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Nasir Ahmed Alawad Department of Computer Engineering, Faculty of Engineering, Mustansiriyah University, Baghdad, Iraq
Control and Systems Engineering Department, University of Technology, Baghdad, Iraq

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Amjad Jaleel Humaidi Control and Systems Engineering Department, University of Technology, Baghdad, Iraq

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Ahmed Sabah Alaraji Department of Computer Engineering, University of Technology, Baghdad, Iraq

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Abstract

This study revealed the system of a lower limb exoskeleton created for knee rehabilitation. The exoskeleton has been extensively used in rehabilitation robotic device research, but its practical applicability is limited due to its high nonlinearity and uncertain behavior. As a result, the control technique is critical in increasing the efficacy of rehabilitation devices. For the rehabilitation and help of a patient with a lower-limb condition, a sliding mode control (SMC) with proportional derivative (PD) control approach are used as parallel loops. Active disturbances rejection control (ADRC) is used by these controllers to cancel any external influences. To overcome the degradation of disturbance rejection and robustness caused by a failure to fully adjust for the entire disturbance, a (SMC) loop was introduced to the control regulation. By assessing performance indices related to the estimated inaccuracy, the results demonstrate the effectiveness of the suggested controller. Simulink is used for simulation and analysis.

Abstract

This study revealed the system of a lower limb exoskeleton created for knee rehabilitation. The exoskeleton has been extensively used in rehabilitation robotic device research, but its practical applicability is limited due to its high nonlinearity and uncertain behavior. As a result, the control technique is critical in increasing the efficacy of rehabilitation devices. For the rehabilitation and help of a patient with a lower-limb condition, a sliding mode control (SMC) with proportional derivative (PD) control approach are used as parallel loops. Active disturbances rejection control (ADRC) is used by these controllers to cancel any external influences. To overcome the degradation of disturbance rejection and robustness caused by a failure to fully adjust for the entire disturbance, a (SMC) loop was introduced to the control regulation. By assessing performance indices related to the estimated inaccuracy, the results demonstrate the effectiveness of the suggested controller. Simulink is used for simulation and analysis.

1 Introduction

Exoskeletons are a type of mechanical robot that can help people and improve their physical capabilities, such as enhancing soldier strength, supporting the elderly in walking, and healing patients with limb injuries. It has attracted a lot of attention in recent years [1, 2]. The lower leg is the weakest of the limb joints in humans. It facilitates in human movement by supporting body weight, absorbing impact stress, and assisting lower limb swing [3]. Over the past few decades, different control techniques have been researched to increase the accuracy of joint motion control for exoskeletons. In [4, 5] used a feedback and feed forward proportional–integral–derivative (PID) controller for monitoring the limb exoskeleton's desirable output. Despite its ease of implementation, the usage of PID control is limited by the convergence analysis and coefficients adjustment. Soft computing approaches such as fuzzy sets and artificial neural networks have been investigated in recent years (ANNs). For exoskeletons, a fuzzy controller with a bang-bang controller has been proposed [6]. In [7] the rehabilitation robot developed an adaptive self-organizing fuzzy controller. In [8] it was demonstrated how to achieve accurate control performance using ANN-based model predictive control (MPC) methods. Despite the ability to approximate nonlinear properties, real-time performance is constrained, and all of these control applications are limited. System uncertainties, such as exogenous disruptions, unmodeled dynamics, and parameter perturbations, have a significant impact on the performance of a control system. The development of the any controller that attempts to fulfill these objectives while also assuring disturbance rejection and strong tracking performance in the face of huge uncertainty is complicated. As a result, anti-disturbance approaches using both external- and internal-loop controllers and estimators have been widely employed [9]. The ADRC controller was first proposed in [10], which offers many benefits. The industry's rapid adoption of ADRC over the past three decades is evidence of the technology's value in position control and other application fields [1115]. Regarding the biomechanics of the exoskeleton, (SMC) may be an appropriate solution due to its robustness to both internal and external system uncertainties [1618]. To achieve optimal performance, SMC parameters should be chosen carefully. Genetic Algorithm GA [19], particle swarm optimization (PSO) [20], and Grey-wolf optimization [21] are examples of common optimization methods that are given and used in exoskeleton devices. Algorithm for ant colony optimization [22] was also used. GA is easy to use and capable of finding global optima, which can be used to improve the structure of optimization systems [23]. In this research, a hybrid proposed control technique that combines optimal SMC and PD compensation is presented.

