CN101567617B - Optimal design method for structural parameters of cylindrical linear motors - Google Patents
Optimal design method for structural parameters of cylindrical linear motors Download PDFInfo
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Abstract
The invention relates to an optimal design method for structural parameters of cylindrical linear motors, belongs to the field of electromechanical intellectualization, and is motor parameter optical design technology. The method comprises the following steps: under the condition of meeting a structural parameter restriction range, dynamically sampling the structural parameters; analyzing thrust and undulating values of the cylindrical linear motors with different structural parameters by a finite element analysis method; taking the structural parameters as input of a neural network and the thrust and the undulating values as output of the neural network, and fitting functional relations of the structural parameters, the thrust and the undulation by the neural network; and finally optimizing the structural parameters to be optimized by combining an intelligent optimization method to obtain satisfying thrust and undulation performance in unit volume of a motor. The method is easy for engineered application, and can avoid inaccurate concentrated parameter calculation caused by complex magnetic circuits of the motor, difficult parameter optimization caused by the intercoupling of the parameters and the like. The technology can be also used for optimal design of structural parameters of motors with any structure.
Description
Technical field
The present invention relates to a kind of optimal design method for structural parameters of cylindrical linear motors, belong to the dynamoelectric intelligent field, is a kind of parameter of electric machine design optimizing.
Background technology
The computational analysis method of motor mainly contains following three kinds: the magnetic circuit theory method of lumped parameter, the Theory of Electromagnetic Field method and the finite element method of distributed constant.The magnetic circuit theory method of lumped parameter is to utilize the method for equivalent magnetic circuit to analyze, and distributed constant is considered to concentrated magnetic circuit model.The magnetic circuit theory method of lumped parameter is simple, and Theoretical Calculation is easy, but the distribution heterogeneity of motor-field and the existence of leakage field make that this method error of calculation is relatively large.In order to remedy error, there is a large amount of corrected parameters in the design of electrical motor, then needing wastes time and energy by test acquisition again and again in a large amount of engineering experiences, wastes resource simultaneously.The Theory of Electromagnetic Field method of distributed parameter is mainly utilized the Maxwell equation, find the solution the field distribution function in whole zone, can finely handle complicated Distribution of Magnetic Field analysis, but this theoretical method calculation of complex can not solve the non-linear factor of complex conditions and material etc. simultaneously.In conjunction with Maxwell equation in the Theory of Electromagnetic Field and calculus of variations thought, be difficult to the problem of difference discrete at complex region in the calculus of variations, Finite Element Method is arisen at the historic moment.
The basic thought of finite element method is that object (being the continuous territory of finding the solution) is separated into combination limited and that interconnect by certain way, simulate or approach original object, thereby a continuous infinite degrees of freedom problem reduction is discrete finite degrees of freedom problem, a kind of numerical analysis method of then finding the solution.It is very complicated that the complexity of concrete motor model has determined the Theoretical Calculation method for solving of motor to find the solution, and be difficult to accurately find the solution, and often carries out finite element analysis by the computer finite element analysis software, tries to achieve exact solution.Finite Element Method is simply effective, and the computational accuracy height, but amount of calculation is very big, and is difficult for carrying out theory analysis and calculating.
Because of structure of the linear motion actuator model and material characteristics complexity, conditions such as while incentives plus restraints are numerous, the analytical method of linear electric motors is difficult points of current research, finite element method has become scientific and technological engineering staff's optimal selection, and, impel the scientific research personnel that linear electric motors are optimized design again to high-performance linear motor demand more, objective function of optimization design is, under identical initial conditions, the unit effective volume should obtain maximum thrust and minimum force oscillation.Traditional optimal design all is the method that designs by relatively, obtains one group of parameter of relatively coming out, but intercouples between the motor internal parameter, relatively brings very big trouble to parameter, makes comparison method be difficult to obtain more excellent parameter.Emerging intelligent algorithm provides new means for optimization method, but intelligent algorithm all is mechanism with the iterative search, the different parameters of electric machine is carried out iterative computation seek optimized parameter.
The method that traditional design method adopts magnetic circuit to analyze, need a large amount of engineering experiences, mainly by obtaining some corrected parameters in conjunction with actual engineering, and calculate motor performance by the method for lumped parameter, most calculating all is some estimations, also is a kind ofly manual to gather way through test for the correction of structural parameters.Use the distributed constant computational speed fast, but calculate accurately inadequately, be difficult to satisfy accurate optimization and calculate needs, and when only using Finite Element Method to carry out Design of Structural parameters, amount of calculation is very big, increases the engineering difficulty.
