An Extended State Convergence Architecture for Multilateral Teleoperation Systems

Farooq, Umar; El‐Hawary, Mohamed E.; Asad, Muhammad Usman; Luo, Jun

doi:10.1109/access.2017.2658347

Cited by 14 publications

(8 citation statements)

References 31 publications

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“…Then, the specificity (Sp), sensitivity (Se), accuracy (Ac) and kappa coefficient (KC) are exploited to assess the performance of classifiers. Among them, the definitions of Sp, Se, and Ac are [32]: In addition, the KC is engaged to measure the average performance of classifiers, and its definition is [33]:…”

Section: ) the Optimal Model Selectingmentioning

confidence: 99%

The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures

2020

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The morphological modeling methods are efficient in quantifying the change of arterial blood pressure (ABP) waves. The related works focus on minimizing the modeling error but ignore the classification related modeling expression in practical applications. In this study, we explored the optimal modeling method for ABP wave related classifications. Two types of conventional models, Gaussian or Lognormal kernel function mixtures, were employed to quantitively describe the change of ABP signals, and the parameters of different models were engaged to train the different classifiers by probabilistic neural network (PNN) and random forest (RF) for identifying the ABP waves by age, gender, and whether belonging to extreme bradycardia (EB) or extreme tachycardia (ET). Then, we defined some indexes about the performance of modeling and classifications as the references to compare the different models. The ABP signals of Fantasia and 2015 PhysioNet/CinC Challenge databases were exploited as the experimental data to select the optimal model. The modeling results show that the Lognormal kernel function mixtures have a lower error in ABP wave modeling. The two-sample Kolmogorov-Smirnov test (ks-test) results indicate that the parameters of all models are markedly different at a highly significant level (h = 1, p < 0.05) between different groups. The classification results show that the classifiers based on the four-Gaussian function model have the best performance with the average Kappa coefficients (KC) of 99.160 ± 0.123%, while the average KC for the classifiers of two-Lognormal function models is 97.585 ± 0.172%, which means there is excessive information redundancy in the classifications by the three and four kernel functions models. Considering some other indexes such as time consumption and RAM space, the 2 Lognormal function model has more potential in practical applications. INDEX TERMS Arterial blood pressure signal, optimal morphological model, Gaussian function, lognormal function, classification.

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Section: ) the Optimal Model Selectingmentioning

confidence: 99%

The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures

2020

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“…This paper proposes an improved version of extended state convergence architecture [22] through the use of disturbance observers. The proposal enables the existing architecture to handle systems' uncertainties by treating them as disturbances and compensates their effects to improve the tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Disturbance Observer-Based Extended State Convergence Architecture for Multilateral Teleoperation Systems, 1-10.

Asad¹,

Farooq²,

Dey³

et al. 2023

International Journal of Robotics and Automation

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In the existing extended state convergence architecture, k -master systems can control the motion of l-slave systems to perform a certain task in a remote environment. However, dependency of this control framework on systems' parameters leads to a degraded control performance in the presence of significant parameter variations. In this study, we have integrated extended state observers in extended state convergence architecture to counter the effect of uncertainties, which has resulted in a more practical architecture for multilateral teleoperation systems. In order to validate the proposed enhanced architecture, simulations are performed in MATLAB/Simulink environment by considering a symmetric (2 × 2) as well as asymmetric (2 × 1) teleoperation system. A comparative assessment with the existing state convergence architecture proves the superiority of the proposed architecture. In addition, hardware experimentation is carried out on Quanser QUBE-servo systems by setting up an asymmetric (1 × 2) teleoperation system in the QUARC environment.

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“…The use of multiple manipulators becomes essential in certain tasks due to the limitations of a bilateral teleoperation system. The authors Farooq et al (2017) and Asad et al (2021) have devised strategies to handle multiple systems which are required in applications such as industrial manufacturing processes, automotive assembling, rehabilitation (Culmer et al, 2010), surgical training, signal modification (Chebbi et al, 2005), and space missions. In the specialized literature, a vast amount of controller design techniques is reported for bilateral teleoperation systems.…”

Section: Introductionmentioning

confidence: 99%

An Improved Composite State Convergence Scheme with Disturbance Compensation for Multilateral Teleoperation Systems

ASAD¹,

GU²,

FAROOQ³

et al. 2022

STUD INFORM CONTROL

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Composite state convergence is a novel scheme applied for the bilateral control of a telerobotic system. The scheme offers an elegant design procedure and employs only three communication channels to establish synchronization between a single-master and a single-slave robotic system. This paper expands the capability of the composite state convergence scheme to accommodate any number of master and slave systems and proposes a disturbance observer-based composite state convergence architecture where k-master systems can cooperatively control l-slave systems in the presence of uncertainties. A systematic method is presented to compute the control gains while observer gains are determined in a standard way.To validate the proposed architecture, MATLAB simulations are performed on symmetric and asymmetric arrangements of single-degree-of-freedom teleoperation systems. Finally, experimental results are obtained using Quanser's Qube-Servo systems in QUARC/Simulink environment.

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An Extended State Convergence Architecture for Multilateral Teleoperation Systems

Cited by 14 publications

References 31 publications

The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures

The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures

Disturbance Observer-Based Extended State Convergence Architecture for Multilateral Teleoperation Systems, 1-10.

An Improved Composite State Convergence Scheme with Disturbance Compensation for Multilateral Teleoperation Systems

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