A self-tuning brain emotional learning–based intelligent controller for trajectory tracking of electrohydraulic actuator

Nahian, Syed Abu; Dinh, Truong Quang; Ahn, Kyoung Kwan

doi:10.1177/0959651814530275

Cited by 11 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The junction of BEL with control systems design has led to the concept known as "brain emotional learning-based intelligent control" (BELBIC). Further details regarding the operational details of this control method can be found at [7][8][9][10].…”

Section: The Belbic Controllermentioning

confidence: 99%

BELBIC Based Step-Down Controller Design Using PSO

Coelho

Braz-César

Gonçalves

2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

show abstract

Section: The Belbic Controllermentioning

confidence: 99%

BELBIC Based Step-Down Controller Design Using PSO

Coelho

Braz-César

Gonçalves

2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…To have a close-loop model, sensory inputs and a reward signal must have feedback signals from the system output. Thus, the inputs of BELBIC can generally be expressed by Equations (11) and (12) (Nahian et al, 2014):…”

Section: Proposed Belbic-based Admission Controller For Cloud Servermentioning

confidence: 99%

An FPGA implemented brain emotional learning intelligent admission controller for SaaS cloud servers

Iranpour

Sharifian

2016

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

Dynamic resource allocation in a cloud environment has become possible using virtualization technologies in cloud computing. One of the applications of these technologies is offering various applications by Software-as-a-Service (SaaS) infrastructures. Unfortunately, due to request rate increments in cloud rush hours, the related server cannot serve all the requests according to the service level agreement. Hence, the cloud provider’s quality of service will decrease. Thus a mechanism is required to control the admission rate of requests for cloud servers. In this study, an intelligent controller is designed and implemented on a field-programmable gate array (FPGA) in order to control the admission rate of requests for a SaaS server in the cloud. The controller is based on a brain emotional learning-based intelligent controller (BELBIC). First, an analytical model of a server is proposed and simulated, which shows the behavioural characteristics of a real server. Next, the BELBIC is designed to control the admission rate of the server. Finally, the system is implemented on FPGA hardware and simulated by a synthetic cloud workload in a hardware-in-the-loop manner. In order to compare the performance of the BELBIC, an adaptive neuro-fuzzy inference system (ANFIS) controller in addition to the popular PID controller is provided. The controllers’ efficiencies are compared in terms of server utilization, admission rate, drop rate of requests and the agility of the controllers. The results proved that the BELBIC offers faster rise time compared with the PID controller, which leads to better cloud utilization and a smaller number of dropped requests.

show abstract

“…And the reward function can be selected as a formation of proportional-integral-derivative control form [40]. In this study, it was set as 100 4 0. for Amygdala and OFC respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Recent conducted researches [40,41] In short, there are four main components in the BEL system. According to study [29], there are some Sensory inputs Si entering into Thalamus (Fig.…”

Section: Belbic Controller Designmentioning

confidence: 99%