Extended state observer design for uncertainty estimation in electronic throttle valve system

Noor S. Mahmood; Amjad J. Humaidi; Raaed S. Al-Azzawi; Ammar Al-Jodah

doi:10.1556/1848.2023.00662

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International Review of Applied Sciences and Engineering

Volume/Issue:: Accepted Manuscript / Online First

Extended state observer design for uncertainty estimation in electronic throttle valve system

Authors:

Noor S. Mahmood

Noor S. Mahmood Control and Systems Engineering Department, University of Technology, Baghdad, 10066, Iraq

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cse.20.04@grad.uotechnology.edu.iq https://orcid.org/0009-0000-0933-0303

Amjad J. Humaidi

Amjad J. Humaidi Control and Systems Engineering Department, University of Technology, Baghdad, 10066, Iraq

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amjad.j.humaidi@uotechnology.edu.iq https://orcid.org/0000-0002-9071-1329

Raaed S. Al-Azzawi

Raaed S. Al-Azzawi Control and Systems Engineering Department, University of Technology, Baghdad, 10066, Iraq

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Raaed.s.alazzawi@uotechnology.edu.iq https://orcid.org/0000-0002-3963-8816

, and

Ammar Al-Jodah

Ammar Al-Jodah School of Physics, Maths, and Computing, The University of Western Australia, Perth, WA 6907, Australia

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ammar.al‐jodah@uwa.edu.au https://orcid.org/0000-0003-4536-1240

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Online Publication Date:: 10 Oct 2023

Publication Date:: 10 Oct 2023

Article Category:: Research Article

DOI:: https://doi.org/10.1556/1848.2023.00662

Keywords (English):: electronic throttle valve; Linear Extended State Observer; Nonlinear Extended State Observer; uncertainty of parameter

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Abstract

The Electronic Throttle Valve (ETV) is the core part of automotive engines which are recently used in control-by-wire cars. The estimation of its states and uncertainty is instructive for control applications. This study presents the design of Extended State Observer (ESO) for estimating the states and uncertainties of Electronic Throttle Valve (ETV). Two versions of ESOs have been proposed for estimation: Linear ESO (LESO) and Nonlinear ESO (NESO). The model of ETV is firstly developed and extended in state variable form such that the extended state stands for the uncertainty in system parameters. The design of both structures of ESOs are developed and a comparison study has been conducted to show the effectiveness of the proposed observers. Numerical simulation has been conducted to assess the performance of observers in estimating the states and uncertainties of ETV. The simulated results showed that both full order and reduced order models of ETV have the same transient characteristics. Moreover, the effectiveness of two versions of observers has been examined based on Root Mean Square of Error (RMSE) indicator. The results showed that the NESO has less estimation errors for both states and uncertainties than LESO.

Abstract

1 Introduction

The throttle valve part is one of the important parts in automobile engines. By changing the opening angle of the valve plate, the air–fuel ratio is adjusted during the combustion process. In conventional engines of cars, the driver controls the valve plate directly by connecting the valve plate to the accelerator pedal via a wire connection. In this classical technology, external and internal conditions like weather conditions, road conditions, fuel efficiency, emission performance of vehicles, fuel economy are not taken into consideration. This has an adverse effect on the whole efficiency of the engine and the accuracy of the car system; especially there are high complexities and nonlinearity in dynamics of the throttle valve due to variable stiffness and mechanical hysteresis. In recent technologies of the automotive industry, the ETV has appeared to solve the problems due to conventional throttle valve in the previous era. The key of using ETV is that its plate angle is controlled by an electronic unit. Instead of using direct connection between the valve plate and the acceleration pedal to control the air flow, the level of the gas pedal is firstly measured via a special sensor which gives feedback to control algorithm. The control algorithm is responsible for generating the required control signals to actuate the DC motor for moving the throttle plate at the required angle [1–3].

The throttle valve system is characterized by high and non-smooth nonlinearities such as gear backlash, stick–slip friction, and a nonlinear spring. This makes control design difficult and sophisticated. Moreover, another control difficulty arises due to variation in system parameters and the inexact modeling of these non-smooth nonlinearities. In addition, unmatched parameter uncertainties inherently exist, which can further complicate the design of controller. Therefore, robust controller is required to cope with all the above model problems [4–6].

