Emotional controller (BELBIC) for electric drives &amp;#x2014; A review

Daryabeigi, E.; Markadeh, Gholamreza Arab; Lucas, Caro

doi:10.1109/iecon.2010.5674934

Cited by 33 publications

(18 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method has successfully been applied to induction motors with conventional PI speed controller [6]. A new type of artificial intelligent controller based on medial brain model and emotional processes (BELBIC) is also used in this work to enhance the control system performance by quick auto learning and proper tracking of the reference signal adaptively, independent from system parameters [7,8,9]. Simulation results are used to evaluate the effectiveness and the partial superiority of the proposed control system over VC and DTC.…”

Section: Introductionmentioning

confidence: 96%

Combined vector and direct torque control methods for IPM motor drives using emotional controller (BELBIC)

Vaez‐Zadeh

Daryabeigi

2011

2011 2nd Power Electronics, Drive Systems and Technologies Conference

Self Cite

View full text Add to dashboard Cite

This paper presents a modern control system for three phase high performance interior permanent magnet motor drives. The control system combines the advantages of vector control and direct torque control and overcomes some of the implementation drawbacks of either of the methods. The system uses a current control loop in connection with a switching table. A brain emotional learning based intelligent controller (BELBIC) is employed in the system which provides excellent performance under different operating conditions. BELBIC is based on the emotion processing mechanism in human's brain providing actions based on sensory inputs and emotional cues. A comprehensive evaluation of an IPM motor performance under the proposed control system proves the effectiveness of the system and its partial superiority over either vector control or direct torque control despite its relatively simple structure.

show abstract

Section: Introductionmentioning

confidence: 96%

Combined vector and direct torque control methods for IPM motor drives using emotional controller (BELBIC)

Vaez‐Zadeh

Daryabeigi

2011

2011 2nd Power Electronics, Drive Systems and Technologies Conference

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the model, the reward signal was not clearly defined and this signal is vital for updating the weights of subsystems. Lucas et al [10] explicitly determined the reward signal and proposed the Brain Emotional Learning (BEL) based Intelligent Controller (BELBIC) which has been successfully utilized in various control applications [11][12][13][14][15][16][17][18][19][20][21] and prediction problems [22][23][24]. Babaie et al [24] formulated the input reward for multi agent optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Emotional Brain-Inspired Adaptive Fuzzy Decayed Learning for online prediction problems

Lotfi

Akbarzadeh-T

2013

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

View full text Add to dashboard Cite

In this paper, we propose a Fuzzy Adaptive BrainInspired Emotional Decayed Learning named Fuzzy ADBEL. Fuzzy ADBEL is a computational model that models the forgetting process and inhibitory mechanism of the emotional brain. In the model, the fuzzy decay rate simulates the forgetting process, and the stimulus and learning weights are considered as fuzzy variables trained by fuzzy learning rules. The final output of the model is evaluated by a fuzzy decision making layer that simulates the inhibitory mechanism. The proposed Fuzzy ADBEL is utilized to predict the Kp, AE and Dst indices showing opposite behaviors and characterizing the chaotic activity of the earth's magnetosphere. Experimental results show that fuzzy approaches including Fuzzy ADBEL and ANFIS (Adaptive NeuroFuzzy Inference System) reaches steady state faster than non-fuzzy approaches, ADBEL and MLP (Multilayer Perceptron). Hence, we hope the proposed model can be used in real time chaotic time series prediction.

show abstract

“…Also the controller was implemented for some other electric drives successfully [8]. Based on the above mentioned evidence of the emotional control approaches in computer and control engineering, it can be concluded that the application of emotion in systems could by its simple and unique control design, overcome the problems of non-linear system, manufacturing imperfections, acceptably.…”

mentioning

confidence: 99%

Progress in Artificial Intelligence Techniques: from Brain to Emotion

2011

View full text Add to dashboard Cite

Artificial Intelligence (AI) techniques, e.g. expert system (ES), fuzzy logic (FL), artificial neural network (ANN), genetic algorithm (GA), particle swarm optimization (PSO) and biologically inspired (BI) have recently been applied widely in power electronics and motor drives. The aim of the AI is to model natural or human intelligence in a computer to think smartly like a human [1], [2].The next form of AI is the embedded AI controller system which has ability in learning, self-organizing, and self-adapting. Had been able to solve common and complex control problem, the AI technique in computational intelligence applied in wide application of industrial process control, robotics, automated planning and scheduling, games, hypermedia, image processing, patterns recognition (handwriting, speech, and facial), logistics, data mining, medicine and healthcare, space and diagnostic technology [1].Each AI method has its own uniqueness and characteristics. The ES and FL techniques tend to mimic the behavioural nature of the human brain and base on the rules; the NN is more generic in nature and tends to pattern directly to the biological NN. The GAs and the evolutionary computation techniques are based on principles of genetics. Basically, GA solves optimization problems through a searching process to find the fittest as a survivor for the best solutions. Among all the sub branches of AI, the NN and FL appear to be most uses for highperformance motor drives. However there are many other feed forward and recurrent NN topologies which require systematic exploration for their applications [3]. In advance, the powerful intelligent control and estimation techniques are dynamically developed through hybrid AI systems such as neuro-fuzzy, neuro-genetic, and neuro-fuzzy-genetic systems. The PSO as a population-based stochastic optimization technique has been developed since 1995 and inspired by social behavior of bird flocking or fish schooling [4]. PSO as evolutionary computation techniques shares many similarities with GA, but PSO offer easy implementation with few adjustable gains. PSO is considered as a fast-developing research topic and applied successfully in optimization function, artificial neural network training, and fuzzy system control [5]. The biological dispositions of animals and mimics bio mechanisms have inspired the BI system. Since 1990s, the NN technology has become one of most attractive topics for the scientific community, and growth rapidly in different and various applications . Basically, the brain emotional learning (BEL) is divided into two parts: amygdala and orbitofrontal cortex. The amygdala is a part of the brain that must be responsible for processing emotions primarly and correspond with the orbitofrontal cortex, thalamus, and sensory input cortex in the network model. The orbitofrontal cortex receives inputs from the cortical areas and the amygdala and responsibles for the reaction to change the contingency of emotions. Error of the expected reward or punishment and the loss of learning in the ...

show abstract

Emotional controller (BELBIC) for electric drives — A review

Cited by 33 publications

References 27 publications

Combined vector and direct torque control methods for IPM motor drives using emotional controller (BELBIC)

Combined vector and direct torque control methods for IPM motor drives using emotional controller (BELBIC)

Emotional Brain-Inspired Adaptive Fuzzy Decayed Learning for online prediction problems

Progress in Artificial Intelligence Techniques: from Brain to Emotion

Contact Info

Product

Resources

About