CN111897212A - Multi-model combined modeling method of magnetic control shape memory alloy actuator - Google Patents
Multi-model combined modeling method of magnetic control shape memory alloy actuator Download PDFInfo
- Publication number
- CN111897212A CN111897212A CN202010516434.2A CN202010516434A CN111897212A CN 111897212 A CN111897212 A CN 111897212A CN 202010516434 A CN202010516434 A CN 202010516434A CN 111897212 A CN111897212 A CN 111897212A
- Authority
- CN
- China
- Prior art keywords
- model
- narmax
- shape memory
- memory alloy
- alloy actuator
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 229910001285 shape-memory alloy Inorganic materials 0.000 title claims abstract description 32
- 238000000034 method Methods 0.000 title claims abstract description 19
- 238000013528 artificial neural network Methods 0.000 claims abstract description 31
- 230000006870 function Effects 0.000 claims abstract description 26
- 238000013507 mapping Methods 0.000 claims abstract description 7
- 210000002569 neuron Anatomy 0.000 claims description 28
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000011478 gradient descent method Methods 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 239000000463 material Substances 0.000 abstract description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 2
- 230000008859 change Effects 0.000 description 3
- 230000007547 defect Effects 0.000 description 3
- 238000006073 displacement reaction Methods 0.000 description 3
- 239000000956 alloy Substances 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 239000000919 ceramic Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003446 memory effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
A multi-model combined modeling method of a magnetic control shape memory alloy actuator belongs to the technical field of control. The invention aims to construct an NARMAX structure model, which can improve the capability of describing multi-value mapping hysteresis by the NARMAX model and simultaneously enable a Bouc-wen model to describe highly asymmetric hysteresis to become a possible multi-model combined modeling method for the magnetic control shape memory alloy actuator. The method comprises the following steps: establishing an NARMAX structural model capable of describing the multi-value mapping hysteresis of the magnetic control shape memory alloy actuator; an unknown nonlinear function of the NARMAX structure model is built by utilizing a wavelet neural network, and the NARMAX structure model which can update model parameters on line and adapt to the complex dynamic hysteresis characteristics of the magnetic control shape memory alloy actuator is built. The invention effectively promotes the application of the intelligent material actuating mechanism in the high-precision manufacturing industry, and can adjust the model parameters on line to adapt to the complex dynamic hysteresis characteristic of the magnetic control shape memory alloy actuator.
Description
Technical Field
The invention belongs to the technical field of control.
Background
Because of the advantages of generating huge macroscopic strain and shape memory effect and the like under the action of a magnetic field, the actuator taking the magnetic control shape memory alloy material as a core device has wide application value and prospect in the field of micro-nano technology. However, due to the inherent complex hysteresis nonlinearity of the magnetic shape memory alloy material, the further application of the magnetic shape memory alloy actuator in the field of high-precision positioning is seriously influenced. Compared with the traditional intelligent material actuator, such as a piezoelectric ceramic actuator and a giant magnetostrictive actuator, the hysteresis loop of the magnetic control shape memory alloy actuator has the characteristics of high saturation and strong asymmetry, and the shape of the hysteresis loop can change along with the change of the amplitude, the frequency and the waveform of an input signal of the actuator. In addition, temperature is also an important factor affecting the shape of the hysteresis ring. Therefore, the hysteresis nonlinear modeling of the magnetron shape memory alloy actuator is a very challenging task and is receiving more and more attention.
At present, models for describing the hysteresis nonlinearity of the magnetron shape memory alloy actuator are mainly classified into phenomenological models such as KP model, PI model, Bouc-wen model and phenomenological models such as Jiles-Atherton model. The model can effectively describe the hysteresis nonlinearity of the magnetic control shape memory alloy actuator, but has the defects of complex calculation process, low modeling precision, incapability of adapting to the dynamic change of a system and the like. The NARMAX model is a nonlinear black box model and has good description capability on complex nonlinear systems. In order to enable the NARMAX model to better describe the hysteresis nonlinearity of the magnetic control shape memory alloy actuator with multi-value mapping, introducing an exogenous variable function is an effective method. The Bocu-Wen model is a commonly used hysteresis model due to its simple structure, and by selecting different model parameters, the Bocu-Wen model can describe different hysteresis characteristics. However, the conventional Bouc-Wen model cannot describe highly asymmetric hysteresis, and therefore is difficult to use to describe the hysteresis exhibited by a magnetically controlled shape memory alloy actuator.