The contributions of this study can be highlighted by the following points:

  • This study has proposed an expanded ADRC by adding a second SMC-LESO in combination with a PD controller LESO to produce a multilayered LESO. It is particularly efficient to use numerous LESOs within that ADRC framework when dealing with highly unpredictable nonlinear systems.

  • The controlled system's stability and global convergence properties have been verified according to Lyapunov's second approach

  • By choosing the best gains for the observer and sliding surface of the SMC technique the chattering phenomenon is minimized.

The rest of this paper is laid out as follows. In the second part, the particular human exoskeleton under investigation is described. The proposed control strategy is clearly described in Section 3. The fourth part incorporates simulations and analysis of results utilizing the proposed approach. In the concluding section, conclusions are formed.

2 Exoskeleton mathematical representation

Figure 1 illustrates how the grouped human-exoskeleton model is realized as a rigid body system represented by a kinematic plant with the objective of studying the dynamical behavior of the human-exoskeleton system without overhyping by taking into account the flexible components included in the patient's psyche and mechanical linkages. A numerical equation of the dynamics of the system depending on the inertia concept can be created using Euler-Lagrangian equations of rigid body dynamics. For helping knee flexion and extension workouts, it designed a stationary 1-DOF exoskeleton as [8, 14, 24, 25]
Jθ¨=τgcosθAsignθ˙Bθ˙+T+τh
Where (θ) joint angle, (T) exoskeleton torque, (τh) human torque, and (τg) gravitational torque of the shank/foot section are all taken into account. Inertia, solid friction coefficient, and viscous friction coefficient are represented by (J), (A), and (B), respectively.
Fig. 1.
Fig. 1.

Simple 1-DOF exoskeleton leg

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2023.00546

3 Proposed controller

In systems engineering, disturbance rejection is just one of several conflicting control design purposes, such as command following, robustness stabilization, noisy sensitive, and so on; in reality, however, it is frequently the design aim that design engineers are thinking about. The traditional ADRC architecture is based on the idea of using an observer, such as an extended state observer ESO, to estimate the state of the system as well as the quantity of a complete perturbation occurring on the system at the same time [26]. ADRC is a newly designed control approach aimed at gaining an insight between theory and application [10]. It will be as simple to use as a typical PID control, with additional sophisticated features such as the ability to soften set point swings using a mixture of a rapid feature synthesis and nonlinear feedback. All unknown components of the controlled system, such as unidentified disturbances and process models, are viewed by ADRC as universal disturbances, and the controller is designed to ignore them. ESO [10] is a critical component of ADRC design. Return to Eq. (1) and substitute x1 and x2 for the variables θ and θ˙. As a state variable, this equation can be formulated as follows:
x˙1=x2
x˙2=f+bτ
Where, b=1/J and f is the term for uncertainty and highly nonlinear that is combined together and is given by:
1J[τgcos(x1)fvx2fssign(x2)+τh]
Rewrite Eq. (2) after added an extra state (x3) that represent the total disturbances:
x˙1=x2
x˙2=x3+boτ
x˙3=f˙
y=x1
The suggested observer dynamics structure of the exoskeleton modelling system in Eq. (4) is:
zˆ˙=Azˆ+Bτ+β(yyˆ)
yˆ=Czˆ
Where, zˆ=[zˆ1zˆ2zˆ3]T is the vectors of estimates of y, y˙, and f, respectively.
The above-mentioned observer is referred as the Linear ESO, and is referred to as the observer gain matrix. The pole-placement approach can be used to determine the components of the observer gain matrix. When properly planned and developed, the predicted observer states will match to those of the plant described by Eq. (4). The following characteristic equation can be generated using the pole-placement approach [10] and the extended state observer structure.
Q(s)=|sI(AβC)|=(s+ωo)3
The observe gain matrix can be calculated using the formula below:
β=[3ωo3ωo2ωo3]

Only the bandwidth ωo of LESO is necessary to determine the elements of the observer gain matrix. This easy tuning strategy, on the other hand, combines the performance and noise-sensitivity trade-offs. Other recent and effective optimization approaches, on the other hand, can be used to tune these parameters [1922]. The tracking differentiator (TD) or Profile Generator (PG) is the second portion of the ADRC design, and it is used in work employing linear PG, which consists of the intended signal trajectory (r) and its derivative (r˙). The basic goal of TD is to get around the fixed point jump constraint.

