CN1482731A - Construction method for contradictorily synchronous control frequency-converter of two-phase induction motor neural net - Google Patents
Construction method for contradictorily synchronous control frequency-converter of two-phase induction motor neural net Download PDFInfo
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Abstract
A construction method of the neural net reversed holding control frequency-converter for the double induction motor comprising, building-up integrally a composite controlled object including magnetic linkage observer, two expanded pressure control inverter, two controlled induction motor and their common load, constructing neural net reverse using static state neural net plus integrator and through a learning algorithm, connecting the neural net reversion in front of the composite controlled object, recombining the linear system, generating a closed-loop control device based on the method for designing of the linear system, finally joining in series connection the neural net reverse and the linear closed-loop control, and shaping neural net reverse synchronizing control converter along with the expanded voltage control inverter, thus the holding control for two induction motor is realized.
Description
One. technical field
The present invention is a kind of synchronisation control means of two AC induction motor systems, is applicable to that two induction alternating current (AC) motors (abbreviation induction machine) drive the high performance control of common load (band shape load), belong to the technical field of Electric Drive control appliance.
Two. background technology
At present, in industrial production, exist in a large number by two motors (even multiple electric motors) and drive common load (as the band shape load etc.), the most of direct current machine that adopts of system with identical speed synchronous operation.Because the DC motor structure complexity is difficult in maintenance, and has the commutation problem, this brings inconvenience to use.Although adopt frequency converter to drive many fields that induction machine has been widely used in the transmission of former employing direct current machine at present, a frequency converter can be controlled an induction machine preferably and drive load running.But the system's (be called for short the two induction system, further abbreviate two induction as) to two common load runnings of driven by motor only adopts frequency converter can't satisfy actual requirement (keeping constant tension force and system synchronization operation as the band shape load).Because in two induction, not only have the speed of every induction machine and intercoupling of rotor flux, and because the tension force of two induction is relevant with the difference of the speed of two induction machines, so there be intercoupling between the speed of two induction and the tension force.The two induction system is the coupled system of the multivariable nonlinearity of a complexity, adopts constant voltage and frequency ratio control of conversion device or adopts vector-control frequency converter all to be difficult to make two induction machines that drive common load to realize synchronous operation no matter be.
The employing method is to increase isochronous controller at present, clearly increases isochronous controller the cost of system is uprised, and realizes difficulty simultaneously, is difficult to the high performance synchronous operation that reaches real.Therefore concerning two induction, adopting frequency converter to add the working method that the induction motor adds isochronous controller usually is not the most effective control mode.
For two induction changes parameter and the adaptability and the robustness of disturbance from improving in essence, realize the speed of two induction and the decoupling zero control of tension force, and then the runnability of raising two induction Synchronization Control, realize real high performance synchronous operation, need to adopt some new control technologys and new control method.
Three. technology contents
1, technical problem
The purpose of this invention is to provide and a kind ofly both can make every motor have good dynamic and static control performance, anti-parameter of electric machine variation and anti-load disturbance ability are strong, can improve every control performance index of two induction again effectively, as the building method of the nerve network reverse Synchronization Control frequency converter of the two induction of dynamic responding speed, steady-state tracking precision and parameter robustness.
2, technical scheme
The building method of the nerve network reverse Synchronization Control of two induction is, at first adopt two flux observers of electric current commonly used, speed flux observation model and Clarke Clark conversion composition, obtain the rotor flux information of two required induction machines of magnetic linkage closed-loop control; And with the Voltage-controlled Current Source inverters (the voltage-controlled inverter that is called for short expansion) of the contrary Parker Park conversion of two general Voltage-controlled Current Source inverters, two and two common two expansions that form of contrary Clarke Clark conversion a part as the Neural network inverse control frequency converter of constructing thereafter; Then as a whole composition composite controlled object is made in the voltage-controlled inverter and the common load (band shape load such as conveyer belt) thereof of controlled two induction and two expansions, the controlled volume of composite controlled object is the rotor flux of speed (as the speed of conveyer belt) and tension force (as the tension force of conveyer belt) and two induction machines; And then adopt static neural network to add integrator s
-1Construct the nerve network reverse of composite controlled object, and make nerve network reverse realize the inverse system function of composite controlled object by the weight coefficient of adjusting neural net; Next nerve network reverse is placed before the composite controlled object, nerve network reverse and composite controlled object are formed pseudo-linear system.Pseudo-linear system is equivalent to the integral linearity subsystem of four decoupling zeros, be respectively the linear subsystem of two magnetic linkage second order integral forms, the linear subsystem of speed second order integral form and the linear subsystem of tension force three rank integral forms, i.e. two of two induction machine correspondences rotor flux subsystems, speed subsystem and tension force subsystems, thereby not only make between the rotor flux of every induction machine and the speed and realize dynamic decoupling, realize decoupling zero but also make between the speed of two induction and the tension force; On this basis, the integrated approach that adopts a kind of linear system designs two rotor flux controllers, a speed control and a tension controller respectively to the integration subsystem of four decoupling zeros, and constitutes the linear closed-loop controller by rotor flux controller, speed control and tension controller; At last the voltage-controlled inverter of linear closed-loop controller, nerve network reverse and flux observer and two expansions being constituted the Neural network inverse control frequency converter jointly comes two induction is controlled, to obtain the fine control performance index, realize the high performance control of two induction.
