field of generation, A/m；Parameter alpha '=the λ of α+9sσ0/(2u0Ms 2), work as dH/dt>When 0, δ
=1；dH/dt<When 0, δ=- 1.
Through being derived from, the relation between magnetization M and driving magnetic field H is：
In formula, c --- reversible component coefficient；
α --- domain wall interaction coefficient；
A --- anhysteretic intensity profiles coefficient；
K --- irreversible loss coefficient；
Ms--- saturation magnetization, A/m.
Formula (2) is solved for the ease of computer, it is necessary to carried out sliding-model control, discrete rear Jiles-
Shown in the calculation formula of Atherton models such as formula (4)：
3) magnetostriction model
It can be seen from document, in the case where magnetic field intensity is certain, the magnetostrictive strain λ of GMM rod and magnetization M
Relation is met：
λ=γ M2 (5)
In formula, M --- the GMM intensity of magnetization, unit is A/m.
According to stress and strain stress relation, magnetostrictive force F is：
F=EHArλ (6)
In formula, EH--- the modulus of elasticity of GMM rod, Pa；
Ar--- the cross-sectional area of GMM rod, m2。
In summary, in the displacement model of super-magnetostrictive drive, having 6 parameters needs identification, i.e. θ=(Ms,
α',a,k,c,γ)。
Mathematical modeling is exactly the core of nature information that affairs change procedure is described with mathematical linguistics, utilizes the input and output of collection
The process that data are recognized to the mathematical model parameter of foundation, referred to as parameter identification.
2. the Identification of parameter principle of the present invention：During parameter identification, first, by the value of unknown parameter in mathematical modeling
Scope is refined, and unknown parameter is possible into valued combinations into the set of a feasible solution；Secondly, suitable calculate is chosen
Constantly search obtains parameter θ value to method in the set of feasible solution, is updated in magnetic hysteresis nonlinear model and obtains in identical driving electricity
Flow the output displacement under I (k) effectsAnd obtain difference e with system reality output displacement x；Finally, difference e is updated to mistake
The unknown parameter in model is constantly adjusted in poor object function so that target function value reaches minimum or necessary requirement.
In summary, the present invention disclose a kind of parameter identification device of super-magnetostrictive drive magnetic hysteresis nonlinear model with
Method, methods described is comprised the steps of：The current signal that voltage module exports displacement transducer 4-20mA is turned by electric current
It is converted into 0-3.3V voltage signal；The current signal for being exported voltage signal and power supply by signal wire is synchronous through ADC
It is transferred to DSP Processor；DSP Processor carries out parameter identification, parameter identification according to the signal measured to magnetic hysteresis nonlinear model
Using population and artificial fish-swarm mixing identification algorithm, the parameter of correction model while measurement data is constantly obtained；Repeat
Said process, meets required precision or is optimal until picking out parameter.The present invention utilizes the super mangneto of ADC synchronous acquisition
The input current value and output displacement value of telescopic driver, and based on DSP Processor and population and artificial fish-swarm mixing identification
Algorithm is recognized to the parameter of its hysteresis model, to improve the precision of its output displacement model, is conducive to error compensation control.
It should be noted that herein, term " comprising ", "comprising" or its any other variant are intended to non-exclusive
Property include so that process, method, article or equipment including a series of key elements not only include those key elements, but also
Including other key elements being not expressly set out, or also include for this process, method, article or equipment intrinsic want
Element.In the absence of more restrictions, the key element limited by sentence "including a ...", and it is non-excluded included described
Also there is other identical element in process, method, article or the equipment of key element.
The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although with reference to the foregoing embodiments
The present invention is described in detail, those of ordinary skill in the art should be understood：It still can be to foregoing embodiments
Described technical scheme is modified, or carries out equivalent substitution to which part technical characteristic；And these are changed or replaced
Change, the essence of appropriate technical solution is departed from the spirit and scope of various embodiments of the present invention technical scheme.
Claims (6)
1. a kind of parameter identification method of super-magnetostrictive drive magnetic hysteresis nonlinear model, it is characterised in that：Include following step
Suddenly：
The current signal that (1a) is exported current signal and power supply that displacement transducer exports 4-20mA by current/voltage module
Each it is converted into the voltage signal between 0-3.3V；
The two-way voltage signal that (1b) is exported current/voltage module by signal wire is through ADC synchronous transfer to DSP processing
Device；
The input current signal and displacement signal that (1c) is collected are super-magnetostrictive drive magnetic hysteresis nonlinear model respectively
Input signal and output signal, DSP microprocessors carry out magnetic hysteresis nonlinear model parameter identification according to the signal measured, and parameter is distinguished
Knowledge may include online real-time identification and off-line identification, and parameter identification uses population and artificial fish-swarm mixing identification algorithm, not
The disconnected parameter for obtaining continuous amendment magnetic hysteresis nonlinear model while measurement data；
(1d) repeats said process, until picked out parameter meets required precision or is optimal.
