Design adaptive neuro-fuzzy speed controller for an electro-mechanical system

Omar, Bouattane; Haikal, Amira Y.; Areed, Fayez F.

doi:10.1016/j.asej.2011.07.003

Cited by 20 publications

(12 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hybrid ANFIS controller was first introduced in 1993 by Jang [4] by combining a fuzzy interface system (FIS) with the learning capability of artificial neural networks (ANN) [5]. ANFIS has previously been applied in many fields such as speed controller of electro-mechanical system [6], control of autonomous vehicles [7], Voltage and Frequency Stability in Islanded Microgrids [5] and also in Power Flow Analysis and Control [8,9].…”

Section: Introductionmentioning

confidence: 99%

Application of metaheuristic control strategies to voltage regulation

Ramjug-Ballgobin

Fowdar

2019

SN Appl. Sci.

View full text Add to dashboard Cite

This paper proposes four controllers applied to two existing generator models (1st and 4th order) with a type 1 excitation system taking into account nonlinearities. Jaya, crow search, and invasive weed optimization algorithm based PID controllers as well as adaptive neuro-fuzzy interface system controller were used to regulate the generator output in case of sudden voltage fluctuations. The results obtained were found to be very promising since most of them improved the uncompensated systems response in terms of overshoot, steady-state error, settling time and rise time. The overall best controller was found to be the Jaya based PID due to its responses as well as its ability to quickly converge to the best fitness.

show abstract

Section: Introductionmentioning

confidence: 99%

Application of metaheuristic control strategies to voltage regulation

Ramjug-Ballgobin

Fowdar

2019

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…The SEDC motor speed was regulated using an ANFIS, but in this system a chopper circuit was utilized. Moreover, the speed response was compared with fuzzy, PI and PID, where the effect of temperature on the speed was considered [12].…”

Section: Introductionmentioning

confidence: 99%

Study on the Extent of the Impact of Data Set Type on the Performance of ANFIS for Controlling the Speed of DC Motor

Guo

Mohamed

2019

J. Eng. Technol. Sci.

View full text Add to dashboard Cite

This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) for tracking SEDC motor speed in order to optimize the parameters of the transient speed response by finding out the perfect training data provider for the ANFIS. The controller was adjusted using PI, PD and PIPD to generate data sets to configure the ANFIS rules. The performance of the ANFIS controllers using these the different data sets was investigated. The efficiencies of the three controllers were compared to each other, where the PI, PD, and PIPD configurations were replaced by ANFIS to enhance the dynamic action of the controller. The performance of the proposed configurations was tested under different operating situations. Matlab's Simulink toolbox was used to implement the designed controllers. The resultant responses proved that the ANFIS based on the PIPD dataset performed better than the ANFIS based on the PI and PD data sets. Moreover, the suggested controller showed a rapid dynamic response and delivered better performance under various operating conditions.

show abstract

“…ANFIS ArchitectureBased onFigure 1, ANFIS architecture consists of layers described as follows: in this layer, each input was mapped according to the classification. Each node in this layer is adaptive with membership set of errors (e) and derivative error (de)[22], there were nine rules in this layer. Each node in this layer represents the activation level, which presented in the equation(5)[22][23], the node in this layer calculates the ratio of total activation level, which described in the equation(6)[22][23], adaptive node i in this layer calculates the combination of the rule.…”

mentioning

confidence: 99%

Position Control of 1-DOF High-Precision Rotary Table using Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller

Saputra

Nurhakim

Utomo

2019

emitter

View full text Add to dashboard Cite

Research of position control of 1-DOF high-precision rotary table using adaptive Neuro-Fuzzy inference system (ANFIS) controller has been done. In the closed-loop system without a controller, the response was oscillating and pounding caused by inertial torque. It because a rotary table receives a considerable load. Based on this, the ANFIS controller is needed to eliminate oscillations and compensate for the inertia. The result shows that there was no oscillation or overshoot with the steady-state error value of 2.27% for the reference angle of 45Â°, valued at 0.10% reference angle of 180Â°, and valued at 0% reference angle of 360Â°. The result proves that ANFIS controllers can eliminate oscillations with and compensate for inertia.

show abstract

Design adaptive neuro-fuzzy speed controller for an electro-mechanical system

Cited by 20 publications

References 3 publications

Application of metaheuristic control strategies to voltage regulation

Application of metaheuristic control strategies to voltage regulation

Study on the Extent of the Impact of Data Set Type on the Performance of ANFIS for Controlling the Speed of DC Motor

Position Control of 1-DOF High-Precision Rotary Table using Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller

Contact Info

Product

Resources

About