Disclosure of Invention
The invention aims to introduce the entire Bouc-wen model as an exogenous variable function into an NARMAX model to construct an NARMAX structure model, so that the capability of the NARMAX model in describing multi-value mapping hysteresis can be improved, and the Bouc-wen model can be a multi-model combined modeling method of a magnetic control shape memory alloy actuator capable of describing highly asymmetric hysteresis.
The method comprises the following steps:
step 1: a multi-model combined modeling method for introducing a Bouc-wen model into an NARMAX model as an exogenous variable is provided, and an NARMAX structure model capable of describing the multi-value mapping hysteresis of the magnetic control shape memory alloy actuator is established;
the NARMAX structural model expression with exogenous variables is as follows:
where k is the discrete time of the system, N is an unknown nonlinear function, y*(k) And h (k-1) represents the output value and the exogenous variable of the NARMAX structure model, respectively, v (k-1) and y (k-1) represent the input and output values of the magnetically controlled shape memory alloy actuator, respectively,nv、nhand nyDenotes the memory delay of v, h and y, respectively, p ═ nv+nh+nyFor the total delay of the system, an exogenous variable h is represented by a Bouc-wen model, and a function expression of the Bouc-wen model is as follows:
wherein,representing the derivative with time, alpha, beta, gamma and d are parameters of the Bouc-wen model;
for convenience, the NARMAX structural model is adapted to polynomial form, the expression being as follows:
wherein, theta is a polynomial coefficient,is the i-th of the model input xpThe value of the input to the term,a matrix term formed by coefficients, q is a model order, and n is (p + q)! P! q! -1 is the total number of terms,is an autoregressive moving average term
Step 2: an unknown nonlinear function of the NARMAX structure model is built by utilizing a wavelet neural network, and the NARMAX structure model which can update model parameters on line and adapt to the complex dynamic hysteresis characteristics of the magnetic control shape memory alloy actuator is built;
the expression of the wavelet neural network is as follows:
wherein O (k) is the output value of the wavelet neural network, omegaijThe weight value of the connection between the ith neuron of the input layer and the jth neuron of the hidden layer, omegajThe weight value, eta, of the connection between the jth neuron of the hidden layer and the neuron of the output layeriThe input quantity of the ith neuron of the input layer, m and n are the numbers of the neurons of the input layer and the hidden layer respectively, ajAnd bjRespectively representing the scaling and translation parameters of the wavelet function of the jth neuron of the hidden layer,for the Morlet wavelet function, the expression is as follows:
the method comprises the steps that I is an input value of a wavelet function, e is a constant, when a wavelet neural network is adopted to construct a nonlinear function of an NARMAX structure model, the input quantity of the neural network is an autoregressive moving average term of the NARMAX structure model, the calculation complexity and the modeling precision are comprehensively considered, the number of neurons in a hidden layer is selected to be 7, the number of neurons in an output layer is 1, the initial weight of the neural network is set to be a random value from 0 to 1, and a gradient descent method is adopted in an optimization algorithm.
The hysteresis model of the magnetic control shape memory alloy actuator, which is established by a multi-model combined modeling method, overcomes the defects of the traditional single model in describing the hysteresis nonlinearity of the magnetic control shape memory alloy actuator, provides a new idea for the modeling of a complex nonlinear system, and can effectively promote the application of an intelligent material actuator in the high-precision manufacturing industry. Aiming at the defects of the traditional single model in describing the hysteresis nonlinearity of the magnetic control shape memory alloy actuator, the invention provides a combined modeling method for establishing an NARMAX structural model by combining an NARMAX model and a Bouc-wen model, and a wavelet neural network is adopted to construct a nonlinear function of the NARMAX structural model. The established NARMAX structure model combines the advantages of the NARMAX model and the Bouc-wen model, and the model parameters can be adjusted on line to adapt to the complex dynamic hysteresis characteristic of the magnetic control shape memory alloy actuator.
