On-line training of adaptive neural network controllers

Lima, A.M.N.; Cavalcanti, José Homero Feitosa; Deep, G.S.

doi:10.1109/iecon.1994.397998

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…The learning ability, self-adapting capability, super fast computing features of artificial intelligence (AI) technology has brought a new challenge to electrical engineers who are struggling with complex, fast advancing, and interdisciplinary areas of technology. Published literatures [18][19][20][21][22][23][24][25][26][27][28][29][30] illustrate the application of AI based controllers for the speed control of DC drives.…”

Section: Introductionmentioning

confidence: 99%

Model reference controlled separately excited DC motor

George

Basu

Chiat

2009

Neural Comput & Applic

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This research article proposes the speed control of a separately excited DC motor (SEDM) in the constant torque region. The novelty of this article lies in the application of artificial neural network-based model reference controller (MRC) for the speed control of SEDM. This paper also discusses and compares the speed control systems of SEDM using PI-controlled and fuzzy logic-controlled chopper circuit with MRC. The entire system has been modeled using MATLAB 7.0/SIMULINK toolbox. It has been observed that chopper-controlled speed control system could be eliminated by the use of MRC and the performance of the proposed system is comparable with speed control system using chopper circuit.

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Section: Introductionmentioning

confidence: 99%

Model reference controlled separately excited DC motor

George

Basu

Chiat

2009

Neural Comput & Applic

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“…The aim of this paper is to simulate a speed control system [2] that can predict the dynamic behaviors of a DC motor fed by chopper circuit. The superior performance of artificial intelligence (AI) based controllers urged power system and power electronic engineers to replace conventional speed control circuit with intelligent speed controllers [3,4]. To test the validity of the proposed controller, it is tested against speed and load variation and compared to other conventional and intelligent controllers.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Neuro-Fuzzy Speed Controller for a Separately excited DC Motor

Omar¹,

Haikal²,

Areed³

2012

IJCA

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In this paper an Adaptive Neuro Fuzzy Inference System (ANFIS) controller using error and derivative of error inputs is proposed for the speed control of a separately excited dc motor (SEDCM) using chopper circuit. This paper investigates the design and simulation of an adaptive Neuro-Fuzzy Inference System (ANFIS) controller for the speed of a DC motor. The performance of the proposed system has been compared with conventional one, where the conventional PI controller (speed controller) in the Chopper-Fed DC Motor Drive is replaced by the adaptive Neuro-Fuzzy controller to improve the dynamic behavior of the model. Computer Simulation is conducted to demonstrate the performance of the proposed controller and results show that the proposed design succeeded over the conventional PI controller where it enhances dynamic responses and reduce ripples. Moreover, results of comparing the proposed ANFIS controller with other related work is improved. The entire system is modeled using MATLAB 2009 toolbox.

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“…Several other speed control techniques using conventional controllers have been reported in [5,17] . Recently, the superior performance of artificial intelligence (AI) based controllers urged power system and power electronic engineers to replace conventional speed control circuit with intelligent speed controllers [18]- [30] .…”

Section: Introductionmentioning

confidence: 99%

Speed Control of Separately Excited DC Motor

George¹

2008

American J. of Applied Sciences

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This paper proposes the speed control of a separately excited dc motor (SEDM) by varying armature voltage. The novelty of this paper lies in the application of nonlinear autoregressive-moving average (NARMA)L2 controller for the speed control of SEDM. This paper also discusses speed control of a SEDM using chopper circuit. The performance of the proposed system has been compared with the traditional one using conventional controllers. The entire system has been modeled using MATLAB 7.0 toolbox. It has been found that both PI and hysteresis current controllers could be eliminated by the use of NARMA-L2 controller

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On-line training of adaptive neural network controllers

Cited by 6 publications

References 8 publications

Model reference controlled separately excited DC motor

Model reference controlled separately excited DC motor

An Adaptive Neuro-Fuzzy Speed Controller for a Separately excited DC Motor

Speed Control of Separately Excited DC Motor

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