Position Control of Hybrid Stepper Motor Using Brain Emotional Controller

Khalilian, Mojtaba; Abedi, Ali; Zadeh, Adel Deris

doi:10.1016/j.egypro.2011.12.1200

Cited by 19 publications

(7 citation statements)

References 11 publications

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“…Thalamus receives the sensory input (SI) and preprocesses the input signals. Processed input signals are delivered to the amygdala and sensory cortex 29‐34 . Amygdala and orbitofrontal cortex are used to compute the results by using the emotional signals (ES).…”

Section: Control Designmentioning

confidence: 99%

Intelligent robust pitch control of wind turbine using brain emotional learning

Cao

Yazdani

Mahmoudi

2021

Int Trans Electr Energ Syst

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Summary This article proposes an implementation of the brain emotional learning‐based intelligent controller (BELBIC) for high‐precision and robust pitch control of a 5‐MW wind turbine. The proposed model‐free controller is a biologically inspired method emulating the learning in the mammalian's limbic system and it is independent of the model dynamics and variations that might occur in a system. The auto‐learning capability of the BELBIC allows accommodating the nonlinearities associated with the wind turbine model and provides a reasonable degree of disturbance enabling precise and robust tracking of the pitch angle, even under unforeseen wind conditions. To investigate the trajectory tracking performance and robustness of the BELBIC in various unpredictable wind conditions, multiple uncertain wind speed conditions including gust and random wind, are simulated in MATLAB/Simulink. The results of simulations are compared with two benchmark control methods, fuzzy‐proportional‐integral‐derivative and gain‐scheduling proportional‐integral. The simulation results clearly indicate that the BELBIC serves better performance and robustness while guaranteeing quick and precise pitch angle response as well as its ability in dealing with nonlinearity and unforeseen wind conditions in comparison to the other two benchmark control methods.

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Section: Control Designmentioning

confidence: 99%

Intelligent robust pitch control of wind turbine using brain emotional learning

Cao

Yazdani

Mahmoudi

2021

Int Trans Electr Energ Syst

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“…Babaie et al [24] formulated the input reward for multi agent optimization problems. Also Abdi et al [25] and khalilian et al [26] applied the reward signal in the same form and formed the formulization of other equations accordingly.…”

Section: Introductionmentioning

confidence: 99%

“…The nature and the description of the relationship between the four main components, consisting of Amygdala, Thalamus, Sensory Cortex and OFC, is common among all the modified versions [24][25][26] of AMG-OFC. What differs from one model to another is how they formulate the reward signal in the learning process.…”

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)

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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.

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“…The ball screws, used in this system, provide 0.5 cm movement for 360 • of rotation. That means for 0.0025 cm slide movement can be possible for each step (200 steps are required for 360 • rotation) of the stepper motor [28] shaft. The TB6560 [49] is used for the driving circuit of stepper motors.…”

Section: Motivationmentioning

confidence: 99%

Automatic System for Plasmodium Species Identification from Microscopic Images of Blood-Smear Samples

Ghosh¹,

Bhattacharjee

Nasipuri

2017

J Healthc Inform Res

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Malaria spreads rapidly in a particular time of the year, and it becomes impossible to arrange sufficient number of pathologists and physician at that time, especially in remote places of the developing nations. Thus, low-cost pathological equipment, which can automatically identify and classify the type of malarial parasites from the microscopic images of blood samples, will be of great help for diagnosis and treatment of malaria. The proposed system detects malarial parasites from the microscopic images of blood samples and also categorizes them based on shape, size and texture. To capture different field images of the same sample, a fully automatic slide movement system is devised to control the slide movement under the "objective lens" of the microscope. The sensitivity and specificity of the system are found to be 91.42 and 87.36% respectively. The system has the potential to a powerful supporting tool for telemedicine with very simple operational instructions.

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Position Control of Hybrid Stepper Motor Using Brain Emotional Controller

Cited by 19 publications

References 11 publications

Intelligent robust pitch control of wind turbine using brain emotional learning

Intelligent robust pitch control of wind turbine using brain emotional learning

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

Automatic System for Plasmodium Species Identification from Microscopic Images of Blood-Smear Samples

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