Summary of the invention
The objective of the invention is to have proposed a kind of parameters of structural dimension Optimization Design of cylindrical linear in order to calculate problems such as inaccuracy, optimization difficulty in the Design method of structural parameters that solves traditional cylindrical linear.
The objective of the invention is to be achieved through the following technical solutions.
A kind of optimal design method for structural parameters of cylindrical linear motors of the present invention is: at first satisfying under the structural parameters restriction range condition, determining motor fixed structure parameter really, selecting motor parameter to be optimized; Secondly to the dynamic sampling of structural parameters, use finite element method, the thrust and the fluctuating nature thereof of cylindrical linear are analyzed; Then structural parameters are imported as neural net, thrust and undulating value are exported as neural net, and neural network training is to carry out match to the functional relation of structural parameters and thrust and fluctuation; Last in conjunction with the neural net after the match, use intelligent optimization method that structural parameters are optimized, obtain the adaptive value of more excellent structural parameters, thereby obtain the performance of interior thrust of more excellent motor unit volume and fluctuation.
Described a kind of optimal design method for structural parameters of cylindrical linear motors, its motor parameter selection method to be optimized is: at first according to the concrete requirement of engineering of reality, determine motor fixed structure parameter really, selection has the structural parameters to be optimized of fundamental influence to be optimized to motor properties, and determines the feasible region of structural parameters to be optimized; Then in feasible region, carry out stochastical sampling, the motor of sampled point structural parameters is carried out finite element analysis, obtain motor thrust and undulating value.
Described a kind of optimal design method for structural parameters of cylindrical linear motors, its neural network training are to adopt the method for dynamic neural network training that neural net is trained; Select the training of part sample during beginning, stochastical sampling part new sample point again then, calculate new sample point and use the thrust and the undulating value of neural network prediction, with the thrust of actual FEM (finite element) calculation and the error of undulating value, if error is more then proceeded training, till obtaining satisfied neural net.
Described a kind of optimal design method for structural parameters of cylindrical linear motors, it to the intelligent optimization method that structural parameters of cylindrical linear motors is optimized is: the target of optimization is exactly to seek optimum sample point as far as possible, make that electric machine structure parameter adaptation value is more excellent, i.e. thrust maximum fluctuation minimum in the unit effective volume; At first algorithm parameter initialization, and the initialization structural parameters calculate the thrust and the undulating value of motor under these structural parameters by the neural net that training is finished, and calculate the adaptive value of this structure as the initial point of search in restriction range, then judge whether to satisfy end condition, stop if satisfying algorithm, otherwise, according to the rule of algorithm, update algorithm, produce new structural parameters, calculate, till satisfying end condition.
Described a kind of optimal design method for structural parameters of cylindrical linear motors, it estimates the computational methods of the adaptive value of structural parameters of cylindrical linear motors, and the computing function of its adaptive value is:
In the formula,
Be structural parameters, the fluctuation of std (force) expression thrust, the average of mean (force) expression thrust size, volume is the motor effective volume, subscript s, t, r represent the weight relationship of force oscillation, thrust size, volume respectively, and big more this index that shows of value is important more.
Beneficial effect
The present invention uses Finite Element Method motor is carried out Accurate Analysis, use neural net that the parameter of electric machine and thrust and undulating value are carried out match, and utilize training to finish the relation that neural net is predicted the parameter of electric machine and performance, and it adaptive value that is applied to intelligent optimization method calculated, in the algorithm optimization process, compare and directly use Finite Element Method to calculate the parameter of electric machine and thrust and undulating value, and then calculate the method for adaptive value, can effectively reduce motor FEM (finite element) calculation number of times.This method clear thinking is understood, is easy to through engineering approaches and uses, and it is inaccurate effectively to avoid in the engineering motor magnetic circuit complexity to cause lumped parameter to calculate, and the parameter optimization difficulty that causes of intercoupling between the parameter etc.The present invention also can be used for the Design of Structural parameters of arbitrary structures motor.
