CN1212702C - Neural network reversal control frequency converter of induction motor and structure method - Google Patents
Neural network reversal control frequency converter of induction motor and structure method Download PDFInfo
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Abstract
The present invention relates to a neural network reversed control frequency converter of an induction motor and a making method thereof, which is applicable to an induction motor for high performance control and belongs to the technical field of a power transmission control device. A voltage controlled voltage source inverter 1 and a coordinate converter 2 make an extended voltage control inverter 3, and an extended voltage control inverter 3, a controlled induction motor 4 and a load 5 form a composite controlled object 6; a static neural network 7 and an integrator (s#+[-1]) make a neural network inverse system 8; a neural network inverse system 8 is connected with the front side of a composite controlled object 6 to make a pseudolinear system 9 containing a rotor flux subsystem 91 and a rotary speed subsystem 92; then a closed loop controller 12 is made and comprises a rotor flux controller 121 and a rotary speed controller 122 according to the synthetic method of a linear system; finally, a closed loop controller 12, a neural network inverse system 8 and an extended voltage control inverter 3 are connected in series orderly, and the serial circuit and a flux observer 11 make jointly a neural network reversed control frequency converter 13, thereby realizing the high performance control of an induction motor.
Description
One. technical field
The present invention is a kind of control method of AC induction motor, is applicable to the high performance control of AC induction motor, belongs to the technical field of Electric Drive control appliance.
Two. background technology
At present, the employing frequency converter drives induction alternating current (AC) motor (abbreviation induction machine) thereby constitutes many fields that frequency conversion speed-adjusting system has been widely used in the transmission of former employing DC motor speed-regulating.Frequency converter kind commonly used in the industry has constant voltage and frequency ratio control of conversion device and vector-control frequency converter.The frequency conversion speed-adjusting system that adopts constant voltage and frequency ratio control of conversion device to drive is realized simplyr, but its dynamic and static speed adjusting performance is all very poor.If higher, generally adopt vector-control frequency converter to the dynamic and static performance requirement of governing system.Yet vector control is a kind of stable state decoupling control method, only the decoupling zero of system relation is only establishment when the motor magnetic linkage reaches stable and keeps constant, and because vector control is a kind of based on motor mathematical model and parameter control method, therefore the frequency conversion speed-adjusting system that adopts vector-control frequency converter to drive changed by induction motor parameter and the influence of load disturbance very greatly, the control of governing system is difficult to reach the high-performance of expectation.For overcoming the dependence of existing Control of Induction Motors method to Mathematical Modeling, inductive motor control system changes parameter and the adaptability and the robustness of disturbance from improving in essence, and then every performance index of raising Control of Induction Motors, need to adopt new control technology or new control method, thereby constitute novel frequency converter.
Three. technology contents
1, technical problem
The building method that the purpose of this invention is to provide a kind of Neural network inverse control frequency converter of induction machine.The frequency converter that adopts this method construct to go out does not rely on the Mathematical Modeling and the parameter thereof of induction machine, can realize dynamic decoupling real between induction machine rotating speed and the rotor flux, thereby has a good dynamic and static control performance, anti-parameter of electric machine variation and load disturbance ability are strong, can improve every control performance index of induction machine effectively, as dynamic responding speed, steady-state tracking precision and parameter robustness and antijamming capability etc.
