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

Search for other papers by Noor S. Mahmood in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0009-0000-0933-0303
,
Amjad J. Humaidi Control and Systems Engineering Department, University of Technology, Baghdad, 10066, Iraq

Search for other papers by Amjad J. Humaidi in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-9071-1329
,
Raaed S. Al-Azzawi Control and Systems Engineering Department, University of Technology, Baghdad, 10066, Iraq

Search for other papers by Raaed S. Al-Azzawi in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-3963-8816
, and
Ammar Al-Jodah School of Physics, Maths, and Computing, The University of Western Australia, Perth, WA 6907, Australia

Search for other papers by Ammar Al-Jodah in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0003-4536-1240
Open access

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

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.

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:

  1. To design the Linear Extended State Observer (LESO) and Nonlinear Extended State Observer (NESO) for estimating the states and uncertainties of ETV system.

  2. 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.

Fig. 1.
Fig. 1.

The schematic diagram of ETV

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00662

The mathematical model of ETV can be described by the following dynamic equations [1]:
θ˙=(Kg1Kg2)ω
ω˙=BtJtω+KtJti1JtTf(ω)1JtTsp(θ)
i˙˙=KvLωRLi+1Lu
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 Kg1 represents the gear ratio of intermediate/pinion gear box and Kg2 represents the gear ratio of the sector/intermediate gear box. The constants Kt and Kv represent the motor torque and motor EMF constants, respectively. The DC motor is actuated by the input voltage u. The coefficients Jt and Bt denote the total inertia and damping coefficients, respectively, of the throttle valve system. Also, there are two nonlinear torques in the model Tsp(θ) and Tf(ω), which are due to non-smooth nonlinearity of spring and stick-slip friction torque.
The nonlinear torques Tsp(θ) and Tf(ω) can be defined, respectively, as:
Tsp(θ)=m1(θθo)+Dsgn(θθo)
Tf(ω)=Fssgn(ω)
where D, m1 denote the spring offset and gain, respectively, while Fs represents coulomb friction constant of positive value. The spring default position is denoted by θo.
One can describe the dynamic equation of Eq. (1) in state form by assigning the states variables x1, x2, and x3 to the physical quantities θ, (Kg1.Kg2).ω and i, respectively. Then, the following state variables can be obtained:
x˙1=x2
x˙2=a21(x1x10)+a22x2+a23x3μsgn(x2)βsgn(x1x10)
x˙3=a32x2+a33x3+b3u
where the coefficients of above equations can be defined by:
a21=(Kg1Kg2/Jt)m1,a22=(Bt/Jt),a23=(Kg1Kg2Kt/Jt),a32=(Kv/Kg1Kg2L)
a33=R/L,b3=1/L,μ=(Kg1Kg2/Jt)Fs,β=(Kg1Kg2/Jt)D
If the value of resistance R is much greater than the inductance vale L, that is RL, then the system of equations, Eq. (6) to Eq. (8), can be reduced to the following state space representation
x˙1=x2
x˙2=a1(x1x10)+a2x2+bu+d(t)
where d(t) represents the lumped total uncertainty and disturbance inherited in the ETV system;
d(t)=a1(x1x1o)+a2x2+buμsgn(x2)βsgn(x1x1o)

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:
x˙1=x2
x˙2=x3+bu
x˙3=dh(x)/dt
where the third sate x3 accumulates all the terms which contain all nonlinearities, uncertainties and other parts of the system; that is,
x3=h(x)=a1(x1x10)+a2x2+bu+d(t)
on the condition that the new state variable is bound |h(x)|hmax 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:
z˙1=z2+α1ϵ(x1z1)
z˙2=z3+α2ϵ2(x1z1)+bu
z˙3=α3ϵ3(x1z1)
where the observer's states z1 and z2 work to estimate the actual states of the system x1 and x2, respectively, while the state of observer z3 is responsible for estimating the lumped uncertainties h(x).

The parameters ϵ, α1, α2 and α3 are positive constants and they are chosen such that the polynomial s3+α1s2+α2s+α3 is Hurwitz. Satisfying this condition, the designed observer can perform the following goal z1x1, z2x2, z3h(x) as t.

