Recurrent Fuzzy Neural Network Using Genetic Algorithm for Linear Induction Motor Servo Drive

Lin, Faa-Jeng; Huang, Po-Kai

doi:10.1109/iciea.2006.257084

Cited by 6 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The controller will continue to apply the algorithm to cut the error down to zero, thus achieving the assigned setpoint. There are many closed-loop speed control algorithms for induction motor speed control, such as PID [19], the fuzzy logic controller (FLC) [20,21], sliding mode controller (SMC) [22], Artificial Neural Network (ANN) [23,24], and the combination between conventional and artificial intelligent method as can be found in [25][26][27]. Compared to others, PID is mostly used because of its simplicity and reasonably good performance [28].…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Torque Measurement using Prony Brake System and Close-loop Speed Control

Maghfiroh

Titus

Sujono

et al. 2022

IJRCS

View full text Add to dashboard Cite

Three-phase induction motors are the main drivers of the industrial world because of their low price and good reliability. However, this type of motor does not have built-in speed control. These problems can be overcome by utilizing the Variable Frequency Drive (VFD) inverter. This research investigates the induction motor's characteristics in every load condition and combines a VFD inverter with an external speed controller based on Arduino. The motor is mounted on a Prony brake testbed frame to measure the motor's torque and mechanical power. The test results show the highest torque value obtained is 0.57 Nm, and the highest output power value is 0.042 kW. The motor cannot maintain the setpoint speed after loading in the open-loop control system. Meanwhile, the closed-loop control system has been successfully implemented, and the motor can return the speed to the setpoint value after loading, with an average settling time of 14.67 seconds.

show abstract

Section: Introductionmentioning

confidence: 99%

Induction Motor Torque Measurement using Prony Brake System and Close-loop Speed Control

Maghfiroh

Titus

Sujono

et al. 2022

IJRCS

View full text Add to dashboard Cite

show abstract

“…However, the PMLSM does not use conventional gears or ball screws, so the payload upon the mover greatly affects the positioning performance (Liu et al, 2004). To cope with this problem, many advanced control techniques (Qingding et al, 2002;Lin et al, 2007;Wai & Chu, 2007), such as fuzzy control, neural networks control and robust control have been developed and applied to the position control of the PMLSM drive to obtain high operating performance. However, the execution of a neural network or fuzzy controller requires many computations, so implementing these highly complex control algorithms depend on the PC systems in most studies before (Qingding et al, 2002;Liu et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Advanced Knowledge Application in Practice

Fuerstner¹

2010

View full text Add to dashboard Cite

An Advanced and Automated Neural Network based Textile Defect Detector 3 under structured environments -a feat that is almost non-existent in list developed countries like Bangladesh. There are some works in (Ciamberlini et al., 1996) based on the optical fourier transform directly obtained from the fabric with optical devices and a laser beam. Digital image processing techniques have been increasingly applied to textured samples analysis over the last ten years. Several authors have considered defect detection on textile materials. Kang et al. (Kang et al., 1999), (Kang et al., 2001 analyzed fabric samples from the images obtained from transmission and reflection of light to determine its interlacing pattern. Wavelets had been applied to fabric analysis by Jasper et al.

show abstract

“…It takes the chromosome as input and produces a number or list of numbers such as objective value as a measure to the chromosome's performance. This is a main link between the algorithm and the system [10].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization

Mohammadi

Akhavan

2014

Journal of Engineering

View full text Add to dashboard Cite

A cost effective off-line method for equivalent circuit parameter estimation of an induction motor using hybrid of genetic algorithm and particle swarm optimization (HGAPSO) is proposed. The HGAPSO inherits the advantages of both genetic algorithm (GA) and particle swarm optimization (PSO). The parameter estimation methodology describes a method for estimating the steadystate equivalent circuit parameters from the motor performance characteristics, which is normally available from the nameplate data or experimental tests. In this paper, the problem formulation uses the starting torque, the full load torque, the maximum torque, and the full load power factor which are normally available from the manufacturer data. The proposed method is used to estimate the stator and rotor resistances, the stator and rotor leakage reactances, and the magnetizing reactance in the steady-state equivalent circuit. The optimization problem is formulated to minimize an objective function containing the error between the estimated and the manufacturer data. The validity of the proposed method is demonstrated for a preset model of induction motor in MATLAB/Simulink. Also, the performance evaluation of the proposed method is carried out by comparison between the results of the HGAPSO, GA, and PSO.

show abstract

Recurrent Fuzzy Neural Network Using Genetic Algorithm for Linear Induction Motor Servo Drive

Cited by 6 publications

References 12 publications

Induction Motor Torque Measurement using Prony Brake System and Close-loop Speed Control

Induction Motor Torque Measurement using Prony Brake System and Close-loop Speed Control

Advanced Knowledge Application in Practice

Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization

Contact Info

Product

Resources

About