2, technical scheme
The building method of the Neural network inverse control frequency converter of induction machine of the present invention is at first general Voltage-controlled Current Source inverter, contrary Parker (Park) conversion and contrary Clarke (Clark) conversion to be formed jointly the part of the Voltage-controlled Current Source inverter (the voltage-controlled inverter that is called for short expansion) of expansion as the Neural network inverse control frequency converter of constructing thereafter; And the voltage-controlled inverter of controlled induction machine and load and expansion made as a whole composition composite controlled object; And then adopt static neural network to add the nerve network reverse that integrator s-1 constructs composite controlled object, and make nerve network reverse realize the inverse system structure of composite controlled object by the weight coefficient of adjusting neural net; Nerve network reverse is placed before the composite controlled object then, nerve network reverse and composite controlled object are formed pseudo-linear system, pseudo-linear system is equivalent to the second order integral linearity subsystem of two decoupling zeros, be rotor flux subsystem and rotating speed subsystem, realize dynamic decoupling thereby make between the rotor flux of induction machine and the rotating speed; On this basis, adopt a kind of integrated approach of linear system that two decoupling zero second order integration subsystems are designed rotor flux controller and rotational speed governor respectively, and constitute closed loop controller by rotor flux controller and rotational speed governor; The flux observer that adopts electric current rotating speed flux observation model commonly used and Clarke conversion to form obtains the required rotor flux information of magnetic linkage closed-loop control; At last the voltage-controlled inverter of closed loop controller, nerve network reverse and flux observer and expansion being constituted the Neural network inverse control frequency converter jointly comes induction machine is controlled, to obtain the fine control performance index, realize high performance for induction motor control, wherein
1. the building method of nerve network reverse is for adding 4 integrator (s with the static neural network with 6 input nodes, 2 output nodes
-1) constitute nerve network reverses with 2 input nodes, 2 output nodes.First input of static neural network is first input of nerve network reverse, and first input of static neural network is through first integrator (s
-1) be second input of static neural network, described second input is the 3rd input of static neural network again through second integrator; The 4th input of static neural network is second input of nerve network reverse, and the 4th input of static neural network is through the 3rd integrator (s
-1) be the 5th input of static neural network, described the 5th input is again through the 4th integrator (s
-1) be the 6th input of static neural network; The output of static neural network is the output of nerve network reverse.
2. the method for adjustment of the weight coefficient of static neural network is with stator voltage component u
Sm *And u
St *As the input of composite controlled object, to motor speed ω
r, the motor stator current i
aAnd i
bCarry out data sampling, and estimate rotor flux ψ by flux observer
r, with the rotational speed omega that obtains
rAnd rotor flux ψ
rOff-line is asked single order and second dervative, and signal is done standardization processing, the training sample set of composition neural net
ψ
r,
ω
r, u
Sm *, u
St *, be used for static neural network is trained, thus the weight coefficient of definite static neural network.
3. nerve network reverse, closed loop controller, coordinate transform and the flux observer in the Neural network inverse control frequency converter is dsp controller for adopting digital signal processor, realize that by establishment DSP program the Voltage-controlled Current Source inverter is voltage source inverter commonly used.
4. the structure of Neural network inverse control frequency converter is made of jointly the voltage-controlled inverter and the flux observer of closed loop controller, nerve network reverse, expansion, wherein forward path is followed in series to form by the voltage-controlled inverter of closed loop controller, nerve network reverse and expansion, two outputs of closed loop controller are respectively two inputs of nerve network reverse, and two outputs of nerve network reverse are respectively two inputs of the voltage-controlled inverter correspondence of expansion; Feedback path has flux observer, and it is input as stator phase current i
a, i
bAnd rotational speed omega
r, be output as rotor flux ψ
rWith rotor flux angle θ.Rotor flux ψ
rWith rotational speed omega
rFeed back to closed loop controller, form the control of rotor flux closed-loop control and speed closed loop respectively; Rotor flux angle θ is used for the contrary Parker transform operation of coordinate transform.
3, technique effect
Principle of the present invention is contrary by constructing neural network, control will be converted into to the control of this multivariable of induction machine, close coupling, non linear system, correspondingly just closed loop controller can be designed easily the second order integral linearity subsystem of rotor flux and rotating speed.Owing to really realized, thereby can distinguish the effective control that independently realizes induction machine rotating speed and rotor flux, obtained good rotational speed regulation performance to the dynamic decoupling between rotating speed and the rotor flux.And owing to adopted the neural net that does not rely on the controlled device Mathematical Modeling to realize the inverse system function, thereby improved the ability of anti-induction motor parameter variation of Neural network inverse control frequency converter and load disturbance greatly.