Description of drawings
The overview flow chart of Fig. 1 structural parameters of cylindrical linear motors optimal design;
Neural metwork training method in Fig. 2 structural parameters of cylindrical linear motors optimal design;
Fig. 3 intelligent optimization method is used for structural parameters of cylindrical linear motors and optimizes flow chart, and wherein left side figure is the particle cluster algorithm flow process, and right figure is the genetic algorithm flow process;
Fig. 4 cylindrical linear structure title and Parameter Map;
Wherein: 1-groove, 2-tooth, 3-tooth pin, 4-notch, 5-permanent magnet, 6-iron core, parameter is: motor outer radius Rt, groove outer radius Rw, groove inside radius Rs, tooth pin outer radius Rh, iron yoke inside radius Re, width of air gap g, permanent magnet outer radius Rm, permanent magnet inside radius Rb, inside radius Ro unshakable in one's determination, pole span 2*Tp, permanent magnet width Tm, slot pitch Wp, groove width W
L, slot opening Wm, the high h of groove.
The electric diagram of Fig. 5 embodiment of the invention cylindrical linear armature winding;
The engineering drawing (part) of the cylindrical linear that the optimization of Fig. 6 embodiment of the invention obtains.
Embodiment
Below in conjunction with drawings and Examples the present invention is elaborated.
In order to introduce the present invention in detail, at first introduce the finite element method that uses among the present invention.Use finite element method, at first set up the physical model of cylindrical linear.Cylindrical linear has axisymmetric feature, thus easily carry out two-dimentional axial symmetry analysis, and do not influence accuracy, a semi-section carries out modeling under the axial symmetry; After physical model is finished in foundation, then need the characteristic of each entity area respective material of assignment, such as air, iron yoke, permanent magnet, coil etc. are arranged; Then the physical model behind this definition material has been carried out mesh generation, generation unit and node, and, unit or node are applied electric current, definition constraints according to the excitation and the binding characteristic of motor, in order to obtain the physical characteristic of respective regions, it is applied power sign or definition path etc.; After above pre-treatment is finished,,, model is carried out numerical computations according to finite element method according to the loading and the constraint of unit; After calculating is finished, both can check the power size and observe magnetic circuit feature, acquisition magnetic flux density distribution situation etc.; The comprehensive Finite Element Method of using is analyzed interior diverse location of pole span distance of motor operation, finally obtains under the given structural parameters condition thrust of motor and undulating value.The thrust magnitude that all relate among the present invention is the average of the interior thrust of pole span distance of motor operation, and undulating value is the standard deviation of the interior thrust of pole span distance of motor operation.
In order to introduce the present invention in detail, below introduce the system of selection of the structural parameters of cylindrical linear motors that uses among the present invention.In the practical work process, at first according to the concrete requirement of engineering of reality, installation external diameter size such as motor, the thrust size that needs, and then establish motor fixed structure parameter really, such as the selection of each component materials, adopting has the brushless mover armature core of groove external structure, the mover overall length, winding number etc.And for some details parameters, these parameters have fundamental influence to motor properties, such as permanent magnet magnet steel width, permanent magnet thickness, width of air gap, then can't determine, with these parameters as structural parameters to be optimized.Then as required, determine the feasible region of these parameters to be optimized.
To n structural parameters to be optimized, in the feasible region of each structural parameters, once sample, form the sample point of a n dimensional vector, each dimension of sample point is represented the numerical values recited of corresponding construction parameter.According to the data of sample point, determine parameter in conjunction with all the other structures of all having determined, promptly formed a kind of design of Structural Parameters scheme of cylindrical linear.The target of optimizing is exactly to seek optimum sample point as far as possible, makes the motor properties optimum.
In order to introduce the present invention in detail, below introduce the training method of the neural net of using among the present invention.As Fig. 2, m sample point (m＞2*n) at first samples, with the design of Structural Parameters scheme of these sample points as m kind cylindrical linear, use finite element method, calculate the thrust and the undulating value of motor in the unit volume, with the input variable of each sample point structural parameters as neural net, with the thrust of motor and undulating value as output variable, neural network training.The present invention uses the BP neural net, comprises in the BP network that a plurality of hidden layers, the neuron in the hidden layer all adopt S type swap block, and the neuron of output layer adopts pure linear transformation function.