Since the PD-type control law cannot match the needs of robustness and disturbance suppression for exoskeleton system with varying disturbances, the third part of ADRC is the control law. In this paper, two controllers are used: proportional-derivative controller (PD) and sliding mod control (SMC). As a result, with payload fluctuations, the closed-loop system can achieve the desired design aim [27]. A PD-type controller is typically implemented based on the aforementioned ESO, Eq. (5) [14, 24].
u=[Kp(rzˆ1)+Kd(r˙zˆ2)zˆ3]b
Let the PD controller output:-
uo=Kp(rzˆ1)+Kd(r˙zˆ2)
And rewrite Eq. (8) as:-
u=(uozˆ3)b
zˆ1 is the estimated feedback signal and zˆ2 is the derivative of zˆ1. The values of controller gains are given by kp=ωc2, kd=2ωc [28, 29] where ωc is the control loop bandwidth. In order to overcome the loss of disturbance rejection and robustness caused by the inability to fully adjust for the total disturbance f, a sliding mode term is introduced to the control law Equation (9) based on the prior arguments. Due to its resilience to external disturbances, SMC – a nonlinear control structure and well-robust control system – has been successfully applied in a wide range of engineering fields. The sliding surface is an important part of SMC since it dictates the planned state trajectories, which affects the system's stability and dynamics. The system under control is a second-order system, with the sliding surface (s) as follows:
s=e˙+μe
The sliding surface coefficient (μ) is design parameter and (e) is the error, where:
e=ry
The second part of SMC is the switching control:
Usw=Ksign(s)
The switching controller gain K is another design parameter. Substituting Eq. (10) into Eq. (12) to have
USM=Ksign(e˙+μe)
Now the control law can be included two controller parts (PD) and (SMC) so that:
C(s)=PDcontroller+SMcontroller
Eq. (9) becomes as:
u=(uozˆ3)+USMb
The tracking error derivative can be obtained. If the switching controller gain >[f(.)+|r¨|], then ee˙<0. The error will decrease to zero at a finite time. This condition for checking the stability of SMC using Lyapunov's second technique. Let derivative of Eq. (10)
s˙=e¨+ce˙
Substituting Eq. (2) and Eq. (11) into Eq. (15), and rewrite:
s˙=[r¨f(.)bu]+μe˙
or
s˙=zˆ3f(.)bKsign(s)
Since F=zˆ3f(.), rewrite Eq. (17) as:
s˙=FKtsign(s)
Where Kt=bK, the system will be stable in sense of Lyapunov if Kt>F because ss˙<0. The sliding condition is established if Kt>0. The proposed control law theorem, numerically, guarantees the operating of the system state trajectory onto sliding surface and its remaining in that position. The design parameters (K, μ) of SMC can be calculated by using PSO optimization method [15].

If one chooses the bandwidth of observer ωo to be equal to ωo=4ωc, then it easy to calculate the elements of observer matrix gains ( β1, β2, β3) according to Eq. (7). Assume that the observer gains were the same in both techniques (PD) and (SMC). Let wc=24.5rad/sec in the design. Figure 2 shows the structure diagram of ADRC with C(s) model, which can combine the two controller techniques and P(s) is the exoskeleton-human model.

Fig. 2.
Fig. 2.

Structure diagram of linear ADRC

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2023.00546

4 Simulation results and discussion

This part uses numerical simulations to validate the effectiveness of the designed controller. MATLAB's Simulink can be used to carry out the simulation results. Table 1 lists the important parameters of the human-exoskeleton system [24] as well as the control parameters.

Table 1.

Shows all system variables with observer tuning parameters

ParameterValue
Inertia (J)0.34kg.m2
Solid Friction Coefficient (A)1 N.m
Viscous Friction Coefficient (B)0.9 N.m.s./rad
Gravity Torque (τg)3.5N.m
Sliding Surface Coefficient μ0.015
Switching controller gain K7.53
Proportional gain Kp600
Derivative gain Kd49
Observer gain β1288
Observer gain β228,812
Observer gain β3941,192

Five categories of indices are taken into account to evaluate the performances of the controlled system in terms of error and control effort. These are the Integral of Absolute magnitude of Error (IAE), Integral Square Error (ISE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Integral Absolute control signal (IAU) were chosen as performance indices for comparison [9, 24, 30].To prove the advantages and superiority of the proposed SMC + PDADRC (Eq. (14)) over SMCADRC (Eq. (14) with uo=0) and PDADRC (Eq. (14) with USM=0) , two cases are considered, nominal case and disturbance case. The desired trajectory angle is set as a sine curve with a frequency of 1.57 Hz and with initial condition (−0.785 rad) and moving the knee to (−1.57 rad) for full flexion and to (0 rad) for full extension in this experiment.