3, technique effect
Principle of the present invention is contrary by constructing neural network, will to this multivariable of two induction, close coupling, the time become non linear system control be converted into control to three rank integral linearity subsystems of the second order integral linearity subsystem of two rotor fluxs, a speed and a tension force, correspondingly just can design linear closed loop controller easily.Owing to really realized speed and the control of the decoupling zero between the tension force to rotor flux and the dynamic decoupling between the speed and the two induction of each induction machine, thereby not only can distinguish the effective control that realizes independently two induction machine speed and rotor flux, and can distinguish the effective control that independently realizes two induction speed and tension force, obtain good speed and tension adjustment performance.Owing to adopted the neural net that does not rely on the controlled device Mathematical Modeling to realize the inverse system function, thereby improved robustness greatly to parameter of electric machine variation and load disturbance.
The invention has the advantages that:
1. employing nerve network reverse, with this controlled volume of two induction (speed of each induction machine and rotor flux, the speed of two induction and tension force) four inputs (given and two velocity setting of two rotor fluxs of two induction machines) four that intercouple export (speed of conveyer belt, the rotor flux of the tension force of conveyer belt and two induction machines) control problem of complex nonlinear coupled system is converted into simple four linear subsystems (two rotor flux linear subsystems, a speed linear subsystem and a tension force linear subsystem) control problem, further appropriate design linear closed-loop controller can obtain the runnability of high performance Synchronization Control and anti-load disturbance.
2. add the inverse system that integrator is realized composite controlled object with static neural network, the contrary synchronous frequency changer of constructing neural network is realized the control to two induction, be completely free of the dependence of traditional Control of Induction Motors method for Mathematical Modeling, reduced the parameter of electric machine effectively and changed and the influence of load disturbance, improved the performance index of two induction control significantly two induction.
The present invention can be used for constructing novel synchronous control of conversion device two induction is carried out high performance control, not only in the synchronous control system that with the induction machine is power set, very high using value is arranged, and be in the synchronous control system of power set at the alternating current machine with other type, application prospect also is very wide.
Four. description of drawings
The rotor flux observer 1 that Fig. 1 is made up of electric current rotating speed magnetic linkage estimation model 11 and Clarke Clark conversion.
Fig. 2 is made of the structure chart of the voltage-controlled inverter 3 of expansion jointly coordinate transform 31, Voltage-controlled Current Source inverter 32.Wherein by Voltage-controlled Current Source inverter 32 and the coordinate transform 31 formed by contrary Parker Park conversion and contrary Clarke Clark conversion.
Fig. 3 is the principle assumption diagram (composite controlled object 5) that drives common loads 4 (band shape load) with the two induction 2 of voltage-controlled inverter 3 drivings of expansion.The voltage-controlled inverter 3 and the common load 4 of two induction electromotor rotor flux observers 1, two induction machines 2 (No. 1 induction machine and No. 2 induction machines), two expansions are wherein arranged.
Fig. 4 is each the induction electromotor rotor flux observer 1 of two induction correspondence and the concrete principle assumption diagram of the induction machine 2 (No. 1 induction machine or No. 2 induction machines) of voltage-controlled inverter 3 drivings of expanding.
Fig. 5 is the Mathematical Modeling schematic diagram and the isoboles thereof of the induction machine 2 that drives of the voltage-controlled inverter 3 of each rotor flux observer 1 of two induction correspondence and expansion (No. 1 induction machine or No. 2 from induction machine).