(k＞n), use the neural net after training that the thrust and the undulating value of each sample dot structure parameter are predicted uses finite element methods to calculate to these sample points simultaneously, obtains actual thrust and undulating value size then to produce k sample point.Then with each calculating particles predicted value f
(s) iWith estimated value f
(r) iSquare-error get and
If err is less than a threshold value of appointment, think that then neural metwork training is successful, otherwise think that neural net still can not well approach the corresponding relation of practical structure parameter and motor performance, reselect k sample point and carry out same calculating, up to meeting the demands, obtain till the satisfied neural net.In addition, the selection of each sample point can not overlap with existing sample point, should make the distance between current sample point and the existing sample point less as far as possible.This moment given structural parameters, neural net exports that motor thrust and undulating value predict the outcome under these structural parameters, thinks this result and actual matching.
In order to introduce the present invention in detail, introduce at last the structural parameters of cylindrical linear motors intelligent optimization method that uses among the present invention.Current intelligent optimization method commonly used, such as particle cluster algorithm, genetic algorithm, workflow as shown in Figure 1, at first algorithm parameter initialization, and in restriction range the initialization structural parameters as the search initial point, the BP neural net of finishing by training is calculated the thrust and the undulating value of motor under these structural parameters, and then calculate the adaptive value that obtains this structural parameters motor performance, and then judge whether to satisfy end condition, stop if satisfying algorithm, otherwise, according to the rule of algorithm, update algorithm produces new structural parameters, calculate, till satisfying end condition.End condition can be appointment iterations, stagnate step number or adaptive value condition and satisfy etc.
As shown in Figure 3, the left side is the particle cluster algorithm flow chart, algorithm initialization particle position and speed in restriction range at first, the BP analysis of neural network motor properties that each particle position is finished as electric machine structure parameter combined training, if algorithm satisfies end condition and then stops, otherwise according to particle cluster algorithm more new formula upgrade particle position and speed; The right side is the genetic algorithm flow chart, at first rational algorithm initialization is set according to the structural parameters restriction range, comprise the algorithm parameter setting, the coding and decoding rule, individual choice rule etc., finish the initialization codes of colony simultaneously, then the coding of each individuality in the colony is deciphered the structural parameters that obtain motor, the adaptive value size of motor performance under these structural parameters of BP analysis of neural network that combined training is finished, if algorithm satisfies end condition and then stops, otherwise according to genetic algorithm according to heredity, intersect, the variation rule is carried out genetic manipulation, generates new colony.Wherein the adaptive value computational methods of motor performance are as follows:
In the following formula,
Be structural parameters, the fluctuation of std (force) expression thrust, the size (average) of mean (force) expression thrust, volume is the motor effective volume, subscript s, t, r represent the weight relationship of force oscillation, thrust size, volume respectively, and big more this index that shows of value is important more.
Embodiment
At first determine the type of motor.According to the concrete engineering demand of present embodiment, adopt three phase windings, and every groove embeds single-phase single layer winding, for motor is operated steadily, the number of using three phase windings should be 6 integral multiple, the groove number also is 6 integral multiple, simultaneously, permanent magnet adopts diametrical magnetization, be alternately distributed, magnetic direction is alternately distributed inside and outside the motor traffic direction, and then briefly analyze according to the present embodiment actual requirement of engineering and in conjunction with Finite Element Method, determine that the linear electric motors winding distributes as shown in Figure 5, partial parameters is as shown in table 2: winding adopts whole apart from distributed winding, adopts the method for attachment of " Y " type, and the winding current phase sequence in each groove is: A+, A-, B-, B+, C+, C-, A-, A+, B+, B-, C-, C+, A+, A-, B-, B+, C+, C-, A-, A+, B+, B-, C-, C+; Each determines that parameter is as shown in table 2: motor outer radius Rt=60mm; Groove outer radius Rw=55.0mm; Groove inside radius Rs=40.2mm; Pole span 2*Tp=2*24.3mm; Permanent magnet inside radius Rb=31mm; Slot pitch Wp=21.5mm; Groove is counted n=24; Groove width W
L=13.4mm; Slot opening Wm=0.2mm; Inside radius Ro=15mm unshakable in one's determination; The high Rs-Rh=1.2mm in groove tooth inclined-plane; The high Rh-Re=0.8mm of notch.Needing optimum parameters is permanent magnet width Tm, permanent magnet thickness Rm-Rb, width of air gap g, and simultaneously that couple variations is the high h of groove, parameter be constrained to Tm＜Tp, Rm＞Rb, g＞0.