  1. ASimulation Results with Nominal case

The track effectiveness of the suggested methodology is examined and compared to the SMCADRC and PDADRC approaches in the nominal case (τh=0) in this section. The simulation results for tracking the trajectory of the knee position and tracking error are shown in Figs 3 and 4. The proposed technique (SMC + PDADRC) responds faster than conventional controllers, resulting in lower tracking error. SMC + PDADRC compensates faster, as shown in the graph. Calculating (IAE), (ISE), (MAE), and (R.M.S.E) during the course of the tests and within 10 s, respectively, is shown in Table 2. Figure 5 depicts the control efforts required to investigate the torque (T) or u(t) for three control methods. The PDADRC control method produces the least decrease in chattering in the process variable index when compared to all other controllers' approaches, according to experiment data (IAU). Because of the sign function's effect, even though SMC + PDADRC and SMCADRC response torques have higher chattering, SMC + PDADRC has less chattering than SMCARC due to the derivative effect of PD mixing with SMC, which has no effect on knee position tracking in general.

Fig. 3.
Fig. 3.

The monitoring path of the knee-joint position with nominal case

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2023.00546

Fig. 4.
Fig. 4.

Tracking error trajectories of the knee for three controllers with nominal case

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2023.00546

Table 2.

Measures of effectiveness for three controllers with nominal case

IndicesPDADRCSMCADRCSMC + PDADRC
R.M.S.E (rad)0.00380.00230.0017
IAE (rad)0.03100.01210.0019
ISE (rad)0.000140.000050.00002
MAE (rad)0.00310.00120.0001
IAU (N.m)28.6310079.58
Fig. 5.
Fig. 5.

Comparison of the three controllers for required torque with nominal case

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2023.00546

  1. BSimulation Results with constant load disturbance case

A second simulation is run with a constant disturbance of 0.5 N.m. at time t = 2 s to test the performance of all controllers with payload condition. Figure 6 depicts the performance of the three controllers (Real vs. Desired output). Figure 7 shows the difference in knee position between the desired and actual settings for the same controllers. The SMC + PDADRC can adapt for load disturbances fast and return to the ideal trajectory in less time than the PDADRC and SMCADRC (0.35 s). When a load disturbance is added, the SMC + PDADRC technique exhibits reduced oscillation, but the PDADRC and SMCADRC methods exhibit larger oscillation while maintaining a steady tracking trajectory. Under the aforementioned simulated conditions, SMC + PDADRC is always stable and has the best tracking accuracy. As a result, the proposed method is more robust to variations in load weight. Figure 8 shows the torque control efforts for three controllers. The SMCADRC and SMC + PDADRC demand more torque and have a higher index (IAU) than the PD-ADRC. Table 3 contains a list of all performance indices. The results show that as the system nears steady state, the proposed sliding gain Eq. (13) converges to near zero, preventing chattering.

Fig. 6.
Fig. 6.

The monitoring path of the knee-joint position with constant load disturbance

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2023.00546

Fig. 7.
Fig. 7.

Tracking error trajectories of the knee for three controllers with constant load disturbance

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2023.00546

Fig. 8.
Fig. 8.

Comparison of the three controllers for required torque with constant load disturbance

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2023.00546

Table 3.

Measures of effectiveness for three controllers with constant load disturbance case

IndicesPDADRCSMCADRCSMC + PDADRC
R.M.S.E (rad)0.05840.05680.0547
IAE (rad)0.12260.11110.1073
ISE (rad)0.03390.03210.0298
MAE (rad)0.01230.01120.0108
IAU (N.m)25.1199.9479.17
  1. CSimulation Results with noise disturbance case

In fact, when a knee trajectory traveling along a predefined path encounters a sudden shock disturbance, such as the human effect (τh0), another common scenario occurs. Because the knee exoskeleton may be impacted while in motion, a perturbation is added at t=1 and 2sec, which is a noise signal with a magnitude of ±0.03, which is used to simulate the impact disruption in real work. Figure 9 depicts the performance of the three controllers. Figure 10 shows the difference in knee position between the desired and actual settings for the same controllers. When compared to PDADRC and SMCDARC, which have R.M.S.E = 0.0067 and R.M.S.E = 0.0064, respectively, SMC + PDADRC can mitigate torque perturbation more quickly and, more crucially, SMC + PDADRC can drive the tracking error to converge to an acceptable range with R.M.S.E = 0.0028. Figure 11 shows the control torque efforts for three controllers. Although the SMC for both ADRC and PDADRC requires more torque and has a higher index (IAU), it can also be seen that the PDADRC requires more control signal at t=1 and 2sec because the variation between these limit times changes abruptly. All performance indices are listed in Table 4.

