A Novel SMMS Teleoperation Control Framework for Multiple Mobile Agents With Obstacles Avoidance by Leader Selection

Huang, Fanghao; Chen, Xuanlin; Chen, Zheng; Pan, Ya‐Jun

doi:10.1109/tsmc.2022.3199112

Cited by 19 publications

(1 citation statement)

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“…In order to improve the stability and transparency of the underwater teleoperation system, force feedback is introduced into the underwater teleoperation system [8]. The force signal sensed by the end of the underwater manipulator is transmitted to the master manipulator through the force sensor at the end, so that the operator can perceive the interactive force of the underwater working environment, which improves the operator's sense of presence during underwater operations, reduces work errors, and improves the efficiency of underwater operations.…”

Section: Introductionmentioning

confidence: 99%

The Adaptive Bilateral Control of Underwater Manipulator Teleoperation System with Uncertain Parameters and External Disturbance

Zhang,

Xia,

et al. 2024

Electronics

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A novel self-adaptive bilateral control strategy is introduced to manage uncertainties inherent in the teleoperation of an underwater manipulator system effectively. In response to uncertainties stemming from both the mathematical model and external disturbances, our approach offers innovative solutions. Firstly, to address uncertainties in the master model parameters, we propose a reference adaptive impedance control based on a nominal model. This control strategy dynamically adjusts the reference position of the desired model, leveraging adaptive control laws to compensate for model uncertainties. Additionally, to tackle uncertainties specific to the slave manipulator, we employ adaptive compensation using radial basis function (RBF) networks. Our unique combination of sliding mode variable structure controllers and robust adaptive controllers aims to mitigate approximation errors, ensuring precise tracking of the master manipulator’s position by the slave manipulator. By employing Lyapunov function analysis, we demonstrate the system’s superior tracking performance and global stability, with assured asymptotic convergence for force–position tracking. Through comprehensive experimentation, our results showcase the exceptional force–position tracking capabilities of the overall control system, even under challenging conditions of model uncertainties and external disturbances. Moreover, our system exhibits remarkable stability, reliability, and robustness, underscoring the effectiveness of our proposed adaptive control approach.

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Section: Introductionmentioning

confidence: 99%