In some control applications, it is not possible to measure all states of the systems. Therefore, state-feedback control techniques are difficult to apply unless another tool is used to estimate the unmeasurable states. This problem can be solved by using state observer, which makes the estimation of all states possible with only measuring the output of the system. The Luenberger observer (LO) was the conventional technique to estimate the states of linear systems. The LO could give quicker convergence of system states when its gain is set at high values. High gain of LO may lead to peaking phenomenon which results in amplification of estimation errors. This difficulty can be overcome by introducing other nonlinear high-gain observers, which can give quick convergence of estimation errors with high suppressing of peaking phenomenon. One effective and successive observer is the sliding mode-observer. This observer gives good robustness characteristics, but it suffers from some problems like anti-chattering, adaptability, and uncertainty estimation [7, 8]. To address these issues, Extended State Observer (ESO) is presented, which estimates the state vector, as well as the uncertainties in an integrated manner.

The ESO plays a vital role in feedback control design of nonlinear systems like active disturbance rejection control (ADRC). The ESO could not only estimate the unmeasured states in real time, but it can also estimate the total disturbance due to external disturbance, unknown coefficient of control, unmodeled system dynamics, or those parts which are hard to be described by the practitioner. In general, the ESO can deal with uncertainties which are either coming from external disturbance or coming from the system itself. However, the high gain nature of ESO is considered a challenge in conditions where the output measurement is contaminated by high-frequency and non-negligible noise [9–13].

In the literature, there are many researchers who have used different structures of observers to estimate the states of ETV. In [14], S. A. Al-Samarraie et al. used Sliding Mode Perturbation Estimator to control the angular position of plate in ETV. In [15], Li Y applied ESO to observe the opening angle of plate in ETV under intelligent double integral sliding mode controller. In [16], J. Xue et al. utilized ESO in control of ETV to compensate the total disturbances due to uncertainties of system parameters and nonlinearities of return spring and frictions. In [17], Y. Li et al. presented the design of ESO based on dynamic model of ETV to estimate the total disturbance and the opening angle change of throttle valve plate. The ESO is the main element of the controller based on double-loop integral sliding-mode control. In [18], B. Yang et al. used Luenberger-sliding mode observer (LSMO) to estimate the change of throttle valve opening. In addition, the total uncertainties, like gear backlash torque and external disturbance, are approximated based on fuzzy logic system. The LSMO is the essential part in output feedback control based on double loop integral sliding mode control. In [19], Zheng et al. have designed ESO to estimate the opening angle of throttle valve plate to satisfy control accuracy of plate angle. The designed ESO is used in output feedback control based on sliding mode control approach. In [20], R. Gzam et al. presented two versions of observers to estimate the state of valve angular position, to detect the faults in sensors and system, and to compensate for the external disturbances. The proposed observers are Luenberger Observer (LO) and Unknown Input Observer (UIO). The observers have supported the robustness of the proposed PID controller against unknown input disturbances and faults of sensors. In [21], T. Agbaje et al. have designed global finite-time observer to estimate the total disturbances and to estimate the derivatives of ET system output. Based on the designed observer, the proposed terminal SMC can achieve finite-time control of plate motion for ETV system.

In this study, ESO has been proposed to estimate the states of ETV systems including the lumped uncertainties of the system. This observer is different from observers addressed in the above literature. The estimated states include the angular position and velocity of the throttle plate. In addition, the observer also estimates the total uncertainties inherited in the ETV system due to external disturbance and nonlinearities of spring characteristics. Two versions of ESO have been considered; one version is based on linear structure of ESO and the other is based on Nonlinear structure of ESO. The contribution of this work can be summarized as follows:

To design the Linear Extended State Observer (LESO) and Nonlinear Extended State Observer (NESO) for estimating the states and uncertainties of ETV system.
To conduct a comparison study in performance between LESO and NESO.

The article is organized in four parts. The first part has addresses the mathematical modeling of ETV for both full and reduced order dynamic models. The second part presents the design of ESO based on reduced-order model of the ETV. The third section conducts numerical simulations to verify the effectiveness of both versions of ESO, represented by LESO and NESO. The fourth part highlights the concluded points obtained by the computer simulation.

