Second Order Sliding Mode Controller Design for Pneumatic Artificial Muscle

Ammar Al-Jodah; Laith Khames

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Publication Date

Sun Jan 14 2018

Journal Name

Journal Of Engineering

Volume

24

Issue Number

1

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Second Order Sliding Mode Controller Design for Pneumatic Artificial Muscle

PAM

Sliding Mode Controller.

Ammar Al-Jodah

Laith Khames

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In this paper, first and second order sliding mode controllers are designed for a single link robotic arm actuated by two Pneumatic Artificial Muscles (PAMs). A new mathematical model for the arm has been developed based on the model of large scale pneumatic muscle actuator model. Uncertainty in parameters has been presented and tested for the two controllers. The simulation results of the second-order sliding mode controller proves to have a low tracking error and chattering effect as compared to the first order one. The verification has been done by using MATLAB and Simulink software.

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Publication Date

Sat Apr 01 2023

Journal Name

Chemical Methodologies

A Novel Design for Gas Sensor of Zinc Oxide Nanostructure Prepared by Hydrothermal Annealing Technique

A.E.

Fuad Tariq

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(13)

Publication Date

Sat Mar 01 2008

Journal Name

Al-khwarizmi Engineering Journal

Minimizing error in robot arm based on design optimization for high stiffness to weight ratio

Ahmed Abdul Hussain

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In this work the effect of choosing tri-circular tube section had been addressed to minimize the end effector’s error, a comparison had been made between the tri-tube section and the traditional square cross section for a robot arm, the study shows that for the same weight of square section and tri-tube section the error may be reduced by about 33%.

A program had been built up by the use of MathCAD software to calculate the minimum weight of a square section robot arm that could with stand a given pay load and gives a minimum deflection. The second part of the program makes an optimization process for the dimension of the cross section and gives the dimensions of the tri-circular tube cross section that have the same weight of

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Publication Date

Sat Oct 01 2022

Journal Name

Structures

Behaviour and design of the ‘lockbolt’ demountable shear connector for sustainable steel-concrete composite structures

Demountable shear connector

Finite element model

Grout-filled tube

Lockbolt

Pushout test

Relative slip

Shear strength

Jun

Ahmed S. H.

George

Sihao

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In order to promote sustainable steel-concrete composite structures, special shear connectors that can facilitate deconstruction are needed. A lockbolt demountable shear connector (LB-DSC), including a grout-filled steel tube embedded in the concrete slab and fastened to a geometrically compatible partial-thread bolt, which is bolted on the steel section's top flange of a composite beam, was proposed. The main drawback of previous similar demountable bolts is the sudden slip of the bolt inside its hole. This bolt has a locked conical seat lug that is secured inside a predrilled compatible counter-sunk hole in the steel section's flange to provide a non-slip bolt-flange connection. Deconstruction is achieved by demounting the tube from the t

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(35)

Publication Date

Fri Aug 16 2024

Journal Name

International Journal Of Mathematics And Computer Science

Artificial Intelligence Techniques to Identify Individuals through Palm Image Recognition

Palm print

Random Forest

Support Vector Machine

k-NN

SqueezNet

Machine Learning

Wisal Hashim

Shahlaa

Samera Shams

Aseel

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Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le

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(3)

Publication Date

Sun May 01 2022

Journal Name

International Journal Of Multiphase Flow

Application of artificial neural network to predict slug liquid holdup

Ghassan H.

F.S.

Faleh

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Publication Date

Mon Jan 01 2024

Journal Name

International Journal Of Mathematics And Computer Science

Artificial Intelligence Techniques to Identify Individuals through Palm Image Recognition

Wisal

Shahlaa

Samera

Aseel

...Show More Authors

Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le

(6)

(3)

Publication Date

Tue Jan 01 2013

Journal Name

Thesis

User Authentication Based on Keystroke Dynamics Using Artificial Neural Networks

Mays M. Hoobi

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Computer systems and networks are being used in almost every aspect of our daily life, the security threats to computers and networks have increased significantly. Usually, password-based user authentication is used to authenticate the legitimate user. However, this method has many gaps such as password sharing, brute force attack, dictionary attack and guessing. Keystroke dynamics is one of the famous and inexpensive behavioral biometric technologies, which authenticate a user based on the analysis of his/her typing rhythm. In this way, intrusion becomes more difficult because the password as well as the typing speed must match with the correct keystroke patterns. This thesis considers static keystroke dynamics as a transparent layer of t

Publication Date

Wed Nov 01 2023

Journal Name

Al-rafidain Journal Of Medical Sciences ( Issn 2789-3219 )

After Introducing Artificial Intelligence, can Pharmacists Still Find a Job?

Ehab

Samer

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(1)

Publication Date

Sat May 31 2025

Journal Name

3rd International Scientific Conference For Human And Social Studies And Epistemological Challenges

Frankenstein Complex in Daniel H. Wilson's Robopocalypse ( ): Artificial Intelligence Conspiracies

With the advent of technology

the fear of losing control has consistently accompanied humanity. While humans have discovered

invented

and created many remarkable things

they have also been haunted by the possibility of their creations turning against them. This fear

often referred to as the Frankenstein Complex

derives its name from Mary Shelley's Frankenstein (􀀔􀀛􀀔􀀛)

in which Dr. Frankenstein's creation ultimately destroys its maker. The Frankenstein Complex embodies the subconscious fear of artificial intelligence (AI) surpassing and potentially dominating humankind. This apprehension has inspired numerous literary works

including Daniel H. Wilson’s novels. The present study examines the widespread fear of AI

which has fueled conspiracy theories in literature. These theories have not only amplified public anxiety but also hindered understanding among those with limited knowledge of AI and robotics. Dr. Wilson

a robotics expert

uses his imagination as a driving force for both his writing and his inventions. The research focuses on incidents leading to the rise of robots

the concept of the New War

how it was fought

and how it concluded. By comparing

contrasting

and analyzing literary elements and devices with prominent conspiracy theories

the study highlights the interplay between fiction and scientific development. The researchers conclude that while many conspiracy theories contain elements of truth

they often inspire scientists to explore them further. Additionally

some conspiracy theories warrant serious consideration. The study recommends acknowledging the Frankenstein Complex while maintaining faith in the potential benefits of artificial intelligence.

Ansam

Mayada

Zahraa

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Publication Date

Tue Dec 01 2015

Journal Name

Journal Of Engineering

Modeling and Control of Fuel Cell Using Artificial Neural Networks

fuel cell

hydrogen fuel

renewable energy

artificial neural networks

PID

Particle swarm optimization

Hayder Sabah

Hayder

Eiman Ali Eh.

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This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

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