Drawings
FIG. 1 is a schematic block diagram of a NARMAX structure model based on a wavelet neural network;
FIG. 2 is a schematic illustration of an experimental platform;
FIG. 3 is a graph comparing the output of the model at an input signal frequency of 0.3Hz (24 deg.C);
FIG. 4 is a graph comparing the output of the actuator at an input signal frequency of 0.3Hz (24 deg.C);
FIG. 5 is a graph comparing the output of the model at an input signal frequency of 1Hz (24 deg.C);
FIG. 6 is a graph comparing the output results of the actuator at an input signal frequency of 1Hz (24 deg.C);
FIG. 7 is a graph comparing the output of the model at an input signal frequency of 3Hz (24 deg.C);
FIG. 8 is a graph comparing the output of the actuator at an input signal frequency of 3Hz (24 deg.C);
FIG. 9 is a graph comparing the output of the model at 10 ℃ with the same frequency input;
FIG. 10 is a graph comparing actuator output at 10 ℃ with the same frequency input;
FIG. 11 is a graph comparing the output of the model at 30 ℃ with the same frequency input;
FIG. 12 is a graph comparing actuator output at 30 ℃ with the same frequency input.
Detailed Description
The invention comprises the following steps:
step 1: a multi-model combined modeling method for introducing a Bouc-wen model into an NARMAX model as an exogenous variable is provided, and the NARMAX structure model capable of describing the multi-value mapping hysteresis of the magnetic control shape memory alloy actuator is established.
The NARMAX structural model expression with exogenous variables is as follows:
where k is the discrete time of the system, N is an unknown nonlinear function, y*(k) And h (k-1) represents the output value and the exogenous variable of the NARMAX structure model, respectively, v (k-1) and y (k-1) represent the input and output values of the magnetically controlled shape memory alloy actuator, respectively,nv、nhand nyDenotes the memory delay of v, h and y, respectively, p ═ nv+nh+nyIs the total delay of the system.
The exogenous variable h is represented by a Bouc-wen model, and a Bouc-wen model function is expressed as follows:
For convenience, the NARMAX structural model is adapted to polynomial form, the expression being as follows:
wherein, theta is a polynomial coefficient,is the i-th of the model input xpThe value of the input to the term,a matrix term formed by coefficients, q is a model order, and n is (p + q)! P! q! -1 is the total number of terms,is an autoregressive moving average term.
Step 2: an unknown nonlinear function of the NARMAX structure model is built by utilizing a wavelet neural network, and the NARMAX structure model which can update model parameters on line and adapt to the complex dynamic hysteresis characteristics of the magnetic control shape memory alloy actuator is built.
The expression of the wavelet neural network is as follows:
wherein O (k) is the output value of the wavelet neural network, omegaijThe weight value of the connection between the ith neuron of the input layer and the jth neuron of the hidden layer, omegajThe weight value, eta, of the connection between the jth neuron of the hidden layer and the neuron of the output layeriThe input quantity of the ith neuron of the input layer is m and n are the numbers of the neurons of the input layer and the hidden layer respectively. a isjAnd bjRespectively representing the scale and translation parameters of the wavelet function of the jth neuron of the hidden layer.
wherein, I is the input value of the wavelet function, and e is a constant. When a wavelet neural network is adopted to construct a nonlinear function of the NARMAX structure model, the input of the neural network is an autoregressive moving average term of the NARMAX structure model, a structural block diagram is shown in FIG. 1, the calculation complexity and the modeling precision are comprehensively considered, the number of neurons in a hidden layer is selected to be 7, and the number of neurons in an output layer is 1.
The initial weight of the neural network is set to be a random value from 0 to 1, the optimization algorithm adopts a gradient descent method, and the updating rule formula for obtaining the neural network parameters according to the gradient descent method is as follows:
wij(k)=wij(k-1)-ηd_wij+μ[wij(k-1)-wij(k-2)](6)
wj(k)=wj(k-1)-ηd_wj+μ[wj(k-1)-wj(k-2)](7)
aj(k)=aj(k-1)-ηd_aj+μ[aj(k-1)-aj(k-2)](10)
bj(k)=bj(k-1)-ηd_bj+μ[bj(k-1)-bj(k-2)](11)
wherein d _ wij、d_wj、d_ajAnd d _ bjAre respectively a parameter wij、wj、ajAnd bjE (k) is an error function, e*(k) For modeling errors, Ij(k) Is the input value of the jth neuron of the hidden layer.
The modeling method of the present invention was then experimentally verified.