The invention has the advantages that:
A. adopt nerve network reverse, the control of this Complex Nonlinear System of induction machine is converted into the control of simple pseudo-linear system, really realized the dynamic decoupling between induction machine rotating speed and the rotor flux.On this basis, by the appropriate design closed loop controller, can obtain good dynamic and static speed adjusting performance.
B. adopt static neural network to add the inverse system that integrator is realized composite controlled object, the constructing neural network inverse frequency transformer is realized the control to induction machine, be completely free of the dependence of traditional Control of Induction Motors method for Mathematical Modeling, reduced induction motor parameter effectively and changed and the influence of load disturbance, improved every performance index of Control of Induction Motors significantly Control of Induction Motors.
The present invention can be used for constructing new type inverter induction machine is carried out high performance control, not only in the drive system that with the AC induction motor is power set, very high using value is arranged, and be in the drive system of power set at the alternating current machine with other type, application prospect also is very wide.
Four. description of drawings
Fig. 1 is made of the structure chart of the voltage-controlled inverter 3 of expansion jointly Voltage-controlled Current Source inverter 1, coordinate transform 2.The coordinate transform 2 that Voltage-controlled Current Source inverter 1 is wherein arranged and form by contrary Parker (Park) conversion and contrary Clarke (Clark) conversion.
Fig. 2 is the structure chart of the composite controlled object 6 that formed by the voltage-controlled inverter 3 of expansion and controlled induction machine 4 and load 5 thereof.Voltage-controlled inverter 3, induction machine 4 and load 5 that expansion is wherein arranged.
Fig. 3 is the complete mathematical model schematic diagram (left figure) and the equivalent mathematical model schematic diagram (right figure) thereof of composite controlled object 6.
Fig. 4 is nerve network reverse 8 and the composite controlled object 6 common schematic diagrames that form pseudo-linear system 9, wherein left side figure is the connection layout of nerve network reverse 8 and composite controlled object 6, right figure comprises rotor flux subsystem 91 and rotating speed subsystem 92 for its isoboles is a pseudo-linear system 9.Nerve network reverse 8 includes 4 integrator (s
-1) and a static neural network 7.
The structure chart of the flux observer 11 that Fig. 5 is made up of electric current rotating speed flux observation model 10 and Clarke (Clark) conversion.Electric current rotating speed flux observation model 10 is wherein arranged, Clarke (Clark) conversion and induction machine 4.
Fig. 6 is added to two component of voltage signals that composite controlled object 6 inputs are used to obtain the neural metwork training data.
The structure principle chart that Fig. 7 is simplified by the closed-loop control system that closed loop controller 12 and pseudo-linear system 9 are formed.Wherein pseudo-linear system 9 comprises rotor flux subsystem 91 and rotating speed subsystem 92; Closed loop controller comprises rotor flux controller 121 and rotational speed governor 122.
Fig. 8 is the structure principle chart with the induction motor speed regulation system of Neural network inverse control frequency converter 13 controls.Wherein Neural network inverse control frequency converter 13 is made up of the voltage-controlled inverter 3 of nerve network reverse 8, closed loop controller 12, flux observer 11 and expansion.
Fig. 9 is to be controller with the digital signal processor DSP, and Intelligent Power Module IPM is the hardware structure diagram of a kind of implementation of the present invention of Voltage-controlled Current Source inverter.Dsp controller 15, photoelectric code disk 14, induction machine 4 and load 5 thereof are wherein arranged.