Fig. 6 is the equivalent control block diagram (composite controlled object 5) of two induction four inputs (input of the voltage-controlled inverter of two expansions) and four outputs (rotor flux of two induction, speed and tension force).
Fig. 7 is the schematic diagram and the isoboles thereof of the pseudo-linear system 7 of nerve network reverse 6 and composite controlled object 5 compound formations.Integrator s is wherein arranged
-1, static neural network 61, pseudo-linear system 7.
Fig. 8 is two component of voltage signals (the voltage-controlled inverter input end signal that is added to No. 2 expansions is identical) that the voltage-controlled inverter input that is added in the composite controlled object shown in Figure 35 No. 1 expansion is used to obtain the neural metwork training data.
The structure chart of the closed-loop control system that Fig. 9 is made up of linear closed-loop controller 8 and pseudo-linear system 7.Wherein pseudo-linear system 7 comprises two rotor flux subsystems 71 and 73, speed subsystems 72 and a tension force subsystem 74; The linear closed-loop controller comprises two rotor flux controllers 81 and 83, speed controls 82 and a tension controller 84.
Figure 10 is the control principle block diagram of whole system.
Figure 11 is the complete principle block diagram that adopts 9 pairs of two inductions 5 of nerve network reverse Synchronization Control frequency converter to control.
Figure 12 adopts DSP to form schematic diagram as apparatus of the present invention of nerve network reverse Synchronization Control frequency converter.DSP10, photoelectric encoder 12 are wherein arranged.
Figure 13 is to be the realization systems soft ware block diagram of the present invention of controller with DSP.
Five. specific embodiments
Embodiment of the present invention are: at first formed the voltage-controlled inverter of expansion jointly by Voltage-controlled Current Source inverter, contrary Parker Park conversion and contrary Clarke Clark conversion, the voltage-controlled inverter of this expansion will be as a part of whole nerve network reverse Synchronization Control frequency converter.Secondly with the voltage-controlled inverters of two expansions and two induction machines and load (band shape load such as conveyer belt) thereof as a composite controlled object, this composite controlled object equivalence is nine rank Differential Equation Models under the rotor flux coordinate system, and the vector of system rank relatively is { 2,2,2,3}.Adopt the static neural network (static neural network is multitiered network MLN) of 13 input nodes, 4 output nodes to add 9 integrator (s
-1) construct the nerve network reverse of composite controlled object.And make nerve network reverse realize the inverse system function of composite controlled object by each weight coefficient of adjusting static neural network.Nerve network reverse is serially connected in before the composite controlled object, nerve network reverse and composite controlled object synthesize by three second order integration subsystem (s again
-2) i.e. two rotor flux subsystems and a speed subsystem and one three rank integration subsystem (s
-2) be the pseudo-linear system that the tension force subsystem constitutes, thus the control of the nonlinear multivariable systems of a complexity is converted into the control that three simple second-order integration subsystems add simple three rank integration subsystems.Three second order integration subsystems and one three rank integration subsystem for decoupling zero, adopt a kind of linear system integration method, as PID or POLE PLACEMENT USING etc., make two rotor flux controllers, a speed control and a tension controller respectively, rotor flux controller, speed control and the linear closed loop controller of tension controller mutual group.Final formation by the voltage-controlled inverter of nerve network reverse, linear closed-loop controller, expansion and flux observer totally 4 nerve network reverse Synchronization Control frequency converters of partly forming comes two induction is controlled.According to different control requirements, can select different hardware and softwares to realize.
Concrete enforcement divides following 9 steps:
1. structure rotor flux observer.Two induction machines are constructed rotor flux observer (being called for short flux observer as shown in Figure 1) respectively, and flux observer is made up of electric current, speed magnetic linkage estimation model and Clarke Clark conversion commonly used.Flux observer be input as induction machine stator phase current i
a, i
bAnd speed omega
r, be output as rotor flux angle θ and rotor flux ψ
rWherein rotor flux angle θ will be used to realize Parker Park transform operation and contrary Parker Park transform operation, rotor flux ψ
rWill be as the feedback quantity of magnetic linkage closed-loop control.Flux observer will be as a part of whole nerve network reverse Synchronization Control frequency converter.
