Control of the Drive System With Stiff and Elastic Couplings Using Adaptive Neuro-Fuzzy Approach

Orłowska-Kowalska, Teresa; Szabat, K.

doi:10.1109/tie.2006.888787

Cited by 106 publications

(60 citation statements)

References 17 publications

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“…Artificial intelligence (AI) techniques have recently been applied in power electronics and motor drives [10]. AI techniques aim to implant human or natural intelligence in a computer so that this computer can think intelligently.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation of Brain Emotional Learning Developed for Digital Signal Processor-Based Interior Permanent Magnet Synchronous Motor Drive Systems

Sadeghi¹,

Daryabeigi²

2014

Journal of Power Electronics

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In this study, a brain emotional learning-based intelligent controller (BELBIC) is developed for the speed control of an interior permanent magnet synchronous motor (IPMSM). A novel and simple model of the IPMSM drive structure is established with the intelligent control system, which controls motor speed accurately without the use of any conventional PI controllers and is independent of motor parameters. This study is conducted in both real time and simulation with a new control plant for a laboratory 3 ph, 3.8 Nm IPMSM digital signal processor (DSP)-based drive system. This DSP-based drive system is then compared with conventional BELBIC and an optimized conventional PI controller. Results show that the proposed method performs better than the other controllers and exhibits excellent control characteristics, such as fast response, simple implementation, and robustness with respect to disturbances and manufacturing imperfections.

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Section: Introductionmentioning

confidence: 99%

Real-Time Implementation of Brain Emotional Learning Developed for Digital Signal Processor-Based Interior Permanent Magnet Synchronous Motor Drive Systems

Sadeghi¹,

Daryabeigi²

2014

Journal of Power Electronics

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“…1. In literature two concepts of ANN adaptive controller were described literature [2,3,4]. In [4] the neuro-fuzzy adaptive model following control (MFC) was presented.…”

Section: Introductionmentioning

confidence: 99%

“…In literature two concepts of ANN adaptive controller were described literature [2,3,4]. In [4] the neuro-fuzzy adaptive model following control (MFC) was presented. In this work the neuro-fuzzy controller is trained "on-line" on base of error (difference) between output signals of reference model and controlled plant.…”

Section: Introductionmentioning

confidence: 99%

Adaptive speed controller for servodrive based on artificial neural network trained online

Pajchrowski

Zawirski

2013

2013 15th European Conference on Power Electronics and Applications (EPE)

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The aim of the research was to find out a method of adaptive speed control, which guaranties robustness against variation of selected parameters of system like moment of inertia, time constant etc. The goal was achieved due to application of artificial neural network, which was trained on line on the base of speed control error. The work shows a structure of Artificial Neural Network (ANN), applied as adaptive speed controller, and presents an algorithm of ANN training. Some new modified versions of this algorithm were analysed and verified by simulation and experimental tests. Presented results well illustrate interestion properties of the proposed adaptive neural speed controller.

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“…In addition to classical [12,13], fuzzy logic [14], robust control [4] approximations system identification [15] methods have also been used in control of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Gray-fuzzy control of a nonlinear two-mass system

Erenturk

2010

Journal of the Franklin Institute

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Control of the Drive System With Stiff and Elastic Couplings Using Adaptive Neuro-Fuzzy Approach

Cited by 106 publications

References 17 publications

Real-Time Implementation of Brain Emotional Learning Developed for Digital Signal Processor-Based Interior Permanent Magnet Synchronous Motor Drive Systems

Real-Time Implementation of Brain Emotional Learning Developed for Digital Signal Processor-Based Interior Permanent Magnet Synchronous Motor Drive Systems

Adaptive speed controller for servodrive based on artificial neural network trained online

Gray-fuzzy control of a nonlinear two-mass system

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