Figure 10 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 Neural network inverse control frequency converter.Secondly the voltage-controlled inverter that will expand and induction machine and load thereof are as a composite controlled object, and this composite controlled object equivalence is the quadravalence Differential Equation Model under the rotor flux coordinate system, the vectorial relative rank of system be 2,2}.Adopt the static neural network (static neural network is multitiered network MLN) of 6 input nodes, 2 output nodes to add 4 integrator (s
-1) construct the nerve network reverse of composite controlled object.And make nerve network reverse realize the inverse system structure 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 two second order integration subsystem (s again
-2) be the pseudo-linear system that rotor flux subsystem and rotating speed subsystem constitute, thus the control of the nonlinear multivariable systems of a complexity is converted into the control of two simple second-order integration subsystems.Two second order integration subsystems for decoupling zero, adopt a kind of linear system integration method, as proportion integration differentiation PID or POLE PLACEMENT USING etc., make rotor flux controller and rotational speed governor respectively, rotor flux controller and rotational speed governor are formed closed loop controller jointly.Final formation by the voltage-controlled inverter of nerve network reverse, closed loop controller, expansion and flux observer totally 4 Neural network inverse control frequency converters of partly forming comes induction machine is controlled.
Concrete enforcement divides following 9 steps:
1. 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 1) with two stator voltage components.The voltage-controlled inverter of expansion will be as a part of whole Neural network inverse control frequency converter.
2. formation composite controlled object.Composite controlled object is formed in the voltage-controlled inverter of the expansion that will construct in the first step and controlled induction machine and load thereof jointly, and this composite controlled object is with two stator voltage component u
Sm *And u
St *Be its input (as shown in Figure 2).
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 complete Mathematical Modeling of composite controlled object.Then through equivalence shown in Figure 3, the equivalent mathematical model (shown in the right figure of Fig. 3) of composite controlled object be the quadravalence differential equation under the rotor flux coordinate system, its vectorial relative rank be 2,2}.Inverse system through provable this quadravalence Differential Equation Model of deriving exists, and can determine that two inputs of its inverse system are the second dervative of induction electromotor rotor magnetic linkage and the second dervative of rotating speed, and two outputs are respectively two 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.
4. adopt static neural network to add 4 integrator constructing neural networks contrary (shown in the frame of broken lines of the left figure of Fig. 4).Wherein static neural network adopts 3 layers MLN network, and the input layer number is 6, and the hidden layer node number is 9, and output layer node number is 2, 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 4 integrator s with static neural network then with 6 input nodes, 2 output nodes
-1Constitute nerve network reverse (shown in the frame of broken lines of the left figure of Fig. 4), 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.Static neural network is formed nerve network reverse with 4 integrators, and the output of static neural network is exactly the output of nerve network reverse.
5. construct the observer (as shown in Figure 5) of rotor flux.Flux observer is made up of commonly used electric current rotating speed flux observation model and Clarke (Clark) conversion, flux observer be input as stator phase current i
a, i
bAnd rotational speed omega
r, be output as rotor flux angle θ and rotor flux ψ
rWherein rotor flux angle θ is used to realize Parker (Park) conversion and contrary Parker (Park) conversion, rotor flux ψ
rFeedback quantity as the magnetic linkage closed-loop control.
6. adjust the weight coefficient of static neural network.(a) general two component of voltage u as shown in Figure 6
Sm *And u
St *Form with input is added to the input of composite controlled object as shown in Figure 2, gathers the induction machine rotational speed omega with 6 milliseconds sampling periods
rWith i
a, i
b, obtain ψ by flux observer
r, and preserve data { ω
r, ψ
r.(b) rotating speed and rotor flux signal off-line are asked its single order and second dervative respectively, and signal is done standardization processing, the training sample set of composition neural net
ψ
r,
ω
r, u
Sm *, u
St *.(c) adopt the error anti-pass BP algorithm that drives quantifier and learning rate changing that static neural network is trained, through 200 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 rotating speed subsystem and rotor flux subsystem.Constitute nerve network reverse (shown in the frame of broken lines among the left figure of Fig. 4) by the static neural network of having determined each weight coefficient and 4 integrators, nerve network reverse and composite controlled object compose in series pseudo-linear system (shown in the right figure of Fig. 4), this pseudo-linear system is made of jointly a rotating speed subsystem and a rotor flux subsystem, thereby reached the dynamic decoupling between rotating speed and the rotor flux, complicated nonlinear multivariable systems control has been converted into the control of simple two single argument linear systems.