2. construct the voltage-controlled inverter of expansion.At first form coordinate transform by contrary Parker Park conversion and contrary Clarke Clark conversion, afterwards this coordinate transform and Voltage-controlled Current Source inverter commonly used are formed jointly the voltage-controlled inverter of expansion, the voltage-controlled inverter of this expansion is its input (as shown in Figure 2) with two stator voltage components.The voltage-controlled inverter of expansion will be as a part of whole nerve network reverse Synchronization Control frequency converter.
3. formation composite controlled object.The voltage-controlled inverter of two expansions that structure is good, two induction machine flux observers and controlled two induction machines and common load (band shape load) composition composite controlled object, this composite controlled object is its input with four stator voltage components, and the speed and the tension force of two induction electromotor rotor flux observation values, two induction are output (as shown in Figure 3).
By analyze, equivalence and derivation, for the structure of nerve network reverse and learning training provide basis on the method.At first set up the Mathematical Modeling of each compound controlled subobject (as shown in Figure 4), promptly set up the Mathematical Modeling of voltage-controlled inverter of each induction machine, each expansion and the Mathematical Modeling of each induction electromotor rotor flux observer, and through equivalence shown in Figure 5.Consider the common load of two induction machines and two induction machines then, the equivalent mathematical model (schematic diagram as shown in Figure 6) of composite controlled object, i.e. the nine rank differential equations the rotor flux coordinate system under, the relative rank of its vector be 2,2,2,3}.Inverse system through provable this nine rank Differential Equation Model of deriving exists, and four inputs can determining its inverse system are No. 1 induction electromotor rotor magnetic linkages second dervative, the second dervative of two induction speed, the second dervative of No. 2 induction electromotor rotor magnetic linkages and three order derivatives of two induction tension force, four outputs are respectively four inputs of composite controlled object.Need to prove, this step only provides basis on the method for the structure of following nerve network reverse and learning training, in concrete enforcement of the present invention, and this step, comprise theoretical proof and some corresponding equivalent transformations and derivation etc. that the composite controlled object inverse system is existed, can skip.
5. adopt static neural network to add nine integrator constructing neural networks contrary (shown in the frame of broken lines of the left figure of Fig. 7).Wherein static neural network adopts 3 layers MLN network, and the input layer number is 13, and the hidden layer node number is 19, and output layer node number is 4, and the hidden neuron activation primitive uses S type hyperbolic tangent function
The neuron of output layer adopts pure linear function f (x)=x, and x is neuronic input, and the weight coefficient of static neural network will be determined in next step off-line learning.Add 9 integrator s with static neural network then with 13 input nodes, 4 output nodes
-1Constitute nerve network reverse (shown in the frame of broken lines of the left figure of Fig. 7), wherein first of static neural network is input as first input of nerve network reverse, and it is through first integrator s
-1Being output as second input of static neural network, is the 3rd input of static neural network again through second integrator; The 4th second input that is input as nerve network reverse of static neural network, it is through the 3rd integrator s
-1Being output as the 5th input of static neural network, is the 6th input of static neural network again through the 4th integrator.The 7th the 3rd input that is input as nerve network reverse of static neural network, it is through the 5th integrator s
-1Being output as the 8th input of static neural network, is the 9th input of static neural network again through the 6th integrator; The tenth the 4th input that is input as nerve network reverse of static neural network, it is through the 7th integrator s
-1Be output as the 11 input of static neural network, the 12 input that is static neural network through the 8th integrator again is after the 9th the 13 input that integrator is a static neural network.Static neural network is formed nerve network reverse with nine integrators, and the output of static neural network is exactly the output of nerve network reverse.
6. adjust the weight coefficient of static neural network.(a) two component of voltages as shown in Figure 8 are added to the voltage-controlled inverter of expanding for No. 1 and No. 2 (being the input of composite controlled object) respectively with the form of importing, with 6 milliseconds sampling periods collection induction machine speed omega
R1, ω
R2With current i
A1, i
B1And i
A2, i
B2And tension force F, according to ω
R1, ω
R2With i
A1, i
B1And i
A2, i
B2, obtain two rotor flux ψ by two rotor flux observers
R1And ψ
R2, and preserve data { ψ
R1, ω
R2, ψ
R2, F}.(b) two rotor fluxs and rate signal off-line are asked its single order and second dervative respectively, the tension signal off-line is asked its single order, second dervative and three order derivatives respectively, and signal is done standardization processing, the training sample set of composition neural net
ψ
R1,
ω
R1,
ψ
R2,
F, u
Sm1 *, u
St1 *, u
Sm2 *, u
St2 *.(c) adopt the error anti-pass BP algorithm that drives quantifier and learning rate changing that static neural network is trained, through 500 training, neural net output mean square error meets the demands less than 0.001, thereby has determined each weight coefficient of static neural network.
