Speed Control of Direct Torque Controlled Induction Motor By using PI, Anti-Windup PI And Fuzzy Logic Controller

Açıkgöz, Hakan; Keçecioğlu, Ö. Fatih; Ganı, Ahmet; Şekkeli, Mustafa

doi:10.18201/ijisae.58843

Cited by 9 publications

(8 citation statements)

References 7 publications

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“…Compared to combined classical-AI controllers [18] and AI controllers with static tuning parameters [19], the proposed TSK-PSO-FLC is a dynamic controller (where it could tune its parameters by it-self according to the Ek and dEk). Moreover, compared to traditional DC motor driving techniques [20] the FOC mechanism could deliver the exact resultant magnetic force amount to the PM-BLDC motor (the generated electric torque "Te") which was decided through the proposed dynamic AI controller.…”

Section: Analysis Of the Simulation Test Resultsmentioning

confidence: 99%

“…xi (t) at a discrete-time t then the updated position xi (t+1) and the updated velocity vi (t+1) is determined according to (17), (18) and [15],…”

Section: Dynamic Pso Mechanism (Dpsom)mentioning

confidence: 99%

“…To overcome this problem, in the proposed TSK-PSO-FL controller, the parameters of the dynamic PSO mechanism was tuned in real-time through a separately dedicated TSK-FLC as described in Section 3. Through this TSK-FLC, for every iteration or discrete sample re-initialized the particle positions (solutions) by optimizing the population size (nPop), inertia weights (ωmax and ωmin), acceleration coefficients (a1 and a2) and charged the PSO by adding a dynamic acceleration (af) into the standard equation (18). Finally, this eighteen-dimensional proposed dynamic PSO mechanism could be expressed as shown in (19) and 20 The output of the TSK-FLC determined according to (24), (25), and (26).…”

Section: Dynamic Pso Mechanism (Dpsom)mentioning

confidence: 99%

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ADynamic AI Controllerfor aField-Oriented ControlledBLDC Motorto Achieve the Desired AngularVelocity and Torque

Jayetileke

Mel

Ratnayake

2019

ijisae

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Compared to traditional motor controlling techniques, modern AI controllers have many advantages. However, most of the developed FOC mechanisms are based on classical controlling techniques such as PID controllers, hybrid AI-classical controllers and model reference controllers. All these traditional controllers are based on sophisticated mathematical models (system transfer functions). These traditional controllers are unable to tune its system parameters by itself to adapt according to the non-linear variations of actual speed and torque of the motor. This paper discussed, a novel scheme of metaheuristic adaptive fuzzy logic-particle swarm optimization control mechanism to optimize the speed regulation of electric current space vector-controlled BLDC motor. Therefore, dynamic TSK-PSO-FLC was investigated. The dynamic behaviour of the proposed controller enables it to optimize its tuning parameters by itself under non-linear load and speed varying conditions to track the desired angular speed and the torque trajectories. This is a part of the designed and developed dynamic AI controller, for stability and traction control of an all-wheel-drive electric rover. Therefore, initially, the performance of the proposed controller has been tested on a simulation environment (MATLAB Simulink model) for one wheel. Finally, the identified dynamic parameters through the Simulink model (the sensored BLDC motor and the proposed AI controller) were utilized to test the performance of the developed TSK-PSO-FLC, while it is tracking a given desired speed trajectory of the BLDC motor (sensored, 3-ph and 250 W) in real-time operation. Simulated test results are analyzed and compared with the observed test results through the developed hardware model in addition to the newly published research work. The angular speed of 2500 rpm within a 500 N m torque have been taken into consideration as a generalized condition. It was noticed that the percentage overshoot (Mp%), settling time (Ts) and the steady-state error (Ess) is 0.501, 731.455 μs and 1.22 respectively. Therefore, compared to classical control based FOC mechanisms, the analyzed test results of the proposed control mechanism is showing that it has been optimized and enhanced the speed regulation performance of the BLDC motor significantly while increasing the frequency of the desired input trajectory up to 2 kHz.

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Section: Analysis Of the Simulation Test Resultsmentioning

confidence: 99%

“…xi (t) at a discrete-time t then the updated position xi (t+1) and the updated velocity vi (t+1) is determined according to (17), (18) and [15],…”

Section: Dynamic Pso Mechanism (Dpsom)mentioning

confidence: 99%

Section: Dynamic Pso Mechanism (Dpsom)mentioning

confidence: 99%

See 1 more Smart Citation

ADynamic AI Controllerfor aField-Oriented ControlledBLDC Motorto Achieve the Desired AngularVelocity and Torque

Jayetileke

Mel

Ratnayake

2019

ijisae

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“…These four input variables heavily influence the control variable (u) applied to the cart. The accuracy of membership functions (MFs) and rule base of the T-S fuzzy controller remarkably improves the performance of the system (Sugeno, 1985;Marzi, 2005;Acikgoz, 2014).…”