2. a kind of parameter identification method of super-magnetostrictive drive magnetic hysteresis nonlinear model according to claim 1, its
It is characterised by：Described population and artificial fish-swarm mixing identification algorithm is comprised the steps of：
(2a) sets population scale N, acceleration parameter c1, c2And c3, Inertia Weight w, visible range visual, step-length Step are maximum
Sound out number of times try_number, crowding δ, maximum iteration Maxgen, error e；
(2b) divides population N for the equal population pop of 2 quantity1And pop2, pop1According to the fitness function of particle cluster algorithm
Each individual fitness value is calculated, optimal value pg is obtained1；pop2Calculated according to the fitness function of artificial fish-swarm algorithm
The fitness value of each individual, obtains optimal value pg2, compare optimal value pg1And pg2Size, optimal value is assigned to bulletin board
pg；
(2c)pop1Pg is obtained according to particle cluster algorithm1_ new and new population pop1_new；
(2d)pop2New optimal solution pg is obtained according to artificial fish-swarm algorithm2_ new and new population pop2_new；
(2e) compares pg1_ new and pg2_ new fitness value, pg on optimal value pg_new and bulletin board is compared, such as excellent
In bulletin board, then bulletin board is updated, on the contrary bulletin board is constant；
(2f) repeats (2c)~(2e) steps, until iterations d reaches the maximum iteration Maxgen or bulletin board of setting
On optimal solution in error e circle of setting untill；
(2g) output optimal solution (the individual state pg i.e. on bulletin board).
3. a kind of parameter identification device of super-magnetostrictive drive magnetic hysteresis nonlinear model, it is characterised in that：Include input mould
Block, signal processing module and output module, the signal processing module is comprising data storage cell, parameter identification unit and obscures
PID control unit, the parameter identification unit is made up of fitness function and Identification of parameter, including parameter off-line identification portion
It is divided to and two parts of on-line parameter identification, wherein Identification of parameter uses population and artificial fish-swarm mixing identification algorithm, no
Parameter of the disconnected collection measurement data simultaneously simultaneously to identification is modified.
4. a kind of parameter identification device of super-magnetostrictive drive magnetic hysteresis nonlinear model according to claim 3, its
It is characterised by：The input module includes current/voltage module, keyboard input module and ADC；The current/voltage mould
Block is used for the current signal for exporting displacement transducer and the current signal of power supply output is each converted into voltage signal；It is described to press
Key input module is used for parameter off-line identification and the switching of on-line parameter identification both of which；The ADC is used for gathering electricity
The voltage signal of stream/voltage module output and the temperature signal of temperature sensor output.
5. a kind of parameter identification device of super-magnetostrictive drive magnetic hysteresis nonlinear model according to claim 3 or 4,
It is characterized in that：The output module includes energy supply control module, DAC module and screen display module, the power supply mould
Block is used for the current signal for controlling power supply to export.
6. a kind of parameter identification dress of super-magnetostrictive drive magnetic hysteresis nonlinear model according to claim 3 or 4 or 5
Put, it is characterised in that：The screen display module is used for display parameters identification result and Identification Errors value.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107807532A (en) * | 2017-11-30 | 2018-03-16 | 北京航空航天大学 | A kind of adaptive inversion vibration isolation control method for ultra-magnetic telescopic vibration-isolating platform |
| CN107807531A (en) * | 2017-11-30 | 2018-03-16 | 北京航空航天大学 | A kind of adaptive inversion tracking and controlling method for ultra-magnetic telescopic tracking platform |
| CN108519569A (en) * | 2018-05-07 | 2018-09-11 | 上海交通大学 | JA hysteresis model parameter identification methods containing stress |
| CN108696176A (en) * | 2018-05-08 | 2018-10-23 | 广东工业大学 | A kind of piezoelectric ceramic actuator control method based on particle cluster algorithm |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106502092A (en) * | 2016-10-21 | 2017-03-15 | 东南大学 | A kind of thermal process model parameter identification method using improvement Hybrid Particle Swarm |
-
2017
- 2017-07-24 CN CN201710604425.7A patent/CN107239643A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106502092A (en) * | 2016-10-21 | 2017-03-15 | 东南大学 | A kind of thermal process model parameter identification method using improvement Hybrid Particle Swarm |
Non-Patent Citations (5)
| Title |
|---|
| 杨理华等: "超磁致伸缩作动器非线性模型辨识研究", 《振动与冲击》 * |
| 罗德相等: "粒子群和人工鱼群混合优化算法", 《计算机与应用化学》 * |
| 耿超等: "基于人工鱼群与蛙跳混合算法的变压器Jiles-Atherton模型参数辨识", 《中国电机工程学报》 * |
| 马志新等: "GMA-DSP测控系统的PID控制设计与实现", 《现代制造工程》 * |
| 黄天录等主编, 西安电子科技大学出版社 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107807532A (en) * | 2017-11-30 | 2018-03-16 | 北京航空航天大学 | A kind of adaptive inversion vibration isolation control method for ultra-magnetic telescopic vibration-isolating platform |
| CN107807531A (en) * | 2017-11-30 | 2018-03-16 | 北京航空航天大学 | A kind of adaptive inversion tracking and controlling method for ultra-magnetic telescopic tracking platform |
| CN107807532B (en) * | 2017-11-30 | 2020-02-18 | 北京航空航天大学 | Self-adaptive reverse vibration isolation control method for giant magnetostrictive vibration isolation platform |
| CN107807531B (en) * | 2017-11-30 | 2020-02-18 | 北京航空航天大学 | Self-adaptive inverse tracking control method for giant magnetostrictive tracking platform |
| CN108519569A (en) * | 2018-05-07 | 2018-09-11 | 上海交通大学 | JA hysteresis model parameter identification methods containing stress |
| CN108519569B (en) * | 2018-05-07 | 2019-07-16 | 上海交通大学 | JA hysteresis model parameter identification method containing stress |
| CN108696176A (en) * | 2018-05-08 | 2018-10-23 | 广东工业大学 | A kind of piezoelectric ceramic actuator control method based on particle cluster algorithm |
| CN108696176B (en) * | 2018-05-08 | 2019-07-26 | 广东工业大学 | A kind of piezoelectric ceramic actuator control method based on particle swarm algorithm |
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