Robust Adaptive Disturbance Attenuation

Jafari, Saeid; Ioannou, Pétros

doi:10.1007/978-3-030-74628-5_6

Cited by 1 publication

(4 citation statements)

References 57 publications

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“…Concurrently, Figure 4 showed a diminishing error to null levels, along a relatively high convergence rate ( \ 1 s), under the control law equation (32). In addition, Figure 3 showed a smooth and accurate estimated surgeon's intended motion path via the machine learning operation equation (8). This was also supported by the results of Figure 5, to which contact forces/moments showed to be smooth and continuous throughout all experiments.…”

Section: Resultssupporting

confidence: 62%

“…with varying degrees of elasticity that demanded achieving a desired dynamic behavior for the haptic's end-effector, using secondorder impedance mechanical system using by equation ( 6), characterized by a given damping, and stiffness, assisted in achieving this goal. Figures 2-7 detail the experimental results obtained for the state-feedbackbased adaptive modified function approximation technique (SFAT), under the control law equation (32), and for an intended surgeon's motion path equation (8), estimated using a machine learning technique, namely, the RBFNNs. Supported by Figures 3, 5, and 6, findings showed a smooth and effective operation.…”

Section: Resultsmentioning

confidence: 99%

“…Since Dx IHIPi 2 R n was properly formulated in the previous section, the aim is to express h d 2 R n in a way similar to equation (8). In this regard, let the regulator variables be defined by the following…”

Section: Model-based Controlmentioning

confidence: 99%

“…In the context of ARCs, one class has been extensively examined with the objective of attenuating unmodeled dynamics. 8 As part of its adaptive law, uncertainties are presumed to be linear in parameters (LIPs) in order to individually approximate each of them. Its robust law, however, acts to reduce external disturbances.…”

Section: Introductionmentioning

confidence: 99%

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Skill learning approach based on impedance control for spine surgical training simulators with haptic playback

Brahmi

Bojairami

Ghomam

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a robust adaptive impedance control based on the modified function approximation technique, and augmented with a novel integral nonsingular terminal sliding mode control, is proposed for a surgical haptic system. The haptic device is maneuvered by surgeons, whereby the mode of action, motion path, and haptic dynamics are rendered as unknowns. The inclusion of integral nonsingular terminal sliding mode control leverages the capability of ensuring consistent tracking of system’s trajectories performance, fast transient response, finite-time convergence, improved robustness, and reduced chattering. However, the modified function approximation technique strategy allows to estimate model’s dynamics regardless of the availability of dynamic uncertainties’ lower and upper bounds. In addition, an estimate of the intended motion mode and path is integrated in the evolved adaptive impedance control, via radial basis function neural network, permitting the haptic arm to track the target impedance model. Finally, using the haptic arm, controlled experimental cases and comparative study were done, after which the proposed surgical simulator adopted the results for real-time validation.

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Section: Resultssupporting

confidence: 62%

Section: Resultsmentioning

confidence: 99%

Section: Model-based Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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