To needing the structural parameters of optimal design, use intelligent optimization method to carry out the parameter optimization design then.Present embodiment adopts particle swarm optimization algorithm, and as parameter optimization method, the target of optimization is the adaptive value minimum that makes motor performance.The s=1 of adaptive value computing function, t=1.5, r=1 in the present embodiment, in the expression present embodiment, the volume volume (m of unit
3) be fixed value, most important optimization aim is to obtain bigger thrust, and the importance of force oscillation is taken second place.According to the manufacturing process ability, the precision of all parameters optimization is 0.1mm.Algorithm parameter is provided with as shown in table 1 below, and population size is 20, iterations 200, inertial factor w from 0.9 to 0.4 linear decrease, study factor c
1, c
2Be 2.05.
The setting of table 1 algorithm parameter
| Parameter | Be provided with |
| |
20? |
| Iterations | 200? |
| Inertial factor w | 0.9 ~ 0.4 successively decreases |
| Study factor c 1，c 2 | 2.05? |
Wherein parameter of electric machine setting and optimization result are as shown in table 2 below, permanent magnet width Tm=21.5mm, and permanent magnet thickness Rm-Rb=5.5mm, width of air gap g=1.7mm, the high h=14.8mm of groove, the engineering drawing of the cylindrical linear of optimal design is as shown in Figure 6.
Table 2 linear electric motors parameter is provided with and the optimization result
| Title | Optimization range constraint and remarks | Design result | Title | Optimization range constraint and remarks | Design result |
| Motor outer radius Rt | Specify | 60mm? | Groove outer radius Rw | Specify | 55.0m m |
| Groove inside radius Rs | The Rh+1.2 groove tooth high 1.2mm in inclined-plane | 40.2mm? | Tooth pin outer radius Rh | The high 0.8mm of Re+0.8 notch | 39mm? |
| Iron yoke inside radius Re | (Rm, Rm+g) width of air gap is gmm | 38.2mm? | Permanent magnet outer radius Rm | ?(Rb?Re)? | 36.5m m |
| Permanent magnet inside radius Rb | Specify | 31mm? | Inside radius Ro unshakable in one's determination | Specify | 15mm? |
| Slot pitch Wp | Specify | 51.5mm? | Groove width W L | Specify | 13.4m m |
| Slot opening Wm | Specify | 0.2mm? | Groove is counted n | Specify | 24? |
| |
Specify | 2*24.3m m? | Permanent magnet width Tm | ?(0，Tp)? | 21.5m m |
Motor maximum thrust 3439N after the optimization, minimum thrust 2963N, average 3224N, the fluctuation ratio is 7.4%, adaptive value is 8.3573.Although it is relatively large that the result who optimizes fluctuates, thrust is bigger, meets the requirement of design high thrust cylindrical linear, and this design synthesis adaptive value is less, adheres to specification, and the parameter optimization result is rationally effective simultaneously.
Above-described only is preferred embodiment of the present invention, and the present invention not only is confined to the foregoing description, all any changes of being done within the spirit and principles in the present invention, is equal to replacement, improvement etc. and all should be included within protection scope of the present invention.
