Fractional order model reference adaptive control for anesthesia

Navarro-Guerrero, Gerardo; Tang, Yu

doi:10.1002/acs.2769

Cited by 23 publications

(12 citation statements)

References 46 publications

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“…(Extension of Barbalat's lemma [43]) Let f : R → R + be a function that is uniformly continuous, and t 0 I α t is given by the Riemann-Liouville integral with α ∈ (0, 1). If lim t→∞ t 0 I α t f (t) exists and is finite, then f (t) → 0, as t → ∞.…”

Section: Lemmamentioning

confidence: 99%

“…The overall control performances of the FOMRAC scheme [43] and the proposed FOCMRAC scheme are shown in Table 3.…”

Section: Inter-patient Robustnessmentioning

confidence: 99%

“…Due to some different properties between fractional-order integrals and integer-order integrals, it is not easy (but is possible) to use Barbalat's lemma in fractional-order systems. In this paper, we will use the extension of this lemma for fractional-order systems that was presented in reference [43].…”

Section: Lemmamentioning

confidence: 99%

“…Simulations tested the robustness of the control schemes against the intra-patient variability, inter-patient variability, disturbance, noise, and time delay. Also, this FOCMRAC design was compared with the scalar FOMRAC presented in [43].…”

Section: Simulationsmentioning

confidence: 99%

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Fractional-Order Closed-Loop Model Reference Adaptive Control for Anesthesia

Navarro-Guerrero

Tang

2018

Algorithms

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Abstract:The design of a fractional-order closed-loop model reference adaptive control (FOCMRAC) for anesthesia based on a fractional-order model (FOM) is proposed in the paper. This proposed model gets around many difficulties, namely, unknown parameters, lack of state measurement, inter and intra-patient variability, and variable time-delay, encountered in controller designs based on the PK/PD model commonly used for control of anesthesia, and allows to design a simple adaptive controller based on the Lyapunov analysis. Simulations illustrate the effectiveness and robustness of the proposed control.

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Section: Lemmamentioning

confidence: 99%

“…The overall control performances of the FOMRAC scheme [43] and the proposed FOCMRAC scheme are shown in Table 3.…”

Section: Inter-patient Robustnessmentioning

confidence: 99%

Section: Lemmamentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

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Fractional-Order Closed-Loop Model Reference Adaptive Control for Anesthesia

Navarro-Guerrero

Tang

2018

Algorithms

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“…We find in the literature different models described by fractional derivatives, like the SIR model [6,7,21,24,47,50,60,61], the SIR model with vaccination [17,36,58], the SIRC model [17], the SEIR model [23,51], the MSEIR model [8], etc. We also refer some applications to biomedical modelling [11,29,44,45,48].…”

Section: Introductionmentioning

confidence: 99%

An epidemiological MSEIR model described by the Caputo fractional derivative

Almeida

Cruz

Martins

et al. 2018

Int. J. Dynam. Control

105

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A fractional MSEIR model is presented, involving the Caputo fractional derivative. The equilibrium points and the basic reproduction number are computed. An analysis of the local asymptotic stability at the disease free equilibrium is given. Finally, using Matlab, a numerical simulation of the varicella outbreak among Shenzhen school children, China, is carried out.Mathematics Subject Classification 2010: 26A33, 92D30, 37N25 .

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Adaptive switching control of uncertain fractional systems: Application to Chua's circuit

Aghababa

2018

Adaptive Control & Signal

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SummarySince the introduction of fractional‐order differential equations, there has been much research interest in synthesis and control of oscillatory, periodic, and chaotic fractional‐order dynamical systems. Therefore, in this article, the problem of stabilization and control of nonlinear three‐dimensional perturbed fractional nonlinear systems is considered. The major novelty of this article is handling partially unknown dynamics of nonlinear fractional‐order systems, as well as coping with input saturation along the existence of model variations and high‐frequency sensor noises via just one control input. The method supposes no known knowledge on the upper bounds of the uncertainties and perturbations. It is assumed that the working region of the input saturation function is also unknown. After the introduction of a simple finite‐time stable nonlinear sliding manifold, an adaptive control technique is used to reach the system variables to the sliding surface. Rigorous stability discussions are adopted to prove the convergence of the developed sliding mode controller. The findings of this research are illustrated using providing computer simulations for the control problem of the chaotic unified system and the fractional Chua's circuit model.

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Fractional order model reference adaptive control for anesthesia

Cited by 23 publications

References 46 publications

Fractional-Order Closed-Loop Model Reference Adaptive Control for Anesthesia

Fractional-Order Closed-Loop Model Reference Adaptive Control for Anesthesia

An epidemiological MSEIR model described by the Caputo fractional derivative

Adaptive switching control of uncertain fractional systems: Application to Chua's circuit

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