Speed and Flux Control of Induction Motors Using Emotional Intelligent Controller

Markadeh, Gholamreza Arab; Daryabeigi, E.; Lucas, Caro; Rahman, M.A.

doi:10.1109/tia.2011.2125710

Cited by 61 publications

(41 citation statements)

References 32 publications

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“…One of the most influential works in artificial fear conditioning is the brain emotional learning (BEL) model, proposed by Morén and Balkenius [6] and widely used in a variety of engineering and industrial applications [7], [8], [9], [10]. Their model consists of interconnected modules of artificial neural networks (ANNs) that simulate the role of neural circuitries involved in fear learning.…”

Section: Related Workmentioning

confidence: 99%

Improving the predictive performance of SAFEL: A Situation-Aware FEar Learning model

Rizzi

Johnson

Vargas

2016

2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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Abstract-In this paper, we optimize the predictive performance of a Situation-Aware FEar Learning model (SAFEL) by investigating the relationship between its parameters. SAFEL is a hybrid computational model based on the fear-learning system of the brain, which was developed to provide robots with the capability to predict threatening or undesirable situations based on temporal context. The main aim of this work is to improve SAFEL's emotional response. An emotional response coherent with environmental changes is essential not only for self-preservation and adaptation purposes, but also for improving the believability and interaction skills of companion robots. Experiments with a NAO humanoid robot show that adjusting the ratio between two parameters of SAFEL can significantly increase the predictive performance and reduce parameter settings.

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Section: Related Workmentioning

confidence: 99%

Improving the predictive performance of SAFEL: A Situation-Aware FEar Learning model

Rizzi

Johnson

Vargas

2016

2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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“…The emotional control system receives the error signals between the command speed and the actual motor speed as part of inputs according to Equations (12) to (16) The hysteresis current controller is used to implement the vector control algorithm. The current controller compares the command currents with the corresponding actual motor currents and generates the logic signals to command the inverter switches [18].…”

Section: Ipmsm Drive Control Systemmentioning

confidence: 99%

“…In [17], the speed of a highly non-linear IPMSM was controlled with a conventional BELBIC. Flux and speed control of the induction motor was implemented in [18]. Controller operation was investigated in [19] by the variations in motor parameters, such as the rotor resistor.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation of Brain Emotional Learning Developed for Digital Signal Processor-Based Interior Permanent Magnet Synchronous Motor Drive Systems

Sadeghi¹,

Daryabeigi²

2014

Journal of Power Electronics

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In this study, a brain emotional learning-based intelligent controller (BELBIC) is developed for the speed control of an interior permanent magnet synchronous motor (IPMSM). A novel and simple model of the IPMSM drive structure is established with the intelligent control system, which controls motor speed accurately without the use of any conventional PI controllers and is independent of motor parameters. This study is conducted in both real time and simulation with a new control plant for a laboratory 3 ph, 3.8 Nm IPMSM digital signal processor (DSP)-based drive system. This DSP-based drive system is then compared with conventional BELBIC and an optimized conventional PI controller. Results show that the proposed method performs better than the other controllers and exhibits excellent control characteristics, such as fast response, simple implementation, and robustness with respect to disturbances and manufacturing imperfections.

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“…An evolutionary algorithm (EA) based fuzzy -PID type adaptable controller has been introduced for a PMSM drive in real time can be found in [5][6]. Brain-emotional-learningbased intelligent controller has been adopted and implemented for induction motor drives in a real-time platform in [7], the working of this controller is mainly based on the input data of sensors and emotional cues. A single neuron artificial neural network has been proposed to interior permanent magnet synchronous motor (IPMSM) [8] it doesn't require offline training and wide knowledge of the motor behavior of various drive systems.…”

Section: Introductionmentioning

confidence: 99%

Real- time implementation of parallel type fuzzy- PID controller for effective control of hybrid Pole self bearing Switched reluctance motor

Naidu¹,

Salma²

2018

IJET

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This paper presents a hybrid intelligent and design in a real time for the rapid prototyping of a robust fuzzy controller along with conventional Proportional-Integral and Derivative (PID) controller that allows quick insight of these integrated designs. The design procedure of the parallel fuzzy PID and its combination with the traditional PID in a universal control scheme are extended. The structural design of the parallel fuzzy PID controller is composed of three fuzzy sub controllers which are connected in parallel. These parallel sub controllers are assembled to get the proposed parallel fuzzy type PID controller. The hybrid fuzzy PID gains are expressed in the error domain. Hence, the structural design presents an alternative to control schemes employed so far. This hybrid intelligent controller is formulated and executed in real world hardware for position as well as speed control of a Hybrid Pole Self Bearing Switched Reluctance Motor (HPSBSRM) drive system. The design of the parallel fuzzy PID controller, implementation and finally analysis all are carried out using MATLAB/Simulink environment. Software results concluded that the novel hybrid intelligent parallel fuzzy PID controller generates better control action when compared to traditional PID controller, predominantly in system nonlinearities and in external load variations.

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Speed and Flux Control of Induction Motors Using Emotional Intelligent Controller

Cited by 61 publications

References 32 publications

Improving the predictive performance of SAFEL: A Situation-Aware FEar Learning model

Improving the predictive performance of SAFEL: A Situation-Aware FEar Learning model

Real-Time Implementation of Brain Emotional Learning Developed for Digital Signal Processor-Based Interior Permanent Magnet Synchronous Motor Drive Systems

Real- time implementation of parallel type fuzzy- PID controller for effective control of hybrid Pole self bearing Switched reluctance motor

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