Fig. 9.
Fig. 9.

The monitoring path of the knee-joint position with noise disturbance

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2023.00546

Fig. 10.
Fig. 10.

Tracking error trajectories of the knee for three controllers with noise disturbance

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2023.00546

Fig. 11.
Fig. 11.

Comparison of the three controllers for required torque with noise disturbance

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2023.00546

Table 4.

Measures of effectiveness for three controllers with noise disturbance case

IndicesPDADRCSMCADRCSMC + PDADRC
R.M.S.E (rad)0.00670.00640.0028
IAE (rad)0.03880.02760.0124
ISE (rad)0.000440.000410.00030
MAE (rad)0.00390.00280.0012
IAU (N.m)31.9210080.03

5 Conclusions

A SMC + PDADRC approach is proposed in this paper for controlling a single knee joint rehabilitation exoskeleton. Several experiments have been conducted in the actual exoskeleton system, including angle trajectory tracking under various conditions, initially the nominal case, and then various external disturbances. The results suggest that the revised approach can track the angle of the knee joint well. The mistakes of SMC + PDADRC are greatly decreased when compared to the original PDADRC and SMCADRC in various instances, with MAE reductions of more than 69% and RMSE reductions of more than 58% in these calculations for the worst scenario (noise disturbance). The position transient can swiftly recover to normal and accomplish accurate tracking when exposed to external disturbances. In general, all controller approaches demonstrate stability as measured by the error trajectory. As a result, for exoskeletons with changing payloads or disturbances, the objectives for reliability and perturbation elimination cannot be satisfied by PD-type control laws. In order to overcome the loss of disturbance rejection and robustness caused by a failure to fully adjust for the whole disturbance, a sliding mode term is introduced to the control rule. The fundamental disadvantage of SMC is chattering, which is eliminated in this study by finding the optimal coefficients of (K) and (μ). The future work is expected to include the proposed controller for 2-DOF of motion, which is applied to the Knee-Hip Exoskeleton system. Other control techniques could be suggested to conduct a comparison study for this medical application [3134].

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    T. Luay, “Optimal tuning of linear quadratic regulator controller using ant colony optimization algorithm for position control of a permanent magnet dc motor,” Iraqi J. Comput. Commun. Control Syst. Eng., vol. 20, no. 3, pp. 2941, 2020. https://doi.org/10.33103/uot.ijccce.20.3.3.

    • Search Google Scholar
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    S. Got, M. Lee, and M. Park, “Fuzzy-sliding mode control of a polishing robot based on genetic algorithm,” J. Mech. Sci. & Technol., vol. 15, pp. 580591, 2001. https://doi.org/10.1007/BF03184374.

    • Search Google Scholar
    • Export Citation
  • [24]

    N. A. Alawad, A. J. Humaidi, A. S. M. Al-Obaidi, and A. S. Alaraji, “Active disturbance rejection control of wearable lower-limb system based on reduced ESO,” Indo. J. Sci. Technol., vol. 7, no. 2, pp. 203218, 2022.

    • Search Google Scholar
    • Export Citation
  • [25]

    M. Saber and E. Djamel, “A robust control scheme based on sliding mode observer to drive a knee-exoskeleton,” Asian J. Control, vol. 21, no. 1, pp. 439455, 2019. https://doi.org/10.1002/asjc.1950.

    • Search Google Scholar
    • Export Citation
  • [26]

    A. J. Humaidi, H. M. Badr and A. R. Ajil, “Design of active disturbance rejection control for single-link flexible joint robot manipulator,” in 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania, 2018, pp. 452457. https://doi.org/10.1109/ICSTCC.2018.8540652.

    • Search Google Scholar
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  • [27]

    W. Fan, L. Peng, J. Feng, L. Bo, P. Wei, G. Min, and X. Meilin, “Sliding mode robust active disturbance rejection control for single-link flexible arm with large payload variations,” Electronics, vol. 10, pp. 115, 2021. https://doi.org/10.3390/electronics10232995.