Another version of extended state observer is the nonlinear ESO. The structure of this type can be described by
z˙1=z2+α1ϵg1((x1z1)/ϵ2)
z˙2=z3+α2g1((x1z1)/ϵ2)+bu
z˙3=α3ϵg1((x1z1)/ϵ2)
where α1 , α2 and α3 are design constants of positive value and the function g1 is a nonlinear function of estimation error (x1z1) and ϵ is scaling constant of small value. In this study, a trigonometric function tan(.) is used to represent the function g1 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 symbolSetting value
a211/18
a121.68×103
a2232.9820
a234.2941×103
a3211.6039
a335.2087×102
b34.7438×102
κ4.6139×103
μ2.1073×103

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 x10=7° and x10=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.

Fig. 2.
Fig. 2.

Open loop response (reduce model)

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00662

Fig. 3.
Fig. 3.

Open loop response (full model)

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00662

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 (x10=7° and x10=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.

Fig. 4.
Fig. 4.

Phase portrait (θ,θ)˙ (reduced model)

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00662

Fig. 5.
Fig. 5.

Phase portrait (θ,θ)˙ (full model)

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00662

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 z1 and z2 represent the observer states (the estimated states of actual system states) and z3 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 x10=12°. It is clear that the estimation state z3 against d(t) is very close; as shown in Fig. 7, and the linear ESO could successfully estimate all states of the ETV system.

Fig. 6.
Fig. 6.

The performance of linear ESO

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00662

Fig. 7.
Fig. 7.

The responses of estimation errors due to linear ESO

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00662

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.

Fig. 8.
Fig. 8.

The performance of nonlinear ESO

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00662

Fig. 9.
Fig. 9.

The behaviors of actual and estimated states due to both linear and nonlinear ESO

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00662

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 ErrorRMSE
LESONESO
Tracking estimation error of angle2.113o3.104o
Tracking estimation error of angular velocity1.0981.724
Uncertainty estimation error5.2007.01

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].

References

  • [1]

    Y. Pan, U. Ozguner, and O. H. Dagci, “Variable-structure control of electronic throttle valve,” IEEE Trans. Ind. Electron., vol. 55, no. 11, pp. 38993907, 2008.

    • Search Google Scholar
    • Export Citation
  • [2]

    X. Jiao, J. Zhang, and T. Shen, “An adaptive servo control strategy for automotive electronic throttle and experimental validation,” IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 62756284, 2014.

    • Search Google Scholar
    • Export Citation
  • [3]

    C.-H. Chen, H.-L. Tsai, and Y.-S. Lin, “Servo control design for electronic throttle valve with nonlinear spring effect,” in 2010 11th IEEE International Workshop on Advanced Motion Control (AMC), Nagaoka, Japan, 2010, pp. 8893.

    • Search Google Scholar
    • Export Citation
  • [4]

    A. J. Humaidi and A. H. Hameed, “Design and comparative study of advanced adaptive control schemes for position control of electronic throttle valve,” Information, vol. 10, no. 2, p. 65, 2019. https://doi.org/10.3390/info10020065.

    • Search Google Scholar
    • Export Citation
  • [5]

    A. J. Humaidi and A. H. Hameed, “PMLSM position control based on continuous projection adaptive sliding mode controller,” Systems Sci. Control Eng., vol. 6, no. 3, pp. 242252, 2018.

    • Search Google Scholar
    • Export Citation
  • [6]

    A. J. Humaidi, A. H. Hameed, and M. R. Hameed, “Robust adaptive speed control for DC motor using novel weighted E-modified MRAC,” IEEE Int. Conf. Power, Control, Signals Instrument. Eng., ICPCSI 2017., pp. 313319, 2018. https://doi.org/10.1109/ICPCSI.2017.8392302.

    • Search Google Scholar
    • Export Citation
  • [7]

    G. Bao-Zhu and Z. Zhi-Liang, Active Disturbance Rejection Control for Nonlinear Systems, 1st Edition, John Wiley & Sons, Ltd, 2016.

  • [8]

    W. Han, H. L. Trentelman, Z. Wang, and Y. Shen, “A simple approach to distributed observer design for linear systems,” IEEE Trans. Automatic Control, vol. 64, no. 1, pp. 329336, 2019.