2 Model of electronic throttle valve system

As shown in Fig. 1, the ETV consists of DC motor, motor pinion gear, an intermediate gear, a sector gear, a valve plate, and a nonlinear spring.

The mathematical model of ETV can be described by the following dynamic equations [1]:

\dot{θ} = (K_{g 1} K_{g 2}) ω

(1)

\dot{ω} = - \frac{B_{t}}{J_{t}} ω + \frac{K_{t}}{J_{t}} i - \frac{1}{J_{t}} T_{f} (ω) - \frac{1}{J_{t}} T_{s p} (θ)

(2)

\dot{\dot{i}} = - \frac{K_{v}}{L} ω - \frac{R}{L} i + \frac{1}{L} u

(3)

where,

i

represents the current flowing in the armature circuit of DC motor. The variables

θ

and

ω

denote the angular position and velocity of the rotating plate, respectively. The parameters

R

and

L

represent the resistance and inductance of armature circuit, respectively. The constant

K_{g 1}

represents the gear ratio of intermediate/pinion gear box and

K_{g 2}

represents the gear ratio of the sector/intermediate gear box. The constants

K_{t}

and

K_{v}

represent the motor torque and motor EMF constants, respectively. The DC motor is actuated by the input voltage

u

. The coefficients

J_{t}

and

B_{t}

denote the total inertia and damping coefficients, respectively, of the throttle valve system. Also, there are two nonlinear torques in the model

T_{s p} (θ)

and

T_{f} (ω)

, which are due to non-smooth nonlinearity of spring and stick-slip friction torque.

The nonlinear torques

T_{s p} (θ)

and

T_{f} (ω)

can be defined, respectively, as:

T_{sp} (θ) = m_{1} (θ - θ_{o}) + D s g n (θ - θ_{o})

(4)

T_{f} (ω) = F_{s} s g n (ω)

(5)

where

D

m_{1}

denote the spring offset and gain, respectively, while

F_{s}

represents coulomb friction constant of positive value. The spring default position is denoted by

θ_{o}

One can describe the dynamic equation of Eq. (1) in state form by assigning the states variables

x_{1}

x_{2}

, and

x_{3}

to the physical quantities

θ

(K_{g 1} . K_{g 2}) . ω

and

i

, respectively. Then, the following state variables can be obtained:

{\dot{x}}_{1} = x_{2}

(6)

{\dot{x}}_{2} = a_{21} (x_{1} - x_{10}) + a_{22} x_{2} + a_{23} x_{3} - μ s g n (x_{2}) - β s g n (x_{1} - x_{10})

(7)

{\dot{x}}_{3} = a_{32} x_{2} + a_{33} x_{3} + b_{3} u

(8)

where the coefficients of above equations can be defined by:

a_{21} = (K_{g 1} K_{g 2} / J_{t}) m_{1}, a_{22} = - (B_{t} / J_{t}), a_{23} = (K_{g 1} K_{g 2} K_{t} / J_{t}), a_{32} = - (K_{v} / K_{g 1} K_{g 2} L)

a_{33} = - R / L, b_{3} = - 1 / L, μ = (K_{g 1} K_{g 2} / J_{t}) F_{s}, β = (K_{g 1} K_{g 2} / J_{t}) D

If the value of resistance

R

is much greater than the inductance vale

L

, that is

R ≫ L

, then the system of equations, Eq. (6) to Eq. (8), can be reduced to the following state space representation

{\dot{x}}_{1} = x_{2}

(9)

{\dot{x}}_{2} = a_{1} (x_{1} - x_{10}) + a_{2} x_{2} + b u + d (t)

(10)

where

d (t)

represents the lumped total uncertainty and disturbance inherited in the ETV system;

d (t) = ∆ a_{1} (x_{1} - x_{1 o}) + ∆ a_{2} x_{2} + ∆ b u - μ s g n (x_{2}) - β s g n (x_{1} - x_{1 o})

(11)