    • Search Google Scholar
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    X. Chen, D. Li, Z. Gao, and C. Wang, “Tuning method for second-order active disturbance rejection control,” Proceedings of the 30th Chinese Control Conference, 2011, pp. 63226327.

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    N. Ahmed, A. Humaidi, and A. Sabah, “Clinical trajectory control for lower knee rehabilitation using ADRC method,” J. Appl. Res. Technol., vol. 20, no. 5, pp. 576583, 2022.

    • Search Google Scholar
    • Export Citation
  • [30]

    L. Domingos, F. André, F. Dantas, d. Almeida, A. Junio, M. Edgard, “Comparison of controller's performance for a knee joint model based on functional electrical stimulation input,” International IEEE/EMBS Conference on Neural Engineering (NER) Virtual Conference, May 4–6, 2021.

    • Search Google Scholar
    • Export Citation
  • [31]

    A. J. Humaidi, S. K. Kadhim, and A. S. Gataa, “Optimal adaptive magnetic suspension control of rotary impeller for artificial heart pump,” Cybernetics and Systems, vol. 53, no. 1, pp. 141167, 2022. https://doi.org/10.1080/01969722.2021.2008686

    • Search Google Scholar
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  • [32]

    S. M. Mahdi, N. Q. Yousif, A. A. Oglah, M. E. Sadiq, 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, supp. 176, pp. 119, 2022. https://doi.org/10.3390/act11070176.

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    S. S. Husain, M. Q. Kadhim, A. S. M. Al-Obaidi, A. F. Hasan, A. J. Humaidi, and D. N. Al-Husaeni, “Design of robust control for vehicle steer-by-wire system,” Indo. J. Sci. Technol., vol. 8, no. 2, pp. 197216, 2023.

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    A. Al-Jodah, S. J. Abbas, A. F. Hasan, A. J. Humaidi, A. S. M. Al-Obaidi, A. A. AL-Qassar, and R. F. Hassan, “PSO-based optimized neural network PID control approach for a four wheeled omnidirectional mobile robot,” Int. Rev. Appl. Sci. Eng., vol. 14, no. 1, pp. 5867, 2023.

    • Search Google Scholar
    • Export Citation
  • [1]

    Z. A. Waheed, A. J. Humaidi, M. E. Sadiq, A. A. Al-Qassar, A. F. Hasan, A. Q. Al-Dujaili, A. R. Ajel, and S. J. Abbas, “Control of Elbow Rehabilitation System Based on Optimal-Tuned Backstepping Sliding Mode Controller,” J. Eng. Sci. Technol., vol. 18, no. 1, pp. 584603, 2023.

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    N. A. Alawad, A. J. Humaidi, A. S. Al-Araji, et al., “Improved active disturbance rejection control for the knee joint motion model,” Math. Model. Eng. Probl., vol. 9, no. 2, pp. 477483, 2022.

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    Z. Wei and S. Aiguo, “Active motion control of a knee exoskeleton driven by antagonistic pneumatic muscle actuators,” Actuators, vol. 9, pp. 114, 2020. https://doi.org/10.3390/act9040134.

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    R. Baud, A. Manzoori, A. Ijspeert, et al., “Review of control strategies for lower-limb exoskeletons to assist gait,” J. Neuroengineering Rehabil., vol. 18, no. 119, pp. 134, 2021. https://doi.org/10.1186/s12984-021-00906-3.

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    S. Shabnam and C. Silvia, “An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation,” 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), Edinburgh, UK, 2017, pp. 13111318. https://doi.org/10.1109/ISIE.2017.8001435.

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    C. Caulcrick, W. Huo, E. Franco, S. Mohammed, W. Hoult, and R. Vaidyanathan, “Model predictive control for human-centred lower limb robotic assistance,” IEEE Trans. Med. Robot. Bionics, vol. 3, no. 4, pp. 980991, Nov. 2021. https://doi.org/10.1109/TMRB.2021.3105141.

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    W. R. Abdul-Adheem, A. T. Azar, K. I. Ibraheem, and A. J. Humaidi, “Novel active disturbance rejection control based on nested linear extended state observers,” Appl. Sci., vol. 10, supp. 4069, pp. 127, 2020. . https://doi.org/10.3390/app10124069.

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    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. .

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    W. R. Abdul-Adheem, I. K. Ibraheem, A. J. Humaidi, and A. T. Azar, “Model-free active input–output feedback linearization of a single-link flexible joint manipulator: an improved active disturbance rejection control approach,” Meas. Control, vol. 54, nos 5–6, pp. 856871, 2021. https://doi.org/10.1177/0020294020917171.

