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

Mon Jun 01 2020

Journal Name

Al-khwarizmi Engineering Journal

Prediction of Cutting Force in Turning Process by Using Artificial Neural Network

Marwa Qasim

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Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp

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(3)

Publication Date

Mon Nov 11 2019

Journal Name

Spe

Modeling Rate of Penetration using Artificial Intelligent System and Multiple Regression Analysis

neural network

strength

machine learning

ROP prediction

Upstream Oil & Gas

prediction

ANN model

input parameter

multiple regression analysis

artificial neural network

Farqad

Hassan

Ethar

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Abstract<p>Over the years, the prediction of penetration rate (ROP) has played a key rule for drilling engineers due it is effect on the optimization of various parameters that related to substantial cost saving. Many researchers have continually worked to optimize penetration rate. A major issue with most published studies is that there is no simple model currently available to guarantee the ROP prediction.</p><p>The main objective of this study is to further improve ROP prediction using two predictive methods, multiple regression analysis (MRA) and artificial neural networks (ANNs). A field case in SE Iraq was conducted to predict the ROP from a large number of parame</p> ... Show More

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(8)

Publication Date

Wed Jun 05 2024

Journal Name

International Journal Of Engineering Pedagogy (ijep)

The Impact of Artificial Intelligence on Computational Thinking in Education at University

effect

artificial intelligence (AI)

ChatGPT

computational thinking

education

university

achievement

chemistry

computer science

students

Najwa

Ban

Linda Talib

Maysam Raad

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This study aims to reveal the role of one of the artificial intelligence (AI) techniques, “ChatGPT,” in improving the educational process by following it as a teaching method for the subject of automatic analysis for students of the Chemistry Department and the subject of computer security for students of the Computer Science Department, from the fourth stage at the College of Education for Pure Science (Ibn Al-Haitham), and its impact on their computational thinking to have a good educational environment. The experimental approach was used, and the research samples were chosen intentionally by the research community. Research tools were prepared, which included a scale for CT that included 12 items and the achievement test in b

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(16)

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Publication Date

Thu Oct 31 2024

Journal Name

Iraqi Geological Journal

Artificial Neural Network Application to Permeability Prediction from Nuclear Magnetic Resonance Log

NMR Permeability

Permeability prediction

Artificial Neural Networks

Mustafa

Ghanim M.

Ahmed

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Reservoir permeability plays a crucial role in characterizing reservoirs and predicting the present and future production of hydrocarbon reservoirs. Data logging is a good tool for assessing the entire oil well section's continuous permeability curve. Nuclear magnetic resonance logging measurements are minimally influenced by lithology and offer significant benefits in interpreting permeability. The Schlumberger-Doll-Research model utilizes nuclear magnetic resonance logging, which accurately estimates permeability values. The approach of this investigation is to apply artificial neural networks and core data to predict permeability in wells without a nuclear magnetic resonance log. The Schlumberger-Doll-Research permeability is use

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(3)

(2)

Publication Date

Sun Oct 19 2025

Journal Name

Lecture Notes In Networks And Systems

The Impact of Artificial Intelligence on English Language Learning Challenges and Opportunities

English language · Modern learning · Artificial intelligence · Digital divide · Thematic examination

Qutaiba Mohanad Mhaidi

Ali Hussein

Khalil Ismael

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Publication Date

Sat Jan 01 2005

Journal Name

Journal Of The College Of Languages (jcl)

The Vogue of the Restoration Comedy of Manners and George Etherage's The Man of Mode; or, Sir Fopling Flutter

Mohammed Baqir

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The Vogue of the Restoration Comedy of Manners and George Etherage's The Man of Mode; or, Sir Fopling Flutter

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Publication Date

Tue Jan 01 2019

Journal Name

Ieee Access

Sensing Performance of Modified Single Mode Optical Fiber Coated With Nanomaterials-Based Ammonia Sensors Operated in the C-Band

Husam Abduldaem

Norizah Abdul

Muhammad Zamharir

Muhammad Hafiz

Siti Barirah Ahmad

Mohd Adzir

Mohd Hanif

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(16)

(20)

Publication Date

Sun Jan 01 2017

Journal Name

Aip Conference Proceedings

Macro controlling of copper oxide deposition processes and spray mode by using home-made fully computerized spray pyrolysis system

Mohammed Sh.

Bahaa T.

Omer Sh.

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(6)

(3)

Publication Date

Mon Jul 31 2017

Journal Name

Journal Of Engineering

Development of an On-Line Self-Tuning FPGA-PID-PWM Control Algorithm Design for DC-DC Buck Converter in Mobile Applications

Buck Converter

Digital PID Controller

On-Line

Self-Tuning

Bees-PSO Algorithm

FPGA

Ahmed Sabah

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Publication Date

Sun Jun 03 2012

Journal Name

Baghdad Science Journal

Study of effect of non-central obscuration to obscured circular aperture on third order spherical aberration

Adnan falih

Munther bakir

Talib abdul rudha abdul

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In this research we have been studied the 3rd order spherical aberration for an optical system consisted of obscured circular aperture with non central circular obscuration through the calculation of point spread function (P.S.F) in presence of the obscuration in the center and comparing the obtained results with that results of moving obscuration far away from the center, where the results showed significant improvement for(P.S.F) value. The study was done of different obscurities ratios in addition to the different 3rd order spherical aberration values (W40=0.25 ,0.5 ,0.75 ,1 ).

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