3 Extended state observer design

The key in the design of ESO is how to extend the system model so that the disturbance and uncertainty are lumped into an extended state. In this study the LESO and NESO strategy are presented. In order to apply the ESO, the mathematical model of ETV described by Eq. (9) and Eq. (10) have to be extended in the following form:

{\dot{x}}_{1} = x_{2}

(12)

{\dot{x}}_{2} = x_{3} + b u

(13)

{\dot{x}}_{3} = d h (x) / d t

(14)

where the third sate

x_{3}

accumulates all the terms which contain all nonlinearities, uncertainties and other parts of the system; that is,

x_{3} = h (x) = a_{1} (x_{1} - x_{10}) + a_{2} x_{2} + b u + d (t)

(15)

on the condition that the new state variable is bound

| h (x) | \leq h_{\max}

and differentiable. This leads to casting the following important assumption.

Assumption

For the system described by state variable of Eq. (5), the extended state variable can be satisfied if there is a new bounded and differentiable state variable $h (x)$ which accumulates all nonlinearities and uncertainties of the system.

According to the extended structure of ETV system, the linear version of ESO can be synthesized as follows:

{\dot{z}}_{1} = z_{2} + \frac{α_{1}}{ϵ} (x_{1} - z_{1})

(16)

{\dot{z}}_{2} = z_{3} + \frac{α_{2}}{ϵ^{2}} (x_{1} - z_{1}) + b u

(17)

{\dot{z}}_{3} = \frac{α_{3}}{ϵ^{3}} (x_{1} - z_{1})

(18)

where the observer's states

z_{1}

and

z_{2}

work to estimate the actual states of the system

x_{1}

and

x_{2}

, respectively, while the state of observer

z_{3}

is responsible for estimating the lumped uncertainties

h (x)

The parameters $ϵ$ , $α_{1}$ , $α_{2}$ and $α_{3}$ are positive constants and they are chosen such that the polynomial $s^{3} + α_{1} s^{2} + α_{2} s + α_{3}$ is Hurwitz. Satisfying this condition, the designed observer can perform the following goal $z_{1} \to x_{1}$ , $z_{2} \to x_{2}$ , $z_{3} \to h (x)$ as $t \to \infty$ .

Another version of extended state observer is the nonlinear ESO. The structure of this type can be described by

{\dot{z}}_{1} = z_{2} + α_{1} ϵ g_{1} ((x_{1} - z_{1}) / ϵ^{2})

(19)

{\dot{z}}_{2} = z_{3} + α_{2} g_{1} ((x_{1} - z_{1}) / ϵ^{2}) + b u

(20)

{\dot{z}}_{3} = \frac{α_{3}}{ϵ} g_{1} ((x_{1} - z_{1}) / ϵ^{2})

(21)

where

α_{1}

α_{2}

and

α_{3}

are design constants of positive value and the function

g_{1}

is a nonlinear function of estimation error

(x_{1} - z_{1})

and

ϵ

is scaling constant of small value. In this study, a trigonometric function

\tan (.)

is used to represent the function

g_{1}

in above equation.

4 Computer simulation

In order to verify the effectiveness of proposed versions of extended state observers in estimating the actual states and uncertainties of EVT, the numerical simulation based on MATLAB/SIMULINK has been conducted. This programming tool is efficient in control design of different control applications. It is supported by robust numerical solvers which can cope with complex differential equations. It also provides flexibility in programming. The control engineers can develop their control algorithms either with matlab files, blocks within SIMULINK environment, or hybridization of both Simulink tools with matlab functions. The control designer can code the control laws or represent the differential equations of dynamic system using general instructions which are devoted to these purposes. The physical parameters of ETV are listed in Table 1.

Table 1.