8. make closed loop controller.Rotor flux subsystem and rotating speed subsystem (shown in the right figure of Fig. 4) are made closed loop controller (as shown in Figure 7) respectively.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, rotor flux controller and rotational speed governor have all been selected proportion differential PD controller for use, and its parameter tuning is PD=100+15s.
9. form the Neural network inverse control frequency converter.The voltage-controlled inverter and the flux observer of nerve network reverse, closed loop controller, expansion are formed Neural network inverse control frequency converter (as shown in big frame of broken lines among Fig. 8) jointly.Can require to adopt different hardware or software to realize according to different control.
Fig. 9 has provided the schematic diagram of a kind of specific embodiment of the present invention, and wherein nerve network reverse, closed-loop control, coordinate transform and flux observer are that dsp controller is realized by software by digital signal processor, and the system program block diagram as shown in figure 10; The Voltage-controlled Current Source inverter adopts Intelligent Power Module IPM to constitute; Controlled induction machine model is Y90S-4, and the parameter of electric machine is P
e=1.1kW; U
e=220/380V; I
e=2.7A; f
e=50Hz; n
p=2; n
e=1400rpm;
According to the above, just can realize the present invention.
Claims (5)
1, a kind of building method of Neural network inverse control frequency converter of induction machine is characterized in that the voltage-controlled inverter (3) that this method is promptly expanded Voltage-controlled Current Source inverter (1), the common Voltage-controlled Current Source inverter of forming expansion of coordinate transform (2); Constitute composite controlled object (6) jointly by the voltage-controlled inverter (3) of expansion and the load (5) of induction machine (4) and induction machine (4); Use static neural network (7) and integrator (s again
-1) constitute the nerve network reverse (8) of composite controlled object (6), and make the inverse system structure of nerve network reverse (8) realization composite controlled object (6) by the weight coefficient of adjusting static neural network (7); Then nerve network reverse (8) is connected on composite controlled object (6) before, the equivalence of nerve network reverse (8) and composite controlled object (6) becomes by two pseudo-linear systems (9) of forming of second order integral form rotor flux subsystem (91) and second order integral form rotating speed subsystem (92) independently, again according to the integrated approach of linear system, as proportional plus integral plus derivative controller PID, a kind of in the methods such as POLE PLACEMENT USING or linear quadratic type optimal design, rotor flux subsystem (91) in this pseudo-linear system (9) and rotating speed subsystem (92) are designed rotor flux controller (121) and rotational speed governor (122) respectively, and rotor flux controller (121) and rotational speed governor (122) are formed closed loop controller (12) jointly; The required rotor flux of closed-loop control is provided by flux observer (11), this flux observer is made up of electric current rotating speed flux observation model (10) and Clarke conversion commonly used, by detecting the stator current and the rotating speed of motor of motor, directly calculate rotor flux according to the relation of current of electric, speed and magnetic linkage; Constitute Neural network inverse control frequency converter (13) jointly with voltage-controlled inverter (3) the order serial connection of closed loop controller (12), nerve network reverse (8), expansion and with flux observer (11) at last, come induction machine is carried out high performance of control.
2, the building method of the Neural network inverse control frequency converter of induction machine according to claim 1, the building method that it is characterized in that nerve network reverse (8) is for adding 4 integrator (s with the static neural network (7) with 6 input nodes, 2 output nodes
-1) constitute nerve network reverses (8) with 2 input nodes, 2 output nodes, wherein: first input of static neural network (7) is first input of nerve network reverse (8), and first input of static neural network (7) is through first integrator (s
-1) be second input of static neural network (7), described second input is the 3rd input of static neural network (7) again through second integrator; The 4th input of static neural network (7) is second input of nerve network reverse (8), and the 4th input of static neural network (7) is through the 3rd integrator (s
-1) be the 5th input of static neural network (7), described the 5th input is again through the 4th integrator (s
-1) be the 6th input of static neural network (7); The output of static neural network (7) is the output of nerve network reverse (8).
