Predictive Control Based on Nonlinear Observer for Muscular Force and Fatigue Model

Bakir, Toufik; Bonnard, Bernard; Othman, Sami

doi:10.23919/acc.2018.8430962

Cited by 2 publications

(4 citation statements)

References 12 publications

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“…Definition 2.5. The previous model, where (4) drives the Ca 2+ -concentration C N , is controlled by the FES-input and (9)-(12) drive the evolution of the force-fatigue variables, is called the Marion et al (non-isometric model).…”

Section: 1mentioning

confidence: 99%

“…Sensitivity analysis of the force response to the parameters. Since the dynamics is complex, a model reduction can be crucial for numeric purpose and we introduce the following approach to study the dynamics of the force response with respect to parameters or fatigue variables vs numeric approach [4]. This approach is fitted in an Hamiltonian frame and can be numerically handled using the Hampath software [14].…”

Section: Optimal Control Problem Consider the Smooth Control Systemẋmentioning

confidence: 99%

“…In fine, this will lead in the future to adaptative optimization algorithms, see [18,4] for preliminary results in this direction in the isometric case. In this article, we concentrate to (open loop) optimal control in relation with the problem of maximizing the force response to a single pulse train or to produce a reference force for a sequence of stimulation trains, this is the case for the non-isometric Marion et al model.…”

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confidence: 99%

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Geometric optimal techniques to control the muscular force response to functional electrical stimulation using a non-isometric force-fatigue model

Bonnard¹,

Rouot²

2021

JGM

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A recent force-fatigue parameterized mathematical model, based on the seminal contributions of V. Hill to describe muscular activity, allows to predict the muscular force response to external electrical stimulation (FES) and it opens the road to optimize the FES-input to maximize the force response to a pulse train, to track a reference force while minimizing the fatigue for a sequence of pulse trains or to follow a reference joint angle trajectory to produce motion in the non-isometric case. In this article, we introduce the geometric frame to analyze the dynamics and we present Pontryagin types necessary optimality conditions adapted to digital controls, used in the experiments, vs permanent control and which fits in the optimal sampled-data control frame. This leads to Hamiltonian differential variational inequalities, which can be numerically implemented vs direct optimization schemes.

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Section: 1mentioning

confidence: 99%

Section: Optimal Control Problem Consider the Smooth Control Systemẋmentioning

confidence: 99%

mentioning

confidence: 99%

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Geometric optimal techniques to control the muscular force response to functional electrical stimulation using a non-isometric force-fatigue model

Bonnard¹,

Rouot²

2021

JGM

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“…There is a limited literature that used this model to design an optimized train of pulses to control the force level [6,7,8,9,1]. Our aim is to produce a more 80 TOUFIK BAKIR, BERNARD BONNARD AND JÉRÉMY ROUOT complete study of the problem in the framework of non-linear optimal control, using sampled-data controls.…”

mentioning

confidence: 99%

A case study of optimal input-output system with sampled-data control: Ding et al. force and fatigue muscular control model

Bakir¹,

Bonnard²,

Rouot³

2019

Networks &Amp; Heterogeneous Media

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The objective of this article is to make the analysis of the muscular force response to optimize electrical pulses train using Ding et al. force-fatigue model. A geometric analysis of the dynamics is provided and very preliminary results are presented in the frame of optimal control using a simplified inputoutput model. In parallel, to take into account the physical constraints of the problem, partial state observation and input restrictions, an optimized pulses train is computed with a model predictive control, where a non-linear observer is used to estimate the state-variables.

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Predictive Control Based on Nonlinear Observer for Muscular Force and Fatigue Model

Cited by 2 publications

References 12 publications

Geometric optimal techniques to control the muscular force response to functional electrical stimulation using a non-isometric force-fatigue model

Geometric optimal techniques to control the muscular force response to functional electrical stimulation using a non-isometric force-fatigue model

A case study of optimal input-output system with sampled-data control: Ding et al. force and fatigue muscular control model

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