    • Search Google Scholar
    • Export Citation
  • [9]

    A. J. Humaidi, A. A. Mohammed, A. H. Hameed, I. K. Ibrahim, A. T. Azar, and A. Q. Al-Dujaili, “State estimation of rotary inverted pendulum: a comparative study of observers performance,” in 2020 IEEE Congreso Bienal de Argentina (ARGENCON), Resistencia, Argentina, 2020, pp. 17.

    • Search Google Scholar
    • Export Citation
  • [10]

    A. I. Abdul-Kareem, A. F. Hasan, A. A. Al-Qassar, A. J. Humaidi, R. F. Hassan, I. K. Ibraheem, and A. T. Azar, “Rejection of wing-rock motion in delta wing aircrafts based on optimal LADRC schemes with butterfly optimization algorithm,” J. Eng. Sci. Technol., vol. 17, no. 4, pp. 24762495, 2022.

    • Search Google Scholar
    • Export Citation
  • [11]

    A. J. Humaidi and H. M. Badr, “Linear and nonlinear active disturbance rejection controllers for single-link flexible joint robot manipulator based on PSO tuner,” J. Eng. Sci. Technol. Rev., vol. 11, no. 3, pp. 133138, 2018.

    • Search Google Scholar
    • Export Citation
  • [12]

    W. Ze-Hao and G. Bao-Zhu, “Extended state observer for MIMO nonlinear systems with stochastic uncertainties,” Int. J. Control, vol. 93, no. 3, pp. 424436, 2020.

    • Search Google Scholar
    • Export Citation
  • [13]

    A. Castillo, P. García, R. Sanz, and P. Albertos, “Enhanced extended state observer-based control for systems with mismatched uncertainties and disturbances,” ISA Trans., vol. 73, pp. 110, 2018.

    • Search Google Scholar
    • Export Citation
  • [14]

    S. A. Al-Samarraie, Y. K. Al-Nadawi, M. H. Mishary, and M. M. Salih, “Electronic throttle valve control design based on sliding mode perturbation estimator,” IJCCCE, vol. 15, no. 2, pp. 6574, 2015.

    • Search Google Scholar
    • Export Citation
  • [15]

    Y. Li, B. Yang, X. Zhang, Q. Wu, and T. Zheng, “Extended state observer–based intelligent double integral sliding mode control of electronic throttle valve,” Adv. Mech. Eng., vol. 9, no. 12, 2017.

    • Search Google Scholar
    • Export Citation
  • [16]

    J. Xue, X. Jiao, and Z. Sun, “ESO-based double closed-loop servo control for automobile electronic throttle,” IFAC-PapersOnLine, vol. 51, no. 31, pp. 979983, 2018.

    • Search Google Scholar
    • Export Citation
  • [17]

    Y. Li, B. Yang, T. Zheng, Y. Li, M. Cui, and S. Peeta, “Extended-state-observer-based double-loop integral sliding-mode control of electronic throttle valve,” IEEE Trans. Intell. Transport. Syst., vol. 16, no. 5, pp. 25012510, 2015.

    • Search Google Scholar
    • Export Citation
  • [18]

    B. Yang, M. Liu, H. Kim, and X. Cui, “Luenberger-sliding mode observer based fuzzy double loop integral sliding mode controller for electronic throttle valve,” J. Process Control, vol. 61, pp. 3646, 2018.

    • Search Google Scholar
    • Export Citation
  • [19]

    T.-X. Zheng, B. Yang, Y.-F. Li, and B. Wang, “Extended state observer based sliding mode control of electronic throttle valve,” in Proceeding of the 11th World Congress on Intelligent Control and Automation, Shenyang, China, 2014, pp. 46324637.

    • Search Google Scholar
    • Export Citation
  • [20]

    R. Gzam, H. Gharsallaoui, and M. Benrejeb, “On electronic throttle valve control system based observers,” in 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), Hammamet, Tunisia, 2023, pp. 16.

    • Search Google Scholar
    • Export Citation
  • [21]

    O. T. Agbaje, S. Li, H. Sun, and L. Zhang, “Continuous finite-time TSM control for electronic throttle system,” Jiangsu Annu. Conf. Automation (JACA 2019), vol. 2019, no. 22, pp. 83838389, 2019.