3. the building method of the Neural network inverse control frequency converter of induction machine according to claim 1, the method for adjustment of weight coefficient that it is characterized in that static neural network (7) is for stator voltage component u
Sm *And u
St *As the input of composite controlled object (6), to motor speed ω
r, the motor stator current i
aAnd i
bCarry out data sampling, and estimate rotor flux ψ by flux observer (11)
r, with the rotational speed omega that obtains
rAnd rotor flux ψ
rOff-line is asked single order and second dervative, and signal is done standardization processing, forms the training sample set of neural net
Be used for static neural network (7) is trained, thus the weight coefficient of definite static neural network (7).
4. the building method of the Neural network inverse control frequency converter of induction machine according to claim 1, it is characterized in that nerve network reverse (8), closed loop controller (12), coordinate transform (2) and flux observer (11) in the Neural network inverse control frequency converter (13) are dsp controller (15) for adopting digital signal processor, realize that by establishment DSP program Voltage-controlled Current Source inverter (1) is voltage source inverter commonly used.
5. the building method of the Neural network inverse control frequency converter of induction machine according to claim 1, the structure that it is characterized in that Neural network inverse control frequency converter (13) is by closed loop controller (12), nerve network reverse (8), the voltage-controlled inverter (3) of expansion is formed with flux observer (11) is common, wherein forward path is by closed loop controller (12), the voltage-controlled inverter (3) of nerve network reverse (8) and expansion is followed in series to form, two outputs of closed loop controller (12) are respectively two inputs of nerve network reverse (8), and two outputs of nerve network reverse (8) are respectively two corresponding inputs of voltage-controlled inverter (3) of expansion; Feedback path has flux observer (11), and it is input as stator phase current i
a, i
bAnd rotational speed omega
r, be output as rotor flux ψ
rWith rotor flux angle θ.Rotor flux ψ
rWith rotational speed omega
rFeed back to closed loop controller (12), form the control of rotor flux closed-loop control and speed closed loop respectively; Rotor flux angle θ is used for the contrary Parker transform operation of coordinate transform (2).
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| 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 |
| CN100433537C (en) * | 2006-03-08 | 2008-11-12 | 江苏大学 | Method for controlling bearing-less AC asynchronous motor neural network inverse decoupling controller |
| DE102007040725A1 (en) * | 2007-08-29 | 2009-03-05 | Robert Bosch Gmbh | Electric machine |
| CN101917150B (en) * | 2010-06-24 | 2012-06-20 | 江苏大学 | Robust controller of permanent magnet synchronous motor based on fuzzy-neural network generalized inverse and construction method thereof |
| CN102055390B (en) * | 2011-01-10 | 2012-11-07 | 江苏大学 | Construction method for neural network Alpha-order inverse controller of bearing-free brushless DC motor |
| 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 |
| CN102437816B (en) * | 2011-10-25 | 2014-05-07 | 武汉鑫通科创科技发展有限公司 | Adaptive motor motion control apparatus based on neural network |
| CN102647130A (en) * | 2012-04-20 | 2012-08-22 | 上海电机学院 | Permanent magnet synchronous linear motor control method |
| 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 |
| CN103414432B (en) * | 2013-07-12 | 2015-11-18 | 江苏大学 | A kind of building method of hybrid electric vehicle belt driven starter generator controller |
| CN104765929B (en) * | 2015-04-17 | 2017-08-11 | 浙江工业大学 | A kind of nonlinear state decoupling observation procedure based on Multiple Time Scales recurrent neural network |
| JP6106226B2 (en) | 2015-07-31 | 2017-03-29 | ファナック株式会社 | Machine learning device for learning gain optimization, motor control device including machine learning device, and machine learning method |
| JP6140225B2 (en) | 2015-07-31 | 2017-05-31 | ファナック株式会社 | Motor control device having magnetic flux controller, machine learning device and method thereof |
| CN110985541B (en) * | 2019-11-21 | 2021-06-18 | 东南大学溧阳研究院 | Controller for controlling active magnetic suspension bearing system and control method thereof |
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