The parameter setting of ETV

Parameter symbol	Setting value
$a_{21}$	1/18
$a_{12}$	$- 1.68 \times 10^{3}$
$a_{22}$	$- 32.9820$
$a_{23}$	$4.2941 \times 10^{3}$
$a_{32}$	$- 11.6039$
$a_{33}$	$- 5.2087 \times 10^{2}$
$b_{3}$	$4.7438 \times 10^{2}$
$κ$	$4.6139 \times 10^{3}$
$μ$	$2.1073 \times 10^{3}$

In the first scenario, the open-loop test of ETV has been simulated based on full dynamic model, Eq. (2), and reduced dynamic model, Eq. (3). In Figs 2 and 3, the open-loop tests have been conducted for both reduced order and full order models, respectively, under zero test input with initial values $x_{10} = 7 °$ and $x_{10} = 12 °$ . It is clear from the figures that the plate angle of ETV finally settles at $3 °$ regardless of the value of initial condition and the angle returns to the idle position which keeps a small amount of air to flow that enable the running of the vehicle.

In this open-loop test scenario, it is interesting to investigate the convergence of states in phase plane portrait. Figures 4 and 5 show the trace of trajectories with different initial conditions ( $x_{10} = 7 °$ and $x_{10} = 12 °$ ) for both reduced model and full model, respectively, in the case of homogeneous dynamic system. It is clear from the figures that the convergence of trajectories occurs in finite time. Also, one can notice that there is hard switching at some positions of trajectory as it converges to the equilibrium point. This is due to the change of stiffness in springs of the throttle valve.

In the second scenario, the numerical simulation has been implemented for estimating the states and uncertainties of the ETV system. Figure 6 shows the validation of linear ESO, where $z_{1}$ and $z_{2}$ represent the observer states (the estimated states of actual system states) and $z_{3}$ represents the estimation of disturbance $d (t)$ . In this figure, the input of open-loop system is set to $u = 1.5$ volt and the initial value is set at $x_{10} = 12 °$ . It is clear that the estimation state $z_{3}$ against $d (t)$ is very close; as shown in Fig. 7, and the linear ESO could successfully estimate all states of the ETV system.

In the next simulation, the performance of nonlinear ESO has been verified and assessed. Figure 8 shows the behavior of estimated states due to nonlinear ESO. Again, the nonlinear ESO could successfully estimate the actual states and the state representing the total uncertainty of the ETV system. Figure 9 shows the behaviors of both actual states and estimated states due to both linear and nonlinear ESO.

Table 2 gives the evaluation report based on two types of ESO. The Root Mean Square of Error (RMSE) has been used as index of evaluation. The table shows that the LESO gives less RMSE for estimation errors of angular position and angular error velocity. Moreover, the LESO outperforms NESO in terms of uncertainty estimation. The LESO gives better estimation accuracy of uncertainty as compared to that based on NESO.

Table 2.

Performance evaluation of ESOs

Type of Estimation Error	RMSE
Type of Estimation Error	LESO	NESO
Tracking estimation error of angle	${2.113}^{o}$	${3.104}^{o}$
Tracking estimation error of angular velocity	1.098	1.724
Uncertainty estimation error	5.200	7.01

5 Conclusion

In this study, two versions of ESOs are proposed to estimate the actual states and uncertainties of the ETV system. These observers are LESO and NESO. The extension of the system model is a prerequisition in application of ESO. The extended state represents all uncertainties and external loads in the applied system. The performances of LESO and NESO have been verified using numerical simulations, which showed that both observers could estimate the actual states and uncertainties of throttle valve in a good manner. The performances of the proposed ESOs are evaluated based on the index RMSE. According to Table 2, it has been concluded that LESO gives less RMSE for estimation errors of angular position and angular error velocity. Moreover, the LESO outperforms NESO in terms of uncertainty estimation. Based on these results, one can conclude that the LESO has better estimation performance than NESO in terms of estimation errors.

In order to extend this study to future work, other observer techniques can be suggested to estimate the states of ETV such as adaptive observer, backstepping observer, nonlinear disturbance observer, perturbation observer, sliding mode observer [22–27]. One can conduct a comparison study between one of the suggested observers and the proposed observer in this study. Other suggestion for future work is to use optimization techniques such as Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Butterfly Optimization Algorithm (BOA), and Gray Wolf Optimization (GWO) to tune the design parameters of the proposed observer for further improvement [28–32].