    • Search Google Scholar
    • Export Citation
  • [22]

    M. Y. Hassan, J. H. Amjad, and M. K. Hamza, “On the design of backstepping controller for Acrobot system based on adaptive observer,” Int. Rev. Electr. Eng., vol. 15, no. 4, pp. 328335, 2020.

    • Search Google Scholar
    • Export Citation
  • [23]

    A. J. Humaidi, E. N. Tala'at, M. R. Hameed and A. H. Hameed, “Design of adaptive observer-based backstepping control of cart-Pole pendulum system,” in 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 2019, pp. 15.

    • Search Google Scholar
    • Export Citation
  • [24]

    A. Falah, A. J. Humaidi, A. Al-Dujaili, and I. K. Ibraheem, “Robust super-twisting sliding control of PAM- actuated manipulator based on perturbation observer,” Cogent Eng., vol. 7, no. 1, 2020, Art no. 1858393.

    • Search Google Scholar
    • Export Citation
  • [25]

    A. S. Aljuboury, A. H. Hameed, A. R. Ajel, A. J. Humaidi, A. Alkhayyat, and A. K. A. Mhdawi, “Robust adaptive control of knee exoskeleton-assistant system based on nonlinear disturbance observer,” Actuators, vol. 11, no. 3, p. 78, 2022. https://doi.org/10.3390/act11030078.

    • Search Google Scholar
    • Export Citation
  • [26]

    S. M. Mahdi, N. Q. Yousif, A. A. Oglah, A. J. Humaidi, and A. T. Azar, “Adaptive synergetic motion control for wearable knee‐assistive system: A rehabilitation of disabled patients,” Actuators, vol. 11, no. 7, p. 176, 2022.

    • Search Google Scholar
    • Export Citation
  • [27]

    A. J. Humaidi and M. R. Hameed, “Design and performance investigation of block-backstepping algorithms for ball and arc system,” IEEE Int. Conf. Power, Control, Signals Instrument. Eng., ICPCSI 2017, pp. 325332, 2018. https://doi.org/10.1109/ICPCSI.2017.8392309.

    • Search Google Scholar
    • Export Citation
  • [28]

    Z. A. Waheed and A. J. Humaidi, “Design of optimal sliding mode control of elbow wearable exoskeleton system based on Whale optimization algorithm,” J. Européen des Systèmes Automatisés., vol. 55, no. 4, pp. 459466, 2022. https://doi.org/10.18280/jesa.550404.

    • Search Google Scholar
    • Export Citation
  • [29]

    N. Q. Yousif, A. F. Hasan, A. H. Shallal, A. J. Humaidi, and T. Luay, “Performance improvement of nonlinear differentiator based on optimization algorithms,” J. Eng. Sci. Technol., vol. 18, no. 3, pp. 16961712, 2023.

    • Search Google Scholar
    • Export Citation
  • [30]

    N. M. Noaman, A. S. Gatea, A. J. Humaidi, S. K. Kadhim, and A. F. Hasan, “Optimal tuning of PID-controlled magnetic bearing system for tracking control of pump impeller in artificial heart,” J. Européen des Systèmes Automatisés, vol. 56, no. 1, pp. 2127, 2023. https://doi.org/10.18280/jesa.560103.

    • Search Google Scholar
    • Export Citation
  • [31]

    R. S. Al-Azzawi and M. A. Simaan, “Sampled closed-loop control in multi-controller multi-objective control systems,” SoutheastCon 2018, pp. 17, 2018.

    • Search Google Scholar
    • Export Citation
  • [32]

    R. S. Al-Azzawi and M. A. Simaan, “On the selection of leader in Stackelberg games with parameter uncertainty,” Int. J. Systems Sci., vol. 52, no. 1, pp. 8694, 2020. https://doi.org/10.1080/00207721.2020.1820097.

    • Search Google Scholar
    • Export Citation
  • [1]

    Y. Pan, U. Ozguner, and O. H. Dagci, “Variable-structure control of electronic throttle valve,” IEEE Trans. Ind. Electron., vol. 55, no. 11, pp. 38993907, 2008.

    • Search Google Scholar
    • Export Citation
  • [2]

    X. Jiao, J. Zhang, and T. Shen, “An adaptive servo control strategy for automotive electronic throttle and experimental validation,” IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 62756284, 2014.

