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Research on Model Predictive Control of IPMSM Based on Adaline Neural Network Parameter Identification
2019
Energies
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In order to solve the problem that the MPC algorithm has a large dependence on system parameters, a method which integrates MPC control method and parameter identification for IPMSM is proposed. ...
In this method, the d-q axis inductances and rotor permanent magnet flux of IPMSM motor are identified by the Adaline neural network algorithm, and then, the identification results are applied to the predictive ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/en12244803
fatcat:kp3ttlw2ufdaflmwg66skrosxe
Fault Identification for a Closed-Loop Control System Based on an Improved Deep Neural Network
2019
Sensors
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Fault identification for closed-loop control systems is a future trend in the field of fault diagnosis. ...
In this work, a new fault identification method is proposed, which is based on the deep neural network for closed-loop control systems. ...
The difficulty of fault identification for closed-loop control systems was revealed. The deep neural network method has been proposed. ...
doi:10.3390/s19092131
fatcat:bcxf2oy4afajxm7tqs3jr5qqgm
Dynamic Neural Network Identification and Decoupling Control Approach for MIMO Time-Varying Nonlinear Systems
2014
Abstract and Applied Analysis
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Based on the neural network identifier, the adaptive controller of complex system is designed in the latter. ...
In this paper, the main methodology, on which the method is based, is dynamic neural networks (DNN) and adaptive control with the Lyapunov methodology for the time-varying, coupling, uncertain, and nonlinear ...
Motivated by the seminal paper [18] , there is a continuously increasing interest in applying neural network to identification and control of nonlinear system. ...
doi:10.1155/2014/316206
fatcat:6dvsrmbwrrbover3yyecv42zt4
Identification of an Open-loop Plasma Vertical Position Using Fractional Order Dynamic Neural Network
[article]
2017
arXiv
pre-print
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The aim of this paper is to propose a fractional order nonlinear model to predict the vertical position of a plasma column system in a Tokamak by using real data from Damavand Tokamak. ...
The system is identified based on a newly introduced fractional order dynamic neural network. ...
In this method, the process is controlled in a closed loop structure while the input and output data of the process are used for identification of the system. ...
arXiv:1706.05892v1
fatcat:dstyxwdo5jhr5bwdko2uajyhae
An Artificial Neural Network Based Preestimation Fitler for Bad Data Defection, Identification and Elimination in State Estimation
1996
Mathematical and Computational Applications
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State estimators are vitally important in energy control centers. The measurements that come from control system are generally analysed by a state estimator. ...
In this paper, by using an artificial neural network (ANN), a bad data detection, identification and then elimination preestimation filter is outlined. ...
State Estimation Detection of bad data In our study, as stated before, a back propagation ANN based bad data detection, identification and elimination method is applied on an example system as shown in ...
doi:10.3390/mca1010159
fatcat:rkuynfsxffdvfmjeqgoym7iywm
Exploration and Mining Learning Robot of Autonomous Marine Resources Based on Adaptive Neural Network Controller
2018
Polish Maritime Research
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In summary, the identification method of underwater robot system based on neural network is effective. ...
To study the autonomous learning model of the learning robot for marine resource exploration, an adaptive neural network controller was applied. ...
A series-parallel identification structure is adopted between the controlled system and the identification network. The identification network uses a modified Elman network. ...
doi:10.2478/pomr-2018-0115
fatcat:todswaykxndf7fdjsbn3hv6fre
A Survey on Method of System Identification
2017
DEStech Transactions on Engineering and Technology Research
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After all, one system identification method based on adaptive filter ,the method has been usually used in active vibration control. Neural Network System Identification Algorithm[8]. ...
Its basic thought is to put the network weights as a state of the corresponding dynamic system, using augmented kalman filter estimation to get good results. ...
Introduction In the Vibration control, identification, state estimation and control theory are three mutual penetrable domain, along with the increasingly complex control process, the improvement of the ...
doi:10.12783/dtetr/mdm2016/4997
fatcat:gst6dioqybaijozyywm7bdfko4
A SURVEY OF ARTIFICIAL INTELLIGENCE METHODS IN INTRUSION DETECTION TASKS
2021
InterConf
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One of the ways to eliminate this problem is to use neural networks as a mechanism to detect network attacks. ...
There are many methods for detecting network attacks, but since attacks are constantly changing, special databases of rules or signatures to detect attacks require continuous administration, it becomes ...