Claims (2)
1. an optimal design method for structural parameters of cylindrical linear motors is characterized in that: at first satisfying under the structural parameters restriction range condition, determining motor fixed structure parameter really, selecting motor parameter to be optimized; Secondly to the dynamic sampling of structural parameters, use finite element method, the thrust and the fluctuating nature thereof of cylindrical linear are analyzed; Then structural parameters are imported as neural net, thrust and undulating value are exported as neural net, and neural network training is to carry out match to the functional relation of structural parameters and thrust and fluctuation; Last in conjunction with the neural net after the match, use intelligent optimization method that structural parameters are optimized, obtain the adaptive value of more excellent structural parameters, thereby obtain the performance of interior thrust of more excellent motor unit volume and fluctuation; Wherein, neural network training is to adopt the method for dynamic neural network training that neural net is trained, select the training of part sample during beginning, stochastical sampling part new sample point again then, calculate new sample point and use the thrust and the undulating value of neural network prediction, with the thrust of actual FEM (finite element) calculation and the error of undulating value, if error is more then proceeded training, till obtaining satisfied neural net; Wherein, the intelligent optimization method that structural parameters of cylindrical linear motors is optimized is: the target of optimization is exactly to seek optimum sample point as far as possible, makes that electric machine structure parameter adaptation value is more excellent, i.e. thrust maximum fluctuation minimum in the unit effective volume; At first algorithm parameter initialization, and the initialization structural parameters calculate the thrust and the undulating value of motor under these structural parameters by the neural net that training is finished, and calculate the adaptive value of this structure as the initial point of search in restriction range, then judge whether to satisfy end condition, stop if satisfying algorithm, otherwise, according to the rule of algorithm, update algorithm, produce new structural parameters, calculate, till satisfying end condition; Wherein, estimate the computational methods of the adaptive value of structural parameters of cylindrical linear motors, the computing function of its adaptive value is:
In the formula,
Be structural parameters, the fluctuation of std (force) expression thrust, the average of mean (force) expression thrust size, volume is the motor effective volume, subscript s, t, r represent the weight relationship of force oscillation, thrust size, volume respectively, and big more this index that shows of value is important more.
2. a kind of optimal design method for structural parameters of cylindrical linear motors according to claim 1, it is characterized in that: motor parameter selection method to be optimized is: at first according to the concrete requirement of engineering of reality, determine motor fixed structure parameter really, selection has the structural parameters to be optimized of fundamental influence to be optimized to motor properties, and determines the feasible region of structural parameters to be optimized; Then in feasible region, carry out stochastical sampling, the motor of sampled point structural parameters is carried out finite element analysis, obtain motor thrust and undulating value.
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| CN104483829B (en) * | 2014-12-31 | 2017-06-09 | 重庆科技学院 | The acquisition methods and device of controlled device frequency domain performance |
| CN104598686A (en) * | 2015-01-24 | 2015-05-06 | 安徽大学 | Water pump motor modeling and optimization method based on electromagnetic calculation and neural network |
| CN106055736A (en) * | 2016-05-13 | 2016-10-26 | 安徽大学 | Novel optimal design method for permanent magnet synchronous linear motor |
| CN105915005B (en) * | 2016-06-23 | 2017-06-30 | 山东大学 | For straight tiltedly compound stator winding slotless electric machines and the optimization method of artificial heart pump |
| CN108365784A (en) * | 2017-11-24 | 2018-08-03 | 天津大学 | Based on the control method for brushless direct current motor for improving PSO-BP neural networks |
| CN111478466A (en) * | 2020-04-14 | 2020-07-31 | 合肥工业大学 | Optimization design method for synchronous reluctance motor rotor |
| CN111898286B (en) * | 2020-04-24 | 2024-05-14 | 中国北方车辆研究所 | Motor modeling analysis and optimization method |
| CN114186442B (en) * | 2020-09-14 | 2023-06-16 | 北京理工大学 | Lattice material parameter optimization method based on neural network model and numerical simulation |
| CN112152529B (en) * | 2020-09-28 | 2022-08-12 | 长沙贝士德电气科技有限公司 | Maximum thrust control method and system for permanent magnet linear motor |
| CN112364431B (en) * | 2020-10-12 | 2024-04-05 | 宁波思明汽车科技股份有限公司 | Optimization method of energy feedback device of energy feedback suspension |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2757420Y (en) * | 2004-05-12 | 2006-02-08 | 陈天为 | Cylindrical permanent magnet straight synchronous motor |
| CN1737708A (en) * | 2005-05-18 | 2006-02-22 | 江苏大学 | Nerval net based inverse control system for permanent-magnet synchronous motor with five degrees of freedom without bearing and control method |
-
2009
- 2009-03-06 CN CN2009100087381A patent/CN101567617B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2757420Y (en) * | 2004-05-12 | 2006-02-08 | 陈天为 | Cylindrical permanent magnet straight synchronous motor |
| CN1737708A (en) * | 2005-05-18 | 2006-02-22 | 江苏大学 | Nerval net based inverse control system for permanent-magnet synchronous motor with five degrees of freedom without bearing and control method |
Non-Patent Citations (1)
| Title |
|---|
| 赵吉文，刘永斌，孔凡让，张平，孙丙宇.基于SVM和遗传算法的新型直线电机结构参数优化.《光学精密工程》.2006,第14卷(第5期),870-875. * |
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