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International Review of Applied Sciences and Engineering

Print ISSN:: 2062-0810

Online ISSN:: 2063-4269

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Mohammad Nazir AHMAD Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Malaysia

Murat BAKIROV Center for Materials and Lifetime Management Ltd. Moscow Russia

Nicolae BALC Technical University of Cluj-Napoca Cluj-Napoca Romania

Umberto BERARDI Ryerson University Toronto Canada

Ildikó BODNÁR University of Debrecen Debrecen Hungary

Sándor BODZÁS University of Debrecen Debrecen Hungary

Fatih Mehmet BOTSALI Selçuk University Konya Turkey

Samuel BRUNNER Empa Swiss Federal Laboratories for Materials Science and Technology

István BUDAI University of Debrecen Debrecen Hungary

Constantin BUNGAU University of Oradea Oradea Romania

Shanshan CAI Huazhong University of Science and Technology Wuhan China

Michele De CARLI University of Padua Padua Italy

Robert CERNY Czech Technical University in Prague Czech Republic

György CSOMÓS University of Debrecen Debrecen Hungary

Tamás CSOKNYAI Budapest University of Technology and Economics Budapest Hungary

Eugen Ioan GERGELY University of Oradea Oradea Romania

József FINTA University of Pécs Pécs Hungary

Anna FORMICA IASI National Research Council Rome

Alexandru GACSADI University of Oradea Oradea Romania

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Géza HUSI University of Debrecen Debrecen Hungary

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Gudni JÓHANNESSON The National Energy Authority of Iceland Reykjavik Iceland

László KAJTÁR Budapest University of Technology and Economics Budapest Hungary

Ferenc KALMÁR University of Debrecen Debrecen Hungary

Tünde KALMÁR University of Debrecen Debrecen Hungary

Milos KALOUSEK Brno University of Technology Brno Czech Republik

Jan KOCI Czech Technical University in Prague Prague Czech Republic

Vaclav KOCI Czech Technical University in Prague Prague Czech Republic

Imra KOCSIS University of Debrecen Debrecen Hungary

Imre KOVÁCS University of Debrecen Debrecen Hungary

Angela Daniela LA ROSA Norwegian University of Science and Technology

Éva LOVRA Univeqrsity of Debrecen Debrecen Hungary

Elena LUCCHI Eurac Research, Institute for Renewable Energy Bolzano Italy

Tamás MANKOVITS University of Debrecen Debrecen Hungary

Igor MEDVED Slovak Technical University in Bratislava Bratislava Slovakia

Ligia MOGA Technical University of Cluj-Napoca Cluj-Napoca Romania

Marco MOLINARI Royal Institute of Technology Stockholm Sweden

Henrieta MORAVCIKOVA Slovak Academy of Sciences Bratislava Slovakia

Phalguni MUKHOPHADYAYA University of Victoria Victoria Canada

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Joaquim Norberto PIRES Universidade de Coimbra Coimbra Portugal

László POKORÁDI Óbuda University Budapest Hungary

Antal PUHL University of Debrecen Debrecen Hungary

Roman RABENSEIFER Slovak University of Technology in Bratislava Bratislava Slovak Republik

Mohammad H. A. SALAH Hashemite University Zarqua Jordan

Dietrich SCHMIDT Fraunhofer Institute for Wind Energy and Energy System Technology IWES Kassel Germany

Lorand SZABÓ Technical University of Cluj-Napoca Cluj-Napoca Romania

Csaba SZÁSZ Technical University of Cluj-Napoca Cluj-Napoca Romania

Ioan SZÁVA Transylvania University of Brasov Brasov Romania

Péter SZEMES University of Debrecen Debrecen Hungary

Edit SZŰCS University of Debrecen Debrecen Hungary

Radu TARCA University of Oradea Oradea Romania

Zsolt TIBA University of Debrecen Debrecen Hungary

László TÓTH University of Debrecen Debrecen Hungary

László TÖRÖK University of Debrecen Debrecen Hungary

Anton TRNIK Constantine the Philosopher University in Nitra Nitra Slovakia

Ibrahim UZMAY Erciyes University Kayseri Turkey

Tibor VESSELÉNYI University of Oradea Oradea Romania

Nalinaksh S. VYAS Indian Institute of Technology Kanpur India

Deborah WHITE The University of Adelaide Adelaide Australia

Sahin YILDIRIM Erciyes University Kayseri Turkey

International Review of Applied Sciences and Engineering
Address of the institute: Faculty of Engineering, University of Debrecen
H-4028 Debrecen, Ótemető u. 2-4. Hungary
Email: irase@eng.unideb.hu