7. form two rotor flux subsystems, a speed subsystem and a tension force subsystem.Constitute nerve network reverse (shown in the frame of broken lines among the left figure of Fig. 7) by the static neural network of having determined each weight coefficient and 9 integrators, nerve network reverse and composite controlled object compose in series pseudo-linear system (shown in the right figure of Fig. 7), this pseudo-linear system is made of jointly two rotor flux subsystems, a speed subsystem and a tension force subsystem, thereby reached between speed and the rotor flux, the decoupling zero between speed and the tension force, complicated nonlinear multivariable systems control has been converted into the control of simple four single argument linear systems.
8. make the linear closed-loop controller.Two rotor flux subsystems, speed subsystem and a tension force subsystem (shown in the right figure of Fig. 7) are made linear closed-loop controller (as shown in Figure 9) respectively.The linear closed-loop controller adopts proportional plus integral plus derivative controller PID, POLE PLACEMENT USING or the most excellent method of quadratic performance in the lineary system theory to design, in the embodiment that the present invention provides, two magnetic linkage control devices, a speed control and a tension controller have all been selected proportion differential PD controller for use, its parameter tuning is that two magnetic linkage control devices are PD=1000+54s, speed control is PD=200+22s, and tension controller is PD=100+15s.Whole system as shown in figure 10.
9. form nerve network reverse Synchronization Control frequency converter.Voltage-controlled inverter and two flux observers of nerve network reverse, linear closed-loop controller, two expansions are formed nerve network reverse Synchronization Control frequency converter (as shown in big frame of broken lines among Figure 11) jointly.Can require to adopt different hardware or software to realize according to different control.
Figure 12 has provided the schematic diagram of a kind of specific embodiment of the present invention, and wherein nerve network reverse, closed loop controller, coordinate transform and flux observer are that dsp controller is realized by software by digital signal processor; The voltage-controlled inverter of expansion adopts Intelligent Power Module to realize.The system program block diagram as shown in figure 13.Controlled induction machine model is Y90S-4, and the parameter of electric machine is P
e=1.1kW; U
e=220/380 V; I
e=2.7A; f
e=50Hz; n
p=2; ω
e=1400rpm.
According to the above, just can realize the present invention.
Claims (3)
1, a kind of building method of nerve network reverse Synchronization Control frequency converter of two induction is characterized in that this method does as a whole composition composite controlled object (5) to flux observer (1), controlled two induction (2) with the voltage-controlled inverter (3) and the common load (4) thereof of expansion; And then adopt static neural network to add integrator s
-1Construct the nerve network reverse (6) of composite controlled object, and make nerve network reverse (6) realize the inverse system function of composite controlled object (5) by the weight coefficient of adjusting neural net; Then nerve network reverse (6) is placed composite controlled object (5) before, nerve network reverse (6) is formed pseudo-linear system (7) with composite controlled object (5), pseudo-linear system (7) is equivalent to the integral linearity subsystem of four decoupling zeros, is respectively the linear subsystem (72) of the linear subsystem (71,73) of two magnetic linkage second order integral forms, a speed second order integral form and the linear subsystem (74) of tension force three rank integral forms; On this basis, the integrated approach that adopts a kind of linear system designs two magnetic linkage control devices (81,83), a speed control (82) and a tension controller (84) respectively to the integration subsystem of four decoupling zeros, and constitutes linear closed-loop controller (8) by two magnetic linkage control devices (81,83), a speed control (82) and a tension controller (84); At last the voltage-controlled inverter (3) of linear closed-loop controller (8), nerve network reverse (6) and two flux observers (1) and two expansions is constituted nerve network reverse Synchronization Control frequency converter (9) jointly and come two induction is carried out Synchronization Control, obtain the fine control performance index.
