Implementation of Emotional Controller for Interior Permanent-Magnet Synchronous Motor Drive

Rahman, M.A.; Milasi, Rasoul M.; Lucas, Caro; Araabi, Babak Nadjar; Radwan, T.S.

doi:10.1109/tia.2008.2002206

Cited by 110 publications

(45 citation statements)

References 9 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 mathematical model of an IPMSM drive in a synchronously rotating rotor d-q reference frame can be described by the following equations [17]: …”

Section: Mathematical Model Of Ipmsmmentioning

confidence: 99%

“…A rotor squirrel cage winding generates the required starting torque in the IPMSM drive fed from the mains. The starting process of the IPMSM drive can be considered a s u p e rp o s i t i o n o f t w o o p e r a t i n g m o d e s , n a m e l y, unsymmetrical asynchronous motor mode and permanent magnet-excited synchronous generator mode [17]. Therefore, the effects of the shorted rotor windings must be considered when attempting to implement synchronization.…”

Section: Mathematical Model Of Ipmsmmentioning

confidence: 99%

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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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“…EL control is a promising control strategy for AC voltage and circulating current controls because of its fast dynamic response and robustness. The learning mechanisms of the amygdala and orbitofrontal cortex shape the association between output and input [19], [20], as well as enable the EL controller to be adaptive. The organization of this article is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Design of Emotional Learning Controllers for AC Voltage and Circulating Current of Wind-Farm-Side Modular Multilevel Converters

Li¹,

Liao²,

Liu³

et al. 2016

Journal of Power Electronics

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The introduction of a high-voltage direct-current (HVDC) system based on a modular multilevel converter (MMC) for wind farm integration has stimulated studies on methods to control this type of converter. This research article focuses on the control of the AC voltage and circulating current for a wind-farm-side MMC (WFS-MMC). After theoretical analysis, emotional learning (EL) controllers are proposed for the controls. The EL controllers are derived from the learning mechanisms of the amygdala and orbitofrontal cortex which make the WFS-MMC insensitive to variance in system parameters, power change, and fault in the grid. The d-axis and q-axis currents are respectively considered for the d-axis and q-axis voltage controls to improve the performance of AC voltage control. The practicability of the proposed control is verified under various conditions with a point-to-point MMC-HVDC system. Simulation results show that the proposed method is superior to the traditional proportional-integral controller.

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Implementation of Emotional Controller for Interior Permanent-Magnet Synchronous Motor Drive

Cited by 110 publications

References 9 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

Design of Emotional Learning Controllers for AC Voltage and Circulating Current of Wind-Farm-Side Modular Multilevel Converters

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