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    E. Zhu, J. Pang, N. Sun, H. Gao, Q. Sun, and Z. Chen, “Airship horizontal trajectory tracking control based on Active Disturbance Rejection Control (ADRC),” Nonlinear Dyn., vol. 75, no. 75, pp. 725734, 2013. https://doi.org/10.1007/s11071-013-1099-x.

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    D. Zeng, Z. Yu, L. Xiong, Z. Fu, Z. Li, P. Zhang, B. Leng, and F. Shan, “HFO-LADRC lateral motion controller for autonomous road sweeper,” Sensors, vol. 20, pp. 127, 2020. https://doi.org/10.3390/s20082274.

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    Y. Zhao, Z. Zhao, B. Zhao, and W. Li, “Active disturbance rejection control for manipulator flexible joint with dynamic friction and uncertainties compensation,” 2011 Fourth International Symposium on Computational Intelligence and Design, 2011, pp. 248251. https://doi.org/10.1109/ISCID.2011.164.

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    A. J. Humaidi, H. M. Badr and A. H. Hameed, “PSO-based active disturbance rejection control for position control of magnetic levitation system,” in 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT), Thessaloniki, Greece, 2018, pp. 922928. https://doi.org/10.1109/CoDIT.2018.8394955.

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    P. Rafael, I. Lázaroab, and C. Isaac, “Adaptive sliding-mode controller of a lower limb mobile exoskeleton for active rehabilitation,” ISA Trans., vol. 109, no. March, pp. 218228, 2021. https://doi.org/10.1016/j.isatra.2020.10.008.

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    B. Mahdieh, N. Saeede, H. Mojtaba, and M. Vahid, “Sliding mode control of an exoskeleton robot for use in upper-limb rehabilitation,” 2015 3rd RSI International Conference on Robotics and Mechatronics, (ICROM), 07–09 October, 2015, pp. 694701. https://doi.org/10.1109/ICRoM.2015.7367867.

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    L. Yi, D. Zhi-jiang, W. Wei-dong, and D. Wei, “Robust sliding mode control based on GA optimization and CMAC compensation for lower limb exoskeleton,” Appl. Bionics Biomech., vol. 2016, pp. 113, 2016. https://doi.org/10.1155/2016/5017381.

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    B. Ibrahim, R. Ngadengon, and M. Ahmad, “Genetic algorithm optimized integral sliding mode control of a direct drive robot arm,” Proceedings of the International Conference on Control, Automation and Information Sciences (ICCAIS '12), Hochi Minh City, Vietnam, November, 2012, pp. 328333. https://doi.org/10.1109/ICCAIS.2012.6466612.

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    F. Hassan and L. Rashad, “Particle swarm optimization for adapting fuzzy logic controller of SPWM inverter fed 3-phase IM,” Eng. Technol. J., vol. 29, no. 14, pp. 29122925, 2011.

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    A. A. Al-Qassar, A. I. Abdulkareem, A. F. Hasan, A. J. Humaidi, K. I. Ibraheem, A. T. Azar, and A. H. Hameed, “Grey-wolf optimization better enhances the dynamic performance of roll motion for tail-sitter VTOL aircraft guided and controlled by STSMC,” J. Eng. Sci. Technol., vol. 16, no. 3, pp. 19321950, 2021.

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  • [22]

    T. Luay, “Optimal tuning of linear quadratic regulator controller using ant colony optimization algorithm for position control of a permanent magnet dc motor,” Iraqi J. Comput. Commun. Control Syst. Eng., vol. 20, no. 3, pp. 2941, 2020. https://doi.org/10.33103/uot.ijccce.20.3.3.

    • Search Google Scholar
    • Export Citation
  • [23]

    S. Got, M. Lee, and M. Park, “Fuzzy-sliding mode control of a polishing robot based on genetic algorithm,” J. Mech. Sci. & Technol., vol. 15, pp. 580591, 2001. https://doi.org/10.1007/BF03184374.

    • Search Google Scholar
    • Export Citation
  • [24]

    N. A. Alawad, A. J. Humaidi, A. S. M. Al-Obaidi, and A. S. Alaraji, “Active disturbance rejection control of wearable lower-limb system based on reduced ESO,” Indo. J. Sci. Technol., vol. 7, no. 2, pp. 203218, 2022.