The description capability of the proposed model on the hysteresis nonlinearity of the magnetic control shape memory alloy actuator is tested under different frequency input signals and temperatures. The experimental platform is shown in fig. 2, and the computer controls the output displacement of the actuator by controlling the output signal of the programmable direct-current power supply. The high-precision micrometer is used for measuring the output displacement value of the actuator and transmitting the measured displacement signal back to the computer through the data acquisition card. Comparing the hysteresis modeling result of the NARMAX structure model based on the RBF neural network, the NARMAX structure model based on the wavelet neural network can well describe the dynamic characteristics of the actuator. In addition, under different temperature conditions, the NARMAX structure model based on the wavelet neural network has higher modeling precision. 3-5 are graphs comparing modeling results and errors of an actuator based on NARMAX structural models of a wavelet neural network and an RBF neural network under the condition of different frequency input signals at 24 ℃. 6-7 are graphs comparing the results and errors of actuator modeling based on NARMAX structure models of wavelet neural network and RBF neural network under different temperature conditions when signals with the same frequency are input.
Table 1 shows the comparison of the root mean square error and the maximum modeling error for the above cases.
Table 1 shows the performance index comparison between the NARMAX structure model based on the RBF neural network and the NARMAX structure model based on the wavelet neural network under different frequency input signals and temperatures.
Claims (1)
1. A multi-model combined modeling method of a magnetic control shape memory alloy actuator is characterized in that: the method comprises the following steps:
step 1: a multi-model combined modeling method for introducing a Bouc-wen model into an NARMAX model as an exogenous variable is provided, and an NARMAX structure model capable of describing the multi-value mapping hysteresis of the magnetic control shape memory alloy actuator is established;
the NARMAX structural model expression with exogenous variables is as follows:
where k is the discrete time of the system, N is an unknown nonlinear function, y*(k) And h (k-1) represents the output value and the exogenous variable of the NARMAX structure model, respectively, v (k-1) and y (k-1) represent the input and output values of the magnetically controlled shape memory alloy actuator, respectively,nv、nhand nyDenotes the memory delay of v, h and y, respectively, p ═ nv+nh+nyFor the total delay of the system, an exogenous variable h is represented by a Bouc-wen model, and a function expression of the Bouc-wen model is as follows:
wherein,representing the derivative with time, alpha, beta, gamma and d are parameters of the Bouc-wen model;
for convenience, the NARMAX structural model is adapted to polynomial form, the expression being as follows:
wherein, theta is a polynomial coefficient,is the i-th of the model input xpThe value of the input to the term,a matrix term formed by coefficients, q is a model order, and n is (p + q)! P! q! -1 is the total number of terms,is an autoregressive moving average term
Step 2: an unknown nonlinear function of the NARMAX structure model is built by utilizing a wavelet neural network, and the NARMAX structure model which can update model parameters on line and adapt to the complex dynamic hysteresis characteristics of the magnetic control shape memory alloy actuator is built;
the expression of the wavelet neural network is as follows:
wherein O (k) is the output value of the wavelet neural network, omegaijThe weight value of the connection between the ith neuron of the input layer and the jth neuron of the hidden layer, omegajThe weight value, eta, of the connection between the jth neuron of the hidden layer and the neuron of the output layeriThe input quantity of the ith neuron of the input layer, m and n are the numbers of the neurons of the input layer and the hidden layer respectively, ajAnd bjRespectively representing the scaling and translation parameters of the wavelet function of the jth neuron of the hidden layer,for the Morlet wavelet function, the expression is as follows:
the method comprises the steps that I is an input value of a wavelet function, e is a constant, when a wavelet neural network is adopted to construct a nonlinear function of an NARMAX structure model, the input quantity of the neural network is an autoregressive moving average term of the NARMAX structure model, the calculation complexity and the modeling precision are comprehensively considered, the number of neurons in a hidden layer is selected to be 7, the number of neurons in an output layer is 1, the initial weight of the neural network is set to be a random value from 0 to 1, and a gradient descent method is adopted in an optimization algorithm.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010516434.2A CN111897212B (en) | 2020-06-09 | 2020-06-09 | Multi-model combined modeling method of magnetic control shape memory alloy actuator |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010516434.2A CN111897212B (en) | 2020-06-09 | 2020-06-09 | Multi-model combined modeling method of magnetic control shape memory alloy actuator |
