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Integral Sliding Mode Control Design for Electronic Throttle Valve System
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Abstract

 One of the major components in an automobile engine is the throttle valve part. It is used to keep up with emissions and fuel efficiency low. Design a control system to the throttle valve is newly common requirement trend in automotive technology. The non-smoothness nonlinearity in throttle valve model are due to the friction model and the nonlinear spring, the uncertainty in system parameters and non-satisfying the matching condition are the main obstacles when designing a throttle plate controller.

In this work, the theory of the Integral Sliding Mode Control (ISMC) is utilized to design a robust controller for the Electronic Throttle Valve (ETV) system. From the first instant, the electronic throttle valve dynamics is represented by the nominal system model, this model is not affected by system parameters uncertainty and the non-smooth nonlinearities. This is a consequence of applying the integral sliding mode control. The ISMC consists of two part; the first is the nominal control which is used to control the nominal system, while the second is a discontinuous part which is used to eliminate the effects of the parameters uncertainty and the non-smooth nonlinearities from system model. These features for the ISMC are proved mathematically and demonstrated numerically via seven numerical simulations and for different desired trajectories.  The simulation results clarify that for different system parameters, the ETV behaves as a nominal system. This enables to freely and precisely select the system response characteristics and the time required for the throttle angle to reach the desired value. Moreover the ability to deal with the chattering problem is demonstrated through the worked  simulation tests, where the chattering is eliminated via approximating the signum function by arc tan function.

 Keywords: Electronic throttle valve, Nonlinear spring model, Integral sliding mode control, Non-smooth model.

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Publication Date
Sun May 14 2023
Journal Name
Agrosystems, Geosciences & Environment
Cover crop and biofuel crop effects on hydraulic properties for claypan soils
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Perennial biofuel and cover crops systems are important for enhancing soil health and can provide numerous soil, agricultural, and environmental benefits. The study objective was to investigate the effects of cover crops and biofuel crops on soil hydraulic properties relative to traditional management for claypan soils. The study site included selected management practices: cover crop (CC) and no cover crop (NC) with corn/soybean rotation, switchgrass (SW), and miscanthus (MI). The CC mixture consisted of cereal rye, hairy vetch, and Austrian winter pea. The research site was located at Bradford Research Center in Missouri, USA, and was implemented on a Mexico silt loam. Intact soil cores (76‐mm diam. by 76‐mm long) were taken from the

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Publication Date
Thu Nov 17 2022
Journal Name
Journal Of Information And Optimization Sciences
Hybrid deep learning model for Arabic text classification based on mutual information
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Publication Date
Sun Jan 14 2018
Journal Name
Journal Of Engineering
Regression Modeling of EDM Process for AISI D2 Tool Steel with RSM
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In this paper, Response Surface Method (RSM) is utilized to carry out an investigation of the impact of input parameters: electrode type (E.T.) [Gr, Cu and CuW], pulse duration of current (Ip), pulse duration on time (Ton), and pulse duration off time (Toff) on the surface finish in EDM operation. To approximate and concentrate the suggested second- order regression model is generally accepted for Surface Roughness Ra, a Central Composite Design (CCD) is utilized for evaluating the model constant coefficients of the input parameters on Surface Roughness (Ra). Examinations were performed on AISI D2 tool steel. The important coefficients are gotten by achieving successfully an Analysis of V

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of The Mechanical Behavior Of Materials
Evaluation of a fire safety risk prediction model for an existing building
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Abstract<p>Fire is one of the most critical risks devastating to human life and property. Therefore, humans make different efforts to deal with fire hazards. Many techniques have been developed to assess fire safety risks. One of these methods is to predict the outbreak of a fire in buildings, and although it is hard to predict when a fire will start, it is critical to do so to safeguard human life and property. This research deals with evaluating the safety risks of the existing building in the city of Samawah/Iraq and determining the appropriateness of these buildings in terms of safety from fire hazards. Twelve parameters are certified based on the National Fire Protection Association (NFPA20</p> ... Show More
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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction
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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
IFFT-Based Microwave Non-Destructive Testing for Delamination Detection and Thickness Estimation
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Publication Date
Sun Jul 01 2012
Journal Name
Applied Soft Computing
A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks
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Publication Date
Wed Mar 05 2025
Journal Name
Lecture Notes In Networks And Systems
Leveraging AI for Disaster Management: A Comprehensive Review of Applications and Challenges
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Publication Date
Sun Oct 20 2024
Journal Name
Chemical Papers
Response surface methodology for optimizing crude oil desalting unit performance in iraq
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Publication Date
Sun Mar 30 2025
Journal Name
Studia Universitatis Babeș-bolyai Chemia
GREEN SPECTROPHOTOMETRIC METHOD FOR CONCURRENT ESTIMATION OF PIROXICAM AND MEFENAMIC ACID MIXTURE
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The purpose of this work is to concurrently estimate the UVvisible spectra of binary combinations of piroxicam and mefenamic acid using the chemometric approach. To create the model, spectral data from 73 samples (with wavelengths between 200 and 400 nm) were employed. A two-layer artificial neural network model was created, with two neurons in the output layer and fourteen neurons in the hidden layer. The model was trained to simulate the concentrations and spectra of piroxicam and mefenamic acid. For piroxicam and mefenamic acid, respectively, the Levenberg-Marquardt algorithm with feed-forward back-propagation learning produced root mean square errors of prediction of 0.1679 μg/mL and 0.1154 μg/mL, with coefficients of determination of

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