Section: Takagi-sugeno Type Fuzzy Logic Controller Designmentioning

confidence: 99%

Modelling and Performance Comparison of PD and Takagi-Sugeno Type Fuzzy Logic Controllers for Inverted Pendulum System

Şit

Kılıç

Özçalık

et al. 2016

Bitlis Eren University Journal of Science and Technology

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In recent years, fuzzy logic based modeling and control methods have been increasingly used in the academic field. Inverted pendulum system consists of conventional materials with nonlinear dynamic structure in order to test types of controllers. This paper presents a simulation study of Takagi-Sugeno type fuzzy logic controllers for an inverted pendulum (cart-pole) system. A Takagi-Sugeno method was applied to the inverted-pendulum system, which can balance the pendulum over a greater range of pole angle and cart position. Takagi-Sugeno fuzzy logic controller proposed in this study is compared with the conventional PD controller in order to demonstrate the performance of the proposed controller. This study aims to determine the best control strategy that provides better performance in terms of pendulum's angle and cart's position. A Takagi-Sugeno type fuzzy logic controller yields better results. Simulation results demonstrated that the proposed fuzzy controller is effectiveness and robust.

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“…Vector controlled drives provide excellent dynamic performance of the induction motor and offers good satisfactory steady state as well as transient response and it works like a separately excited DC motor. This method uses the dynamic mathematical model of induction motor and allows independent control of flux and torque [11][12][13][14][15]. IFOC technique is widely used in induction motor drive system to obtain high performances in terms of torque and speed [4,[16][17].…”

Section: Introductionmentioning

confidence: 99%

Efficient speed control of induction motor using RBF based model reference adaptive control method

2016

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Original scientific paperThis paper proposes a model reference adaptive speed controller based on artificial neural network for induction motor drives. The performance of traditional feedback controllers has been insufficient in speed control of induction motors due to nonlinear structure of the system, changing environmental conditions, and disturbance input effects. A successful speed control of induction motor requires a nonlinear control system. On the other hand, in recent years, it has been demonstrated that artificial intelligence based control methods were much more successful in the nonlinear system control applications. In this work, it has been developed an intelligent controller for induction motor speed control with combination of radial basis function type neural network (RBF) and model reference adaptive control (MRAC) strategy. RBF is utilized to adaptively compensate the unknown nonlinearity in the control system. The indirect field-oriented control (IFOC) technique and space vector pulse width modulation (SVPWM) methods which are widespread used in high performance induction motor drives has been preferred for drive method. In order to demonstrate the reliability of the control technique, the proposed adaptive controller has been tested under different operating conditions and compared performance of conventional PI controller. The results show that the proposed controller has got a clear superiority to the conventional linear controllers.Key words: Induction motor, neural network, model reference adaptive control, vector control.Učinkovito upravljanje brzinom induktivnog motora korištenjem metode adaptivnog upravljanja s referentnim modelom zasnovane na RBF-u. Ovaj rad prikazuje adaptivni regulator s referentnim modelom zasnovan na neuronskoj mreži za induktivne motore. Ponašanje tradicionalnih regulatora s povratnom vezom pokazalo se nedovoljno dobrom za upravljanje brzinom induktivnih motora zbog nelineatnosti strukture sustava, promjene okolišnih uvjeta, i efekta ulaznih poremećaja. Uspješno upravljanje brzinom induktivnog motora zahtjeva nealinearne upravljačke sustave. S druge strane, posljednjih godina pokazano je kako su upravljačke metode zasnovane na umjetnoj inteligenciji bitno uspješnije u primjenama upravljanja nelinearnim sustavima. U ovome radu razvijen je inteligentni regulator za upravljanje brzinom induktivnog motora s kombinacijom radijalne neuronske mreže (RBF) i strategije adaptivnog regulatora s referentnim modelom (MRAC). RBF je realiziran kako bi adaptivno kompenzirao nepoznatu nelinearnost u sustavu upravljanja. Tehnika indirektnog vektorskog upravljanja (IFOC) i metoda prostorno vektorske širinsko impulsne modulacije koje su široko korištene za induktivne motore visokih performansi preferirani su kao metode u ovome radu. Kako bi se prikazala pouzdanost tehnike upravljanja, predloženi adaptivni regulator ispitan je u različitih uvjetima rada i usporeeno je vladanje s obzirom na konvencionalni PI regulator. Rezultati pokazuju kako predloženi regulator očito pokazuje bolj...

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Speed Control of Direct Torque Controlled Induction Motor By using PI, Anti-Windup PI And Fuzzy Logic Controller

Cited by 9 publications

References 7 publications

ADynamic AI Controllerfor aField-Oriented ControlledBLDC Motorto Achieve the Desired AngularVelocity and Torque

ADynamic AI Controllerfor aField-Oriented ControlledBLDC Motorto Achieve the Desired AngularVelocity and Torque

Modelling and Performance Comparison of PD and Takagi-Sugeno Type Fuzzy Logic Controllers for Inverted Pendulum System

Efficient speed control of induction motor using RBF based model reference adaptive control method

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