    • Search Google Scholar
    • Export Citation
  • [3]

    C.-H. Chen, H.-L. Tsai, and Y.-S. Lin, “Servo control design for electronic throttle valve with nonlinear spring effect,” in 2010 11th IEEE International Workshop on Advanced Motion Control (AMC), Nagaoka, Japan, 2010, pp. 8893.

    • Search Google Scholar
    • Export Citation
  • [4]

    A. J. Humaidi and A. H. Hameed, “Design and comparative study of advanced adaptive control schemes for position control of electronic throttle valve,” Information, vol. 10, no. 2, p. 65, 2019. https://doi.org/10.3390/info10020065.

    • Search Google Scholar
    • Export Citation
  • [5]

    A. J. Humaidi and A. H. Hameed, “PMLSM position control based on continuous projection adaptive sliding mode controller,” Systems Sci. Control Eng., vol. 6, no. 3, pp. 242252, 2018.

    • Search Google Scholar
    • Export Citation
  • [6]

    A. J. Humaidi, A. H. Hameed, and M. R. Hameed, “Robust adaptive speed control for DC motor using novel weighted E-modified MRAC,” IEEE Int. Conf. Power, Control, Signals Instrument. Eng., ICPCSI 2017., pp. 313319, 2018. https://doi.org/10.1109/ICPCSI.2017.8392302.

    • Search Google Scholar
    • Export Citation
  • [7]

    G. Bao-Zhu and Z. Zhi-Liang, Active Disturbance Rejection Control for Nonlinear Systems, 1st Edition, John Wiley & Sons, Ltd, 2016.

  • [8]

    W. Han, H. L. Trentelman, Z. Wang, and Y. Shen, “A simple approach to distributed observer design for linear systems,” IEEE Trans. Automatic Control, vol. 64, no. 1, pp. 329336, 2019.

    • Search Google Scholar
    • Export Citation
  • [9]

    A. J. Humaidi, A. A. Mohammed, A. H. Hameed, I. K. Ibrahim, A. T. Azar, and A. Q. Al-Dujaili, “State estimation of rotary inverted pendulum: a comparative study of observers performance,” in 2020 IEEE Congreso Bienal de Argentina (ARGENCON), Resistencia, Argentina, 2020, pp. 17.

    • Search Google Scholar
    • Export Citation
  • [10]

    A. I. Abdul-Kareem, A. F. Hasan, A. A. Al-Qassar, A. J. Humaidi, R. F. Hassan, I. K. Ibraheem, and A. T. Azar, “Rejection of wing-rock motion in delta wing aircrafts based on optimal LADRC schemes with butterfly optimization algorithm,” J. Eng. Sci. Technol., vol. 17, no. 4, pp. 24762495, 2022.

    • Search Google Scholar
    • Export Citation
  • [11]

    A. J. Humaidi and H. M. Badr, “Linear and nonlinear active disturbance rejection controllers for single-link flexible joint robot manipulator based on PSO tuner,” J. Eng. Sci. Technol. Rev., vol. 11, no. 3, pp. 133138, 2018.

    • Search Google Scholar
    • Export Citation
  • [12]

    W. Ze-Hao and G. Bao-Zhu, “Extended state observer for MIMO nonlinear systems with stochastic uncertainties,” Int. J. Control, vol. 93, no. 3, pp. 424436, 2020.

    • Search Google Scholar
    • Export Citation
  • [13]

    A. Castillo, P. García, R. Sanz, and P. Albertos, “Enhanced extended state observer-based control for systems with mismatched uncertainties and disturbances,” ISA Trans., vol. 73, pp. 110, 2018.

    • Search Google Scholar
    • Export Citation
  • [14]

    S. A. Al-Samarraie, Y. K. Al-Nadawi, M. H. Mishary, and M. M. Salih, “Electronic throttle valve control design based on sliding mode perturbation estimator,” IJCCCE, vol. 15, no. 2, pp. 6574, 2015.

    • Search Google Scholar
    • Export Citation
  • [15]

    Y. Li, B. Yang, X. Zhang, Q. Wu, and T. Zheng, “Extended state observer–based intelligent double integral sliding mode control of electronic throttle valve,” Adv. Mech. Eng., vol. 9, no. 12, 2017.