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2022
Scimago
Scimago H-index	9
Scimago Journal Rank	0.235
Scimago Quartile Score	Architecture (Q2) Engineering (miscellaneous) (Q3) Environmental Engineering (Q3) Information Systems (Q4) Management Science and Operations Research (Q4) Materials Science (miscellaneous) Q3)
Scopus
Scopus Cite Score	1.6
Scopus CIte Score Rank	Architecture 46/170 (73rd PCTL) General Engineering 174/302 (42nd PCTL) Materials Science (miscellaneous) 93/150 (38th PCTL) Environmental Engineering 123/184 (33rd PCTL) Management Science and Operations Research 142/198 (28th PCTL) Information Systems 281/379 (25th PCTL)
Scopus SNIP	0.686

2021
Scimago
Scimago H-index	7
Scimago Journal Rank	0,199
Scimago Quartile Score	Engineering (miscellaneous) (Q3) Environmental Engineering (Q4) Information Systems (Q4) Management Science and Operations Research (Q4) Materials Science (miscellaneous) (Q4)
Scopus
Scopus Cite Score	1,2
Scopus CIte Score Rank	Architecture 48/149 (Q2) General Engineering 186/300 (Q3) Materials Science (miscellaneous) 79/124 (Q3) Environmental Engineering 118/173 (Q3) Management Science and Operations Research 141/184 (Q4) Information Systems 274/353 (Q4)
Scopus SNIP	0,457

2020
Scimago H-index	5
Scimago Journal Rank	0,165
Scimago Quartile Score	Engineering (miscellaneous) Q3 Environmental Engineering Q4 Information Systems Q4 Management Science and Operations Research Q4 Materials Science (miscellaneous) Q4
Scopus Cite Score	102/116=0,9
Scopus Cite Score Rank	General Engineering 205/297 (Q3) Environmental Engineering 107/146 (Q3) Information Systems 269/329 (Q4) Management Science and Operations Research 139/166 (Q4) Materials Science (miscellaneous) 64/98 (Q3)
Scopus SNIP	0,26
Scopus Cites	57
Scopus Documents	36
Days from submission to acceptance	84
Days from acceptance to publication	348
Acceptance Rate	23%

2019
Scimago H-index	4
Scimago Journal Rank	0,229
Scimago Quartile Score	Engineering (miscellaneous) Q2 Environmental Engineering Q3 Information Systems Q3 Management Science and Operations Research Q4 Materials Science (miscellaneous) Q3
Scopus Cite Score	46/81=0,6
Scopus Cite Score Rank	General Engineering 227/299 (Q4) Environmental Engineering 107/132 (Q4) Information Systems 259/300 (Q4) Management Science and Operations Research 136/161 (Q4) Materials Science (miscellaneous) 60/86 (Q3)
Scopus SNIP	0,866
Scopus Cites	35
Scopus Documents	47
Acceptance Rate	21%

International Review of Applied Sciences and Engineering
Publication Model	Gold Open Access
Submission Fee	none
Article Processing Charge	1100 EUR/article
Regional discounts on country of the funding agency	World Bank Lower-middle-income economies: 50% World Bank Low-income economies: 100%
Further Discounts	Limited number of full waiver available. Editorial Board / Advisory Board members: 50% Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information	Gold Open Access

International Review of Applied Sciences and Engineering
Language	English
Size	A4
Year of Foundation	2010
Volumes per Year	1
Issues per Year	3
Founder	Debreceni Egyetem
Founder's Address	H-4032 Debrecen, Hungary Egyetem tér 1
Publisher	Akadémiai Kiadó
Publisher's Address	H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible Publisher	Chief Executive Officer, Akadémiai Kiadó
ISSN	2062-0810 (Print)
ISSN	2063-4269 (Online)

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Abstract

Abstract

1 Introduction

2 Model of electronic throttle valve system

3 Extended state observer design

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4 Computer simulation

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