Dynamic simulation of induction motor drive using artificial intelligent controller

Laxmi, A. Jaya; Menghal, P. M.; Mukhesh, N.

doi:10.1109/ciec.2014.6959098

Cited by 12 publications

(8 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Park and Clarke vector transformations are one of the keys to vector control of induction motors. The currents id and iq in the d -q frame can be derived from the currents in the a -� frame by the expressions called Park transformation defined as (16) where e is the rotor flux angle (also called transform angle, generated from the speed signal and slip signal) and the currents iu and i� in stationary frame are transformed by Clarke transformation defined as (17) where, 1a,lb and ic stands for the motor phase currents. In this proposed method vector control (field oriented control) is used to control speed of the AC motor drives.…”

Section: Termination Criterion Soiterationsmentioning

confidence: 99%

Vector control of induction motor drive using optimized GA technique

Selvi¹,

Singaram²

2015

2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO)

View full text Add to dashboard Cite

In this paper, we present the design of a Proportional Integral (PI) controller using Genetic Algorithm (GA) to control the speed of an induction motor (IM). The GA methods have been employed successfully to solve complex optimization problems.The main advantage of this metaheuristic method (GA) is its simplicity and robustness. Only speed error is employed as input to the GA so that the computational burden of the GA is reduced and drastically decrease the rise time. It becomes suitable for real time industrial drive applications. The goal of this work is to show that the optimization by GA gives us the possibility of designing a powerful PI controller by optimizing its parameters. The proposed GA based controller does not decrease the system performance as compared to the conventional controller and Neuro-Fuzzy Controller (NFC). In order to prove the performance of the proposed method, the comparative study has been carried out between the proposed, existing and conventional methods.

show abstract

Section: Termination Criterion Soiterationsmentioning

confidence: 99%

Vector control of induction motor drive using optimized GA technique

Selvi¹,

Singaram²

2015

2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO)

View full text Add to dashboard Cite

show abstract

“…The use of software packages such as Simulink / MATLAB, PSPICE, etc. have become a vital tool for performance prediction and system behavior of motor-drives [1]. The transient behavior of a motor-drive system is of interest due to this type of system typically needing speed and torque control [3].…”

Section: Introductionmentioning

confidence: 99%

A Simulink / MATLAB Model of a Three-Phase Sinusoidal PWM Inverter Driven Induction Motor

Leedy¹,

Grider²,

Priddy³

2019

IJSCE

View full text Add to dashboard Cite

A three-phase voltage source inverter driven induction motor dynamic model developed using Simulink / MATLAB is presented. The presented model is derived from the d-q motor model. A modular approach is used in the construction of the motor-drive system model. The model presented is useful for studying both the steady-state and transient behavior of the motor drive system. The developed model enables the user to access all parameters of interest. Modifications can be made to the system model in order for it to be used with other motor drive system topologies. The model has benefits for use by undergraduate student researchers, and professors when used as a teaching tool in undergraduate courses.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Dynamic simulation of induction motor drive using artificial intelligent controller

Cited by 12 publications

References 9 publications

Vector control of induction motor drive using optimized GA technique

Vector control of induction motor drive using optimized GA technique

A Simulink / MATLAB Model of a Three-Phase Sinusoidal PWM Inverter Driven Induction Motor

Adaptive controller with RBF neural network for induction motor drive

Contact Info

Product

Resources

About