2, the building method of the nerve network reverse Synchronization Control frequency converter of two induction according to claim 1, the building method that it is characterized in that nerve network reverse (6) is for adding 9 integrator s with the static neural network (61) with 13 input nodes, 4 output nodes
-1Constitute the nerve network reverse (6) with 4 input nodes, 4 output nodes, wherein: first of static neural network is input as first input of nerve network reverse, and it is through first integrator s
-1Being output as second input of static neural network, is the 3rd input of static neural network again through second integrator; The 4th second input that is input as nerve network reverse of static neural network, it is through the 3rd integrator s
-1Being output as the 5th input of static neural network, is the 6th input of static neural network again through the 4th integrator.The 7th the 3rd input that is input as nerve network reverse of static neural network, it is through the 5th integrator s
-1Being output as the 8th input of static neural network, is the 9th input of static neural network again through the 6th integrator; The tenth the 4th input that is input as nerve network reverse of static neural network, it is through the 7th integrator s
-1Be output as the 11 input of static neural network, the 12 input that is static neural network through the 8th integrator again is after the 9th the 13 input that integrator is a static neural network.Static neural network is formed nerve network reverse with nine integrators, and the output of static neural network is exactly the output of nerve network reverse.
3, the building method of the nerve network reverse Synchronization Control frequency converter of two induction according to claim 1 is characterized in that each weight coefficient of static neural network (61) determines that method is with step excitation signal u
Sm1 *, u
St1 *And u
Sm2 *, u
St2 *Be added to the input of composite controlled object (5); Gather the speed omega of No. 1 induction machine
R1With two stator phase current i
A1, i
B1Speed omega with No. 2 induction machines
R2With two stator phase current i
A2, i
B2And tension force F; According to ω
R1, ω
R2With i
A1, i
B1And i
A2, i
B2, obtain two rotor flux ψ by two rotor flux observers (1)
R1And ψ
R2With two rotor flux ψ
R1, ψ
R2And speed omega
R1, ω
R2Off-line is asked its single order and second dervative respectively, and the tension signal off-line is asked its single order, second order and three order derivatives, and signal is done standardization processing, the training sample set of composition neural net
ψ
R1,
ω
R1,
ψ
R2,
F, u
Sm1 *, u
St1 *, u
Sm2 *, u
St2 *.Static neural network (61) is trained, thus each weight coefficient of definite static neural network (61).
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1303753C (en) * | 2005-06-07 | 2007-03-07 | 南京工业大学 | Speedless sensor inverse controller of AC asynchronous motor |
| CN100426158C (en) * | 2005-12-22 | 2008-10-15 | 安徽工业大学 | Indirect decoupling method of multi variable system based on nerve network reverse idontification and reverso control |
| CN102361429A (en) * | 2011-09-13 | 2012-02-22 | 江苏大学 | Bearing-free asynchronous motor control method based on neural network inverse system theory |
| CN102751920A (en) * | 2012-06-21 | 2012-10-24 | 天津工业大学 | Method for identifying and controlling two-induction-motor synchronous system |
| CN103219936A (en) * | 2013-01-16 | 2013-07-24 | 镇江市江大科技有限责任公司 | Permanent magnet synchronous motor feed-forward decoupling method based on neural network online learning |
| CN108777556A (en) * | 2018-06-29 | 2018-11-09 | 江苏大学 | The model-free adaption robust decoupling control method of two motor speed regulation systems based on neural network contrary modeling |
-
2003
- 2003-07-28 CN CN 03131769 patent/CN1216454C/en not_active Expired - Fee Related
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1303753C (en) * | 2005-06-07 | 2007-03-07 | 南京工业大学 | Speedless sensor inverse controller of AC asynchronous motor |
| CN100426158C (en) * | 2005-12-22 | 2008-10-15 | 安徽工业大学 | Indirect decoupling method of multi variable system based on nerve network reverse idontification and reverso control |
| CN102361429A (en) * | 2011-09-13 | 2012-02-22 | 江苏大学 | Bearing-free asynchronous motor control method based on neural network inverse system theory |
| CN102751920A (en) * | 2012-06-21 | 2012-10-24 | 天津工业大学 | Method for identifying and controlling two-induction-motor synchronous system |
| CN103219936A (en) * | 2013-01-16 | 2013-07-24 | 镇江市江大科技有限责任公司 | Permanent magnet synchronous motor feed-forward decoupling method based on neural network online learning |
| CN103219936B (en) * | 2013-01-16 | 2015-05-20 | 镇江市江大科技有限责任公司 | Permanent magnet synchronous motor feed-forward decoupling method based on neural network online learning |
| CN108777556A (en) * | 2018-06-29 | 2018-11-09 | 江苏大学 | The model-free adaption robust decoupling control method of two motor speed regulation systems based on neural network contrary modeling |
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| Publication number | Publication date |
|---|---|
| CN1216454C (en) | 2005-08-24 |
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