Nonlinear soft tissue mechanics based on polytopic Tensor Product modeling

Takács, Árpád; Haidegger, Tamás; Galambos, Péter; Kuti, József; Rudas, Imre J.

doi:10.1109/sami.2016.7423009

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…This study extends the previous works of the authors, where a nonlinear soft tissue model was obtained based on the experimental investigation of the force response of beef liver specimens during tool-tissue interaction [12], amended with [13], a polytopic qLPV (quasi Linear Parameter Varying) model of the tissue dynamics that is-regarding its mathematical formalism-suitable for direct use of LMI-based control design methods. As a next step, a model-based force control scheme is presented, utilizing an off-the-shelf tooltissue interaction model.…”

Section: Related Worksupporting

confidence: 67%

“…Regarding the Polytopic TP Model of the nonlinear system described in (10) and (11), the detailed derivation of the model can be found in [13], rearranged in a way that considers the socalled error dynamics. The proposed qLPV model assumes that the control goal is the force control of the surgical instrument at the tissue surface contact.…”

Section: Polytopic Tp Model Of the Systemmentioning

confidence: 99%

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Polytopic model based interaction control for soft tissue manipulation

Takács

Kuti

Haidegger

et al. 2016

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Section: Related Worksupporting

confidence: 67%

Section: Polytopic Tp Model Of the Systemmentioning

confidence: 99%

Polytopic model based interaction control for soft tissue manipulation

Takács

Kuti

Haidegger

et al. 2016

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Self Cite

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Control of Diabetes Mellitus by Advanced Robust Control Solution

et al. 2019

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The current article investigates the possibilities of designing a robust control solution for a control problem related to type 1 diabetes mellitus. The proposed control methodology exploits linear parameter varying, linear matrix inequality, tensor product model transformation and extended Kalman filtering, four advanced control methods in order to guarantee hard safety control constraints for diabetic patients. In this research we have applied an extension of the minimal model to simulate the glucose-insulin dynamics and the glucose and insulin absorption of a diabetic patient. We have validated our results on numerical simulations by using realistic patient data. During the evaluation we have used randomized glucose intakes both in time and amount (as ''unfavorable'' disturbance signals). Furthermore, we did a long-term (30 days) assessment to confirm the stability of the proposed framework. The results have shown that the developed controller effectively intervenes into the process and provides appropriate control action by avoiding hypoglycemia thereby satisfying the predefined quantity and quality requirements.INDEX TERMS Extended Kalman filter, linear matrix inequality, linear parameter varying-based control, tensor product model transformation, type 1 diabetes mellitus.

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Extension of the Multi‐TP Model Transformation to Functions with Different Numbers of Variables

Bárányi

2018

Complexity

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The tensor product (TP) model transformation defines and numerically reconstructs the Higher-Order Singular Value Decomposition (HOSVD) of functions. It plays the same role with respect to functions as HOSVD does for tensors (and SVD for matrices). The need for certain advantageous features, such as rank/complexity reduction, trade-offs between complexity and accuracy, and a manipulation power representative of the TP form, has motivated novel concepts in TS fuzzy model based modelling and control. The latest extensions of the TP model transformation, called the multi- and generalised TP model transformations, are applicable to a set functions where the dimensionality of the outputs of the functions may differ, but there is a strict limitation on the dimensionality of their inputs, which must be the same. The paper proposes an extended version that is applicable to a set of functions where both the input and output dimensionalities of the functions may differ. This makes it possible to transform complete multicomponent systems to TS fuzzy models along with the above-mentioned advantages.

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Nonlinear soft tissue mechanics based on polytopic Tensor Product modeling

Abstract: Abstract-Achieving

Cited by 3 publications

References 19 publications

Polytopic model based interaction control for soft tissue manipulation

Polytopic model based interaction control for soft tissue manipulation

Control of Diabetes Mellitus by Advanced Robust Control Solution

Extension of the Multi‐TP Model Transformation to Functions with Different Numbers of Variables

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