    • Search Google Scholar
    • Export Citation
  • [16]

    J. Xue, X. Jiao, and Z. Sun, “ESO-based double closed-loop servo control for automobile electronic throttle,” IFAC-PapersOnLine, vol. 51, no. 31, pp. 979983, 2018.

    • Search Google Scholar
    • Export Citation
  • [17]

    Y. Li, B. Yang, T. Zheng, Y. Li, M. Cui, and S. Peeta, “Extended-state-observer-based double-loop integral sliding-mode control of electronic throttle valve,” IEEE Trans. Intell. Transport. Syst., vol. 16, no. 5, pp. 25012510, 2015.

    • Search Google Scholar
    • Export Citation
  • [18]

    B. Yang, M. Liu, H. Kim, and X. Cui, “Luenberger-sliding mode observer based fuzzy double loop integral sliding mode controller for electronic throttle valve,” J. Process Control, vol. 61, pp. 3646, 2018.

    • Search Google Scholar
    • Export Citation
  • [19]

    T.-X. Zheng, B. Yang, Y.-F. Li, and B. Wang, “Extended state observer based sliding mode control of electronic throttle valve,” in Proceeding of the 11th World Congress on Intelligent Control and Automation, Shenyang, China, 2014, pp. 46324637.

    • Search Google Scholar
    • Export Citation
  • [20]

    R. Gzam, H. Gharsallaoui, and M. Benrejeb, “On electronic throttle valve control system based observers,” in 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), Hammamet, Tunisia, 2023, pp. 16.

    • Search Google Scholar
    • Export Citation
  • [21]

    O. T. Agbaje, S. Li, H. Sun, and L. Zhang, “Continuous finite-time TSM control for electronic throttle system,” Jiangsu Annu. Conf. Automation (JACA 2019), vol. 2019, no. 22, pp. 83838389, 2019.

    • Search Google Scholar
    • Export Citation
  • [22]

    M. Y. Hassan, J. H. Amjad, and M. K. Hamza, “On the design of backstepping controller for Acrobot system based on adaptive observer,” Int. Rev. Electr. Eng., vol. 15, no. 4, pp. 328335, 2020.

    • Search Google Scholar
    • Export Citation
  • [23]

    A. J. Humaidi, E. N. Tala'at, M. R. Hameed and A. H. Hameed, “Design of adaptive observer-based backstepping control of cart-Pole pendulum system,” in 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 2019, pp. 15.

    • Search Google Scholar
    • Export Citation
  • [24]

    A. Falah, A. J. Humaidi, A. Al-Dujaili, and I. K. Ibraheem, “Robust super-twisting sliding control of PAM- actuated manipulator based on perturbation observer,” Cogent Eng., vol. 7, no. 1, 2020, Art no. 1858393.

    • Search Google Scholar
    • Export Citation
  • [25]

    A. S. Aljuboury, A. H. Hameed, A. R. Ajel, A. J. Humaidi, A. Alkhayyat, and A. K. A. Mhdawi, “Robust adaptive control of knee exoskeleton-assistant system based on nonlinear disturbance observer,” Actuators, vol. 11, no. 3, p. 78, 2022. https://doi.org/10.3390/act11030078.

    • Search Google Scholar
    • Export Citation
  • [26]

    S. M. Mahdi, N. Q. Yousif, A. A. Oglah, A. J. Humaidi, and A. T. Azar, “Adaptive synergetic motion control for wearable knee‐assistive system: A rehabilitation of disabled patients,” Actuators, vol. 11, no. 7, p. 176, 2022.

    • Search Google Scholar
    • Export Citation
  • [27]

    A. J. Humaidi and M. R. Hameed, “Design and performance investigation of block-backstepping algorithms for ball and arc system,” IEEE Int. Conf. Power, Control, Signals Instrument. Eng., ICPCSI 2017, pp. 325332, 2018. https://doi.org/10.1109/ICPCSI.2017.8392309.

    • Search Google Scholar
    • Export Citation
  • [28]

    Z. A. Waheed and A. J. Humaidi, “Design of optimal sliding mode control of elbow wearable exoskeleton system based on Whale optimization algorithm,” J. Européen des Systèmes Automatisés., vol. 55, no. 4, pp. 459466, 2022. https://doi.org/10.18280/jesa.550404.

