BELBIC and Its Industrial Applications: Towards Embedded Neuroemotional Control Codesign

Lucas, Caro

doi:10.1007/978-3-642-17384-4_17

Cited by 10 publications

(5 citation statements)

References 28 publications

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“…This controller is based on the limbic system of mammalian brain composed by a set of interconnected brain structures involving the amygdala, orbitofrontal cortex, sensory cortex and thalamus [1]. The emotional learning process modeled by the BELBIC algorithm has been successfully implemented on a wide-range of control engineering applications [2,3,4]. The BEL controller presents interesting features that can be exploited to design structural control systems for civil engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Development of a Brain Emotional Learning Based Controller for Application to Vibration Control of a Building Structure Under Seismic Excitation

Braz-César¹,

Gonçalves²,

Coelho³

et al. 2019

Proceedings of the 7th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Section: Introductionmentioning

confidence: 99%

Development of a Brain Emotional Learning Based Controller for Application to Vibration Control of a Building Structure Under Seismic Excitation

Braz-César¹,

Gonçalves²,

Coelho³

et al. 2019

Proceedings of the 7th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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“…Moreover, some other advantages of the BELBIC are investigated in other electric drives [33][34][35][36]. Utilization of the motors in position control or speed control applications means that they are not used necessarily in nominal speed or load conditions and it is very important that the controller can rapidly adapt to load changes or changes in motor characteristics [38]. The concurrent learning and control action is also very important for online utilization of the neural net in dynamic applications.…”

Section: Introductionmentioning

confidence: 99%

Automatic speed control of an asymmetrical six-phase induction motor using emotional controller (BELBIC)

Daryabeigi

Abjadi

Markadeh

2014

Journal of Intelligent &Amp; Fuzzy Systems

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An induction motor (IM) with two sets of three-phase windings in stator is called a six-phase IM (SPIM). SPIM has lower torque pulsation compared to a three-phase IM and using SPIM, the power rating of inverter legs can be reduced. This paper offers a high performance intelligent controller for speed automatic control of a SPIM. A brain emotional learning based intelligent controller (BELBIC) is proposed as the speed controller. This emotional controller has simple structure with high auto learning feature and it is independent of parameters variations. A direct vector control in rotor flux frame based on space vector pulse width modulation (SVPWM) is used to decouple flux and torque control of the SPIM like a conventional three-phase machine. In this work, the d-axis current and speed reference signals are accurately tracked. An overview of the BELBIC is provided. A comparison with the conventional PI controller and Sliding Mode Control (SLMC) points out the superiority of the proposed BELBIC in speed response and torque ripple. Experimental results indicate that the BELBIC achieves a good speed regulation with a less control effort and torque ripple.

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“…In this model, the reward signal is not clearly defined and this signal is vital for updating the learning weights of system. Lucas et al [7] explicitly determined the reward as reinforcer signal and proposed the BEL base controller (BELBIC) which has been successfully utilized in various control applications [8][9][10][11][12][13][14][15][16][17][18]. BELBIC is an action generation mechanism based on sensory inputs and emotional cues.…”

Section: Introductionmentioning

confidence: 99%

Supervised brain emotional learning

Lotfi¹,

Akbarzadeh-T²

2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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In this paper we propose the supervised version of neuro-based computational model of brain emotional learning (BEL). In mammalian brain, the limbic system processes emotional stimulus and consists of following two main components: amygdala and orbitofrontal cortex (OFC). Recently several models of BEL based on monotonic reinforcement learning in amygdala are proposed by researchers. Here, we introduce supervised version of BEL which can be learned by pattern-target examples. According to the experimental studies, where various comparisons are made between the proposed method, multilayer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS), the main feature of the presented method is fast training in prediction problems.

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BELBIC and Its Industrial Applications: Towards Embedded Neuroemotional Control Codesign

Cited by 10 publications

References 28 publications

Development of a Brain Emotional Learning Based Controller for Application to Vibration Control of a Building Structure Under Seismic Excitation

Development of a Brain Emotional Learning Based Controller for Application to Vibration Control of a Building Structure Under Seismic Excitation

Automatic speed control of an asymmetrical six-phase induction motor using emotional controller (BELBIC)

Supervised brain emotional learning

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