Neural network based dynamic simulation of induction motor drive

Menghal, P. M.; Laxmi, A. Jaya

doi:10.1109/icpec.2013.6527722

Cited by 22 publications

(11 citation statements)

References 10 publications

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“…The convergence of the weights of the identifier and controller are shown in Figure 9, respectively, where all of the INN weights converge after 20 s. Calculated by the IPC control station calculation cycle Ts = 100 ms, equivalent to the online iterative training nearly 200 times. The number of iterations is similar to that of some online learning research [30][31][32][33][34].…”

Section: Implementation Of the Drnn Control Algorithmmentioning

confidence: 96%

A New Method of Applying Data Engine Technology to Realize Neural Network Control

Zheng

Song

2019

Algorithms

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This paper presents a novel diagonal recurrent neural network hybrid controller based on the shared memory of real-time database structure. The controller uses Data Engine (DE) technology, through the establishment of a unified and standardized software architecture and real-time database in different control stations, effectively solves many problems caused by technical standard, communication protocol, and programming language in actual industrial application: the advanced control algorithm and control system co-debugging difficulties, algorithm implementation and update inefficiency, and high development and operation and maintenance costs effectively fill the current technical gap. More importantly, the control algorithm development uses a unified visual graphics configuration programming environment, effectively solving the problem of integrated control of heterogeneous devices; and has the advantages of intuitive configuration and transparent data processing process, reducing the difficulty of the advanced control algorithms debugging in engineering applications. In this paper, the application of a neural network hybrid controller based on DE in motor speed measurement and control system shows that the system has excellent control characteristics and anti-disturbance ability, and provides an integrated method for neural network control algorithm in a practical industrial control system, which is the major contribution of this article.

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Section: Implementation Of the Drnn Control Algorithmmentioning

confidence: 96%

A New Method of Applying Data Engine Technology to Realize Neural Network Control

Zheng

Song

2019

Algorithms

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“…Then with the change of variables the complexity of these equations decrease through movement from poly phase winding to two phase winding (q-d). In other words, the stator and rotor variables like voltage, current and flux linkages of an induction machine are transferred to another reference model which remains stationary [1][2][3][4][5][6]. In Fig.1 stator inductance is the sum of the stator leakage inductance and magnetizing inductance (L ls = L s + L m ), and the rotor inductance is the sum of the rotor leakage inductance and magnetizing inductance (L lr = L r + L m ).…”

Section: Dynamic Modeling and Simulation Of Induction Motor Drivementioning

confidence: 99%

“…In order to obtain the stator and rotor currents of induction motor in two phases, Inverse park transformation is applied in the last stage [6]. …”

Section: Dynamic Modeling and Simulation Of Induction Motor Drivementioning

confidence: 99%

Dynamic simulation of induction motor drive using artificial intelligent controller

Laxmi¹,

Menghal²,

Mukhesh³

2014

Proceedings of the 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC)

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In industries, more than 85 % of the motors are Induction Motors, because of the low maintenance and robustness. Maximum torque and efficiency is obtained by the speed control of induction motor. Using Artificial Intelligence (AI) techniques, particularly the neural networks, performance & operation of induction motor drives is improved. This paper presents dynamic simulation of induction motor drive using neuro controller. The integrated environment allows users to compare simulation results between conventional, Fuzzy and Neural network controller (NNW).The performance of fuzzy logic and artificial neural network based controller's are compared with that of the conventional proportional integral controller. The dynamic Modelling and Simulation of Induction motor is done using MATLAB/SIMULINK and the dynamic performance of induction motor drive has been analyzed for artificial intelligent controller.

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“…In the field of industrial system and especially the electric drives, a lot of techniques have emerged to replace the conventional proportional plus integral controller (PI) because it have many drawbacks like sensitivity to changes in parameters of the system by external condition, and also the integration problem [4], some of these techniques use: sliding mode [1], artificial neural networks [2], and the fuzzy logic controllers [3] in the aim to improve the stability and robustness of the controlled system with more performance.…”

Section: Introductionmentioning

confidence: 99%

Robust IM exponential reaching law sensorless control with MRAS-based online parameters identification

Legrioui

Rezgui

Benalla

2015

2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC)

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Lot of works dealt with the robustness of the indirect rotor flux oriented control (IRFOC), however variation of electrical parameters of induction motor (IM), due mainly to the external condition like warming, cause the major problem of this techniques. In this work, we investigate and introduce a method called exponential reaching law (ERL) technique in the sliding mode controller to improve the control performance and remedy to the boring chattering phenomena. So the suppression of the chattering and the increasing of the reaching speed to the sliding surface is our first aim. As well, to get an adaptive sensorless scheme, a three classical proportional-integral controllers (PI) are included in the adaptation mechanism of the model reference adaptive system (MRAS) to obtain a conjoint online estimation of the rotor speed, the stator resistance (R S ) and the inverse of the rotor time constant (1/T r ) respectively. The proposed control technique is experimentally tested via the (RTI) blocks of Matlab/Simulink, and the dSPACE DS1104 card.

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Neural network based dynamic simulation of induction motor drive

Cited by 22 publications

References 10 publications

A New Method of Applying Data Engine Technology to Realize Neural Network Control

A New Method of Applying Data Engine Technology to Realize Neural Network Control

Dynamic simulation of induction motor drive using artificial intelligent controller

Robust IM exponential reaching law sensorless control with MRAS-based online parameters identification

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