    • Search Google Scholar
    • Export Citation
  • [29]

    N. Q. Yousif, A. F. Hasan, A. H. Shallal, A. J. Humaidi, and T. Luay, “Performance improvement of nonlinear differentiator based on optimization algorithms,” J. Eng. Sci. Technol., vol. 18, no. 3, pp. 16961712, 2023.

    • Search Google Scholar
    • Export Citation
  • [30]

    N. M. Noaman, A. S. Gatea, A. J. Humaidi, S. K. Kadhim, and A. F. Hasan, “Optimal tuning of PID-controlled magnetic bearing system for tracking control of pump impeller in artificial heart,” J. Européen des Systèmes Automatisés, vol. 56, no. 1, pp. 2127, 2023. https://doi.org/10.18280/jesa.560103.

    • Search Google Scholar
    • Export Citation
  • [31]

    R. S. Al-Azzawi and M. A. Simaan, “Sampled closed-loop control in multi-controller multi-objective control systems,” SoutheastCon 2018, pp. 17, 2018.

    • Search Google Scholar
    • Export Citation
  • [32]

    R. S. Al-Azzawi and M. A. Simaan, “On the selection of leader in Stackelberg games with parameter uncertainty,” Int. J. Systems Sci., vol. 52, no. 1, pp. 8694, 2020. https://doi.org/10.1080/00207721.2020.1820097.

    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand
The author instructions are available in PDF.
Please, download the file from HERE

 

Senior editors

Editor-in-Chief: Ákos, Lakatos University of Debrecen (Hungary)

Founder, former Editor-in-Chief (2011-2020): Ferenc Kalmár University of Debrecen (Hungary)

Founding Editor: György Csomós University of Debrecen (Hungary)

Associate Editor: Derek Clements Croome University of Reading (UK)

Associate Editor: Dezső Beke University of Debrecen (Hungary)

Editorial Board

  • 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 Toronto Metropolitan 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, Dübendorf, Switzerland

    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, Prague, Czech Republic

    Erdem CUCE Recep Tayyip Erdogan University, Rize, Turkey

    György CSOMÓS University of Debrecen, Debrecen, Hungary

    Tamás CSOKNYAI Budapest University of Technology and Economics, Budapest, Hungary

    Anna FORMICA IASI National Research Council, Rome, Italy

    Alexandru GACSADI University of Oradea, Oradea, Romania

    Eugen Ioan GERGELY University of Oradea, Oradea, Romania

    Janez GRUM University of Ljubljana, Ljubljana, Slovenia

    Géza HUSI University of Debrecen, Debrecen, Hungary

    Ghaleb A. HUSSEINI American University of Sharjah, Sharjah, United Arab Emirates

    Nikolay IVANOV Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

    Antal JÁRAI Eötvös Loránd University, Budapest, Hungary

    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

    Imre KOCSIS University of Debrecen, Debrecen, Hungary

    Imre KOVÁCS University of Debrecen, Debrecen, Hungary

    Angela Daniela LA ROSA Norwegian University of Science and Technology, Trondheim, Norway

    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

    Balázs NAGY Budapest University of Technology and Economics, Budapest, Hungary

    Husam S. NAJM Rutgers University, New Brunswick, USA

    Jozsef NYERS Subotica Tech College of Applied Sciences, Subotica, Serbia

    Bjarne W. OLESEN Technical University of Denmark, Lyngby, Denmark

    Stefan ONIGA North University of Baia Mare, Baia Mare, Romania

    Joaquim Norberto PIRES Universidade de Coimbra, Coimbra, Portugal

    László POKORÁDI Óbuda University, Budapest, 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

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

Indexing and Abstracting Services:

  • DOAJ
  • ERIH PLUS
  • Google Scholar
  • ProQuest
  • SCOPUS
  • Ulrich's Periodicals Directory

 

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)

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Dec 2023 0 280 38
Jan 2024 0 236 52
Feb 2024 0 257 91
Mar 2024 0 228 65
Apr 2024 0 88 46
May 2024 0 62 28
Jun 2024 0 82 21