Robust speed regulation of indirect vector control induction motor using fuzzy logic controllers based on optimization algorithms

Zeb, Kamran; Uddin, Waqar; Haider, Aun; Belal, Syed Muhammad; Mehmood, C. A.; Khan, M. A.; Kim, H. J.

doi:10.1007/s00202-017-0553-z

Cited by 21 publications

(17 citation statements)

References 43 publications

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“…If the field angle θ f is determined by using Hall sensors or terminal currents and voltages or flux-sensing windings, then it is recognized as a Direct Field Oriented Control (DFOC) scheme. When the field angle is calculated by utilizing the position of the rotor and partial estimation with only machine parameters, without using additional variables, e.g., voltages and currents, it is called an IFOC scheme [24,25]. Figure 3 depicts the classification of Field Oriented Control Schemes (FOCS) for IM [9].…”

Section: Field Oriented Control Scheme: a Taxonomy Overviewmentioning

confidence: 99%

“…Furthermore, this research is based on estimated field angle. with only machine parameters, without using additional variables, e.g., voltages and currents, it is called an IFOC scheme [24,25]. Figure 3 depicts the classification of Field Oriented Control Schemes (FOCS) for IM [9].…”

Section: Field Oriented Control Scheme: a Taxonomy Overviewmentioning

confidence: 99%

“…The rotor flux λ r , field angle θ f and electromagnetic torque T em can be obtained from i sd , i sq , and ω mech as inputs. The SVPWM is designed from [24,25]. In the case of Sinusoidal Pulse Width Modulation (PWM) , ( ) = 0.612 and SVPWM , ( ) = 0.707 .…”

Section: Implementation Of Indirect Field Oriented Control Schemementioning

confidence: 99%

“…The advance adaptive and robust controllers are used to minimize the error between the reference value and measured value and provide a reference current * to the internal loop. The PI controller The SVPWM is designed from [24,25]. In the case of Sinusoidal Pulse Width Modulation (PWM) V LL,max (rms) = 0.612V d and SVPWM V LL,max (rms) = 0.707V d .…”

Section: Implementation Of Indirect Field Oriented Control Schemementioning

confidence: 99%

“…Though, these linear and non-linear control strategies lack analysis of load disturbances, parameter uncertainties due to inductances and temperature, and speed variation in comparison with various optimal and adaptive control strategies. Moreover, electrical fault perturbation investigation, e.g., double phase, single phase, overvoltage, and undervoltage, has not been incorporated in previous work done on IFOC IM drives [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Simulations of Adaptive Design Approaches to Control the Speed of an Induction Machine Considering Parameter Uncertainties and External Perturbations

et al. 2018

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Recently, the Indirect Field Oriented Control (IFOC) scheme for Induction Motors (IM) has gained wide acceptance in high performance applications. The IFOC has remarkable characteristics of decoupling torque and flux along with an easy hardware implementation. However, the detuning limits the performance of drives due to uncertainties of parameters. Conventionally, the use of a Proportional Integral Differential (PID) controller has been very frequent in variable speed drive applications. However, it does not allow for the operation of an IM in a wide range of speeds. In order to tackle these problems, optimal, robust, and adaptive control algorithms are mostly in use. The work presented in this paper is based on new optimal, robust, and adaptive control strategies, including an Adaptive Proportional Integral (PI) controller, sliding mode control, Fuzzy Logic (FL) control based on Steepest Descent (SD), Levenberg-Marquardt (LM) algorithms, and Hybrid Control (HC) or adaptive sliding mode controller to overcome the deficiency of conventional control strategies. The main theme is to design a robust control scheme having faster dynamic response, reliable operation for parameter uncertainties and speed variation, and maximized torque and efficiency of the IM. The test bench of the IM control has three main parts: IM model, Inverter Model, and control structure. The IM is modelled in synchronous frame using d q modelling while the Space Vector Pulse Width Modulation (SVPWM) technique is used for modulation of the inverter. Our proposed controllers are critically analyzed and compared with the PI controller considering different conditions: parameter uncertainties, speed variation, load disturbances, and under electrical faults. In addition, the results validate the effectiveness of the designed controllers and are then related to former works.

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Section: Field Oriented Control Scheme: a Taxonomy Overviewmentioning

confidence: 99%

Section: Field Oriented Control Scheme: a Taxonomy Overviewmentioning

confidence: 99%

Section: Implementation Of Indirect Field Oriented Control Schemementioning

confidence: 99%

Section: Implementation Of Indirect Field Oriented Control Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Simulations of Adaptive Design Approaches to Control the Speed of an Induction Machine Considering Parameter Uncertainties and External Perturbations

et al. 2018

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Real‐time adaptive speed control of vector‐controlled induction motor drive based on online‐trained Type‐2 Fuzzy Neural Network Controller

Açıkgöz

2020

Int Trans Electr Energ Syst

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Background: The speed control of induction motor having superior features such as high efficiency, robust construction, low maintenance and low cost is carried out more effectively with the developments in control theory. With innovations in power electronics and microprocessor technology, it has been made possible to use the vector control method for applications requiring high performance in induction motor drives. Aim and Objective: In this study, Type-2 Fuzzy Neural Network (T2FNN) controller which is durable, adaptable and has fast dynamic response capabilities against parameter changes, is proposed to obtain a robust speed response from induction motor. Materials and Methods: Matlab/RTI model is developed through DS1103 controller card to experimentally test the speed performance of the proposed controller based induction motor. The proposed controller is trained on-line to improve the robustness of the induction motor against disturbances. After that, experimental studies are built to investigate the speed control behavior and effectiveness of the induction motor. Results and Discussion: The performance of T2FNN controller is compared with PI and Type-1 Fuzzy Neural Network (T1FNN) controllers. The experimental results clearly indicate that the proposed controller has a faster and more stable dynamic response capability. Conclusion: The proposed controller is significantly improved the dynamic response of the induction motor compared to T1FNN and PI controllers. In addition, the settling time, overshoot and recovery time promoting percentages of T1FNN and PI controllers by T2FNN controller are in satisfactory levels during all steady-state and the transient conditions.

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Super-Twisting and Nonsingular Terminal Sliding Mode Direct Torque Control of Induction Motor Drives

2019

Lecture Notes in Electrical Engineering

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Robust speed regulation of indirect vector control induction motor using fuzzy logic controllers based on optimization algorithms

Cited by 21 publications

References 43 publications

Dynamic Simulations of Adaptive Design Approaches to Control the Speed of an Induction Machine Considering Parameter Uncertainties and External Perturbations

Dynamic Simulations of Adaptive Design Approaches to Control the Speed of an Induction Machine Considering Parameter Uncertainties and External Perturbations

Real‐time adaptive speed control of vector‐controlled induction motor drive based on online‐trained Type‐2 Fuzzy Neural Network Controller

Super-Twisting and Nonsingular Terminal Sliding Mode Direct Torque Control of Induction Motor Drives

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