Adaptive sliding-mode type-2 neuro-fuzzy control of an induction motor

Masumpoor, Saleh; Yaghobi, Hamid; Khanesar, Mojtaba Ahmadieh

doi:10.1016/j.eswa.2015.04.046

Cited by 69 publications

(54 citation statements)

References 30 publications

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“…The weight of rice on the conveyor belt varies each time; thus, the weight of rice cannot be determined by fixing the advancing distance. When the difference between the entered value in the fuzzy controller and the practical weight is positive, the stepper motor operates one step pitch in one operation cycle and realizes the integral control of motor rpm by continuously monitoring the weighting signals [12][13][14][15][16][17][18] .The motor stops running when the difference between the entered value and the practical weight is zero.…”

Section: Fig 4 Control System Flowchartmentioning

confidence: 99%

Design of an ARM - based Automatic Rice - Selling Machine for Cafeterias

Kang¹,

Xu²,

Li³

et al. 2016

JESTR

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To address the problems of low selling efficiency, poor sanitation conditions, labor-intensive requirement, and quick rice cooling speed in manual rice selling in cafeterias, especially in colleges and secondary schools, this paper presented an Advanced RISC Machines (ARM) microprocessor-based rice-selling machine for cafeterias. The machines consisted of a funnel-shaped rice bin, a thermal insulation box, and a conveying and scattering mechanism. Moreover, this machine exerts fuzzy control over stepper motor rpm, and the motor drives the conveyor belt with a scraper to scatter rice, deliver it, and keep it warm. Apart from an external 4*4 keyboard, a point of sale (POS) machine, an ARM process and a pressure sensor, the machine is also equipped with card swiping and weighting mechanisms to achieve functions of card swiping payment and precise measurement, respectively. In addition, detection of the right amount of rice and the alarm function are achieved using an ultrasonic sensor and a beeper, respectively. The presence of the rice container on the rice outlet is detected by an optoelectronic switch. Results show that this rice-selling machine achieves precise measurement, quick card swiping, fast rice selling, stable operation, and good rice heat preservation. Therefore, the mechanical design enables the machine to achieve its goals.

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Section: Fig 4 Control System Flowchartmentioning

confidence: 99%

Design of an ARM - based Automatic Rice - Selling Machine for Cafeterias

Kang¹,

Xu²,

Li³

et al. 2016

JESTR

View full text Add to dashboard Cite

show abstract

“…Due to this obtain improved performance the Induction motor [5].The simplicity SVM algorithm without flux controller and current transformations developed using direct flux vector control method [6]. The high power rating Induction motor drive ripple in torque is reduced using SVPWM method and also comparison of ripple conventional two level inverter with five level inverter [7].The induction motor performance is improved with type-2 neuro fuzzy sliding mode compare with Neuro fuzzy type-1 when control parameter changes [8].The clustering and gradient algorithm are used to developed the novel neuro fuzzy type-2 system [9]. Pulse width modulation with hybrid space to obtain three phase duty ratio is difficult at higher switching levels frequencies and require sector identification .Implementation of NFC is independent of switching frequency and duty ratio with rotation angle ,change of rotation of angle at higher switching level frequency [10].To reduce torque ripple with performance of induction motor type-2 fuzzy logic speed & current controller placed instead of convention PI speed & current controller .The induction drive implement at no load & step change in load and THD values are compared [11].Direct torque control of Induction motor drive with SVPWM method is used to reduced ripples in torque ,voltage and current [12].Type-2 FNN is compared with type-1 fuzzy systems [13].The comparison of performance between proportional integral (PIDTC) with F2DTC [14].…”

Section: Introductionmentioning

confidence: 99%

Neuro Fuzzy Type-2 Based Space Vector Modulation for Inverter Fed Induction Motor Drive

Gadde*¹,

Sukumar²

2019

IJRTE

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In indirect vector control the conventional speed and current controllers operate satisfactory when the operating point is constant. But the operating point is always dynamic, the reference voltages obtained in a closed loop system feding to the inverter contains more harmonics. Due to this the pulses which are going to produce are uneven. Which intern produces the inverter output voltages which are more harmonics contained. In order to produce the better output voltage from the inverter, this paper presents neuro-fuzzy type-2 space vector modulation (NFT2) technique .A performance comparison of the inverter is done with conventional space vector modulation(SVM) and neuro fuzzy type- 1(NFT1) system using matlab simulation & experimental validation. The performance parameters of the induction motor based on current, torque and speed with neuro-fuzzy type-2 space vector modulation is compared with conventional and type-1neuro fuzzy SVM. The % THD of inverter output voltages are also compared .The experimental validation a dspace-1104 is used to analyze performance of induction motor which is obtained by the simulation. The experimental validations are carried out considering 2HP Induction motor in the lab

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“…Studies in recent years, in order to improve the performance of the induction motor drive systems, used artificial intelligence‐based methods such as artificial neural network (ANN), fuzzy logic, adaptive neuro‐fuzzy inference system (ANFIS), and genetic algorithm, expert system …”

Section: Introductionmentioning

confidence: 99%

“…7,8 Studies in recent years, in order to improve the performance of the induction motor drive systems, used artificial intelligence-based methods such as artificial neural network (ANN), fuzzy logic, adaptive neuro-fuzzy inference system (ANFIS), and genetic algorithm, expert system. [9][10][11][12][13] The ANN has been successfully used for the control of dynamic system and identification. It is easy to design nonlinear controllers with the ability ANN modeling of nonlinear dynamical systems.…”

mentioning

confidence: 99%

Adaptive controller with RBF neural network for induction motor drive

Kılıç

Özçalık

Şit

2017

Int J Numerical Modelling

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In this paper, the radial basis function neural network‐based model reference adaptive speed control for vector controlled induction motor drive system is presented. The speed control of induction motors is challenging because of their complex mathematical model, non‐linear structure, and time varying dynamics. The radial basis function neural network is used to compensate the non‐linearity which comes from the non‐linear state equations of induction motor model. Neural network parameters are online updated via gradient descent algorithm to minimize the error. The drive system has been tested under various operating conditions. This paper demonstrated benefits of the proposed control approach by comparing the algorithm to conventional PI controllers. The results show that the proposed controller ensures good robustness and stable operation of the system under variable speed and variable loads than the PI controller.

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Adaptive sliding-mode type-2 neuro-fuzzy control of an induction motor

Cited by 69 publications

References 30 publications

Design of an ARM - based Automatic Rice - Selling Machine for Cafeterias

Design of an ARM - based Automatic Rice - Selling Machine for Cafeterias

Neuro Fuzzy Type-2 Based Space Vector Modulation for Inverter Fed Induction Motor Drive

Adaptive controller with RBF neural network for induction motor drive

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