3 3 Identification of misuse by method "supervised learning" Identification methods Systems
Method characteristics
State modeling
USTAT, IDIOT
A set of defined states is used to
determine an intrusion ...
doi:10.51582/interconf.21-22.04.2021.051
fatcat:kuupe54nwbf5tjpyf5sircofxe
To Identify a Torque Controller System Approximating a Neural Network Based on Model Reference Technique
2016
International Journal Of Engineering And Computer Science
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The 'process of learning' uses a method of training (trainlm) to train the network. Then the outputs of the proposed system and this PINNC are compared. ...
The NNMRC is configured by learning the behavior of a reference model system, provided to it. The 'process of learning' uses a method of training (trainbfgc) to train this network. ...
DIFFERENT TYPES OF NEURAL NETWORK CONTROLLERS Neural Network Toolbox in MATLAB 7.2 offers three methods for identification of different linear and nonlinear system. ...
doi:10.18535/ijecs/v5i9.06
fatcat:kjaz37qz6vhkzdyle27ykl4csy
Application of functional link neural network to HVAC thermal dynamic system identification
1998
IEEE transactions on industrial electronics (1982. Print)
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This paper describes a functional link neural network approach to performing the HVAC thermal dynamic system identification. ...
Index Terms-Functional link, HVAC, intelligent control, neural network, system identification. ...
The use of neural networks for identification and control provides a means of adapting a controller on line in an effort to minimize a given cost index. ...
doi:10.1109/41.661318
fatcat:hhsahet3jbgjtahihwcmb7vsk4
Modeling and Identification of Permanent Magnet Synchronous Motor via Deterministic Learning
2020
IEEE Access
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A state estimator is built using the RBF neural network with the system state of the motor as input. The weights of the RBF neural network are updated using Lyapunov-based law. ...
adaptive (MRAS) method, state observer method, intelligent identification method and so on. ...
doi:10.1109/access.2020.3020848
fatcat:m2icbrjenzh7zgoz4relcxobpi
Study of the Angular Positioning of a Rotating Object with Neural Model Reference Control
2021
WSEAS Transactions on Computers
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The purpose of the paper is to make a comparative analysis of the neural predictive control technic with the linear control for angular positioning of mechanical parts. ...
The structure of the neural predictive control system and its design are presented. Transient characteristics obtained are compared from the point of view of their control efficiency criteria. ...
Analyzing the obtained characteristics it can be said that with the help of a neural modfel reference control system a behavior similar to the linear state control system can be obtained: a zero error ...
doi:10.37394/23205.2021.20.25
fatcat:s5uhty3pvfdjhkhszce7tnju2q
Local time-varying topology identification of network with unknown parameters based on adaptive synchronization
2018
Automatika
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The network topology identification using the observing output data of the network is a crucial step for complex network analysis, which is of great importance to understand the networks' properties and ...
The network with some or all of characteristics including self-organization, self-similarity, attractor, small world and scale-free is referred to as a complex network. ...
Funding This work was supported by Key program of Natural Science Foundation of Shaanxi Province [grant number 2016 ZDJC-01]. ...
doi:10.1080/00051144.2018.1552473
fatcat:iwdij3eugvba3ayv2mhlzdzzzq
Rotor Resistance Online Identification of Vector Controlled Induction Motor Based on Neural Network
2014
Mathematical Problems in Engineering
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This paper proposes a novel model for rotor resistance parameters identification based on Elman neural networks. ...
This identification method is able to enhance the performance of induction motor's variable-frequency speed regulation. ...
Its ability is not enough to adapt to the requirement of high performance of the control system. However, Elman neural network online identification method is a very good solution. ...
doi:10.1155/2014/831839
fatcat:tdxcmalvqjdidct35iidknspqm
Intelligent Modeling and Design of a Novel Temperature Control System for a Cantilever-Based Gas-Sensitive Material Analyzer
2021
IEEE Access
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The LSTM network identification is obviously better than that of previous Peltier system identification methods, and the NHCOPSO algorithm is found to be superior to other improved PSO and evolutionary ...
A proportional-integral-derivative (PID) algorithm is used to achieve accurate and fast temperature control, with a long short-term memory (LSTM) network trained to identify the nonlinear dynamics of the ...
SYSTEM IDENTIFICATION BASED ON AN LSTM NETWORK This paper proposes a method of model identification for the Peltier temperature control system based on an LSTM network. ...
doi:10.1109/access.2021.3051339
fatcat:mitrjdvovbf4foohl2p6og64hu
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