    • Search Google Scholar
    • Export Citation
  • [25]

    M. Saber and E. Djamel, “A robust control scheme based on sliding mode observer to drive a knee-exoskeleton,” Asian J. Control, vol. 21, no. 1, pp. 439455, 2019. https://doi.org/10.1002/asjc.1950.

    • Search Google Scholar
    • Export Citation
  • [26]

    A. J. Humaidi, H. M. Badr and A. R. Ajil, “Design of active disturbance rejection control for single-link flexible joint robot manipulator,” in 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania, 2018, pp. 452457. https://doi.org/10.1109/ICSTCC.2018.8540652.

    • Search Google Scholar
    • Export Citation
  • [27]

    W. Fan, L. Peng, J. Feng, L. Bo, P. Wei, G. Min, and X. Meilin, “Sliding mode robust active disturbance rejection control for single-link flexible arm with large payload variations,” Electronics, vol. 10, pp. 115, 2021. https://doi.org/10.3390/electronics10232995.

    • Search Google Scholar
    • Export Citation
  • [28]

    X. Chen, D. Li, Z. Gao, and C. Wang, “Tuning method for second-order active disturbance rejection control,” Proceedings of the 30th Chinese Control Conference, 2011, pp. 63226327.

    • Search Google Scholar
    • Export Citation
  • [29]

    N. Ahmed, A. Humaidi, and A. Sabah, “Clinical trajectory control for lower knee rehabilitation using ADRC method,” J. Appl. Res. Technol., vol. 20, no. 5, pp. 576583, 2022.

    • Search Google Scholar
    • Export Citation
  • [30]

    L. Domingos, F. André, F. Dantas, d. Almeida, A. Junio, M. Edgard, “Comparison of controller's performance for a knee joint model based on functional electrical stimulation input,” International IEEE/EMBS Conference on Neural Engineering (NER) Virtual Conference, May 4–6, 2021.

    • Search Google Scholar
    • Export Citation
  • [31]

    A. J. Humaidi, S. K. Kadhim, and A. S. Gataa, “Optimal adaptive magnetic suspension control of rotary impeller for artificial heart pump,” Cybernetics and Systems, vol. 53, no. 1, pp. 141167, 2022. https://doi.org/10.1080/01969722.2021.2008686

    • Search Google Scholar
    • Export Citation
  • [32]

    S. M. Mahdi, N. Q. Yousif, A. A. Oglah, M. E. Sadiq, 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, supp. 176, pp. 119, 2022. https://doi.org/10.3390/act11070176.

    • Search Google Scholar
    • Export Citation
  • [33]

    S. S. Husain, M. Q. Kadhim, A. S. M. Al-Obaidi, A. F. Hasan, A. J. Humaidi, and D. N. Al-Husaeni, “Design of robust control for vehicle steer-by-wire system,” Indo. J. Sci. Technol., vol. 8, no. 2, pp. 197216, 2023.

    • Search Google Scholar
    • Export Citation
  • [34]

    A. Al-Jodah, S. J. Abbas, A. F. Hasan, A. J. Humaidi, A. S. M. Al-Obaidi, A. A. AL-Qassar, and R. F. Hassan, “PSO-based optimized neural network PID control approach for a four wheeled omnidirectional mobile robot,” Int. Rev. Appl. Sci. Eng., vol. 14, no. 1, pp. 5867, 2023.

    • Search Google Scholar
    • Export Citation
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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

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    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

    Eric A. GRULKE University of Kentucky Lexington United States

    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

    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

    Balázs NAGY Budapest University of Technology and Economics Budapest Hungary

    Husam S. NAJM Rutgers University New Brunswick United States

    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

    Antal PUHL (1950–2023) 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

International Review of Applied Sciences and Engineering
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2022  
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9
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0.235
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Engineering (miscellaneous) (Q3)
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Materials Science (miscellaneous) Q3)
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Scopus
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0.686

2021  
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7
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Scopus  
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Scopus
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0,457

2020  
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5
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0,165
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Scopus
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102/116=0,9
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General Engineering 205/297 (Q3)
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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
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0,229
Scimago
Quartile Score
Engineering (miscellaneous) Q2
Environmental Engineering Q3
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Management Science and Operations Research Q4
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Scopus
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46/81=0,6
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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
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International Review of Applied Sciences and Engineering
Language English
Size A4
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Foundation
2010
Volumes
per Year
1
Issues
per Year
3
Founder Debreceni Egyetem
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Address
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ISSN 2062-0810 (Print)
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