Publications (2)
Publication Number | Publication Date |
---|---|
CN111897212A true CN111897212A (en) | 2020-11-06 |
CN111897212B CN111897212B (en) | 2022-05-31 |
Family
ID=73206658
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010516434.2A Active CN111897212B (en) | 2020-06-09 | 2020-06-09 | Multi-model combined modeling method of magnetic control shape memory alloy actuator |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN111897212B (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117649902A (en) * | 2023-12-13 | 2024-03-05 | 西南科技大学 | Neural network modeling and hysteresis characteristic prediction method for intelligent material device |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5794171A (en) * | 1996-02-29 | 1998-08-11 | Ford Global Technologies, Inc. | Process for deriving predictive model of crankshaft rotation of a combustion engine |
CN105353610A (en) * | 2015-10-10 | 2016-02-24 | 吉林大学 | Magnetic-control shape memory alloy actuator modeling method based on KP model |
CN110471284A (en) * | 2019-08-19 | 2019-11-19 | 北京航空航天大学 | Artificial neural's muscle electric drive control method and system based on NARMAX Model Distinguish |
-
2020
- 2020-06-09 CN CN202010516434.2A patent/CN111897212B/en active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5794171A (en) * | 1996-02-29 | 1998-08-11 | Ford Global Technologies, Inc. | Process for deriving predictive model of crankshaft rotation of a combustion engine |
CN105353610A (en) * | 2015-10-10 | 2016-02-24 | 吉林大学 | Magnetic-control shape memory alloy actuator modeling method based on KP model |
CN110471284A (en) * | 2019-08-19 | 2019-11-19 | 北京航空航天大学 | Artificial neural's muscle electric drive control method and system based on NARMAX Model Distinguish |
Non-Patent Citations (2)
Title |
---|
YEWEI YU等: "NARMAX Modeling for Hysteresis of Magnetical Shape Memory Alloy Actuator", 《2019 IEEE 14TH INTERNATIONAL CONFERENCE ON NANO/MICRO ENGINEERED AND MOLECULAR SYSTEMS》 * |
周淼磊等: "磁控形状记忆合金执行器迟滞非线性模型", 《吉林大学学报(工学版)》 * |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117649902A (en) * | 2023-12-13 | 2024-03-05 | 西南科技大学 | Neural network modeling and hysteresis characteristic prediction method for intelligent material device |
Also Published As
Publication number | Publication date |
---|---|
CN111897212B (en) | 2022-05-31 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN110245430B (en) | Improved Bouc-Wen model lag modeling method | |
CN111523236A (en) | Piezoelectric ceramic hysteresis model linearization identification method based on Koopman operator | |
CN104991997B (en) | The broad sense rate correlation P-I hysteresis modeling methods of adaptive differential evolution algorithm optimization | |
CN104796111A (en) | Non-linear self-adaptive filter for dynamic hysteretic system modeling and compensation | |
CN110956312B (en) | Photovoltaic power distribution network voltage prediction method based on EMD-CNN deep neural network | |
CN110161841A (en) | A kind of feedforward-fuzzy PID control method suitable for temporarily rushing formula transonic wind tunnel | |
CN113988449B (en) | Wind power prediction method based on transducer model | |
CN109709792A (en) | Aero-engine stable state circuit pi controller and its design method and device | |
CN111042928A (en) | Variable cycle engine intelligent control method based on dynamic neural network | |
CN111897212B (en) | Multi-model combined modeling method of magnetic control shape memory alloy actuator | |
CN111506868B (en) | Ultra-short-term wind speed prediction method based on HHT weight optimization | |
CN112669168A (en) | Short-term wind power prediction method | |
CN115964923A (en) | Modeling method for forecasting 80-100km atmospheric wind speed in adjacent space based on VMD-PSO-LSTM | |
CN111077771A (en) | Self-tuning fuzzy PID control method | |
Zhou et al. | Iterative learning and fractional order PID hybrid control for a piezoelectric micro-positioning platform | |
Zhou et al. | Multihorizons transfer strategy for continuous online prediction of time‐series data in complex systems | |
CN110985216B (en) | Intelligent multivariable control method for aero-engine with online correction | |
CN114237043B (en) | Gas turbine equipment transfer function closed-loop identification method based on deep learning | |
CN110276478B (en) | Short-term wind power prediction method based on segmented ant colony algorithm optimization SVM | |
CN116627194A (en) | WOA-LSTM temperature control method based on OLED thermal experiment | |
CN114492187B (en) | Supersonic combustor pulse injection control method and system based on humanoid active disturbance rejection | |
Xu et al. | Research on load forecasting method of large Power Grid based on Deep confidence Network | |
CN114139442A (en) | Method for optimizing steam turbine valve flow characteristic function based on K-means-LSTM | |
CN113900379A (en) | Neural network-based predictive control algorithm | |
CN111522226A (en) | Multi-objective optimization high-type PID optimal controller design method for servo turntable |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |