Disturbance rejection and performance enhancement of perturbed tri-stable energy harvesters by adaptive finite-time disturbance observer

Fang, Shitong; Padar, Naser; Mirzaei, Mojtaba; Zhou, Shengxi; Liao, Wei-Hsin

doi:10.1007/s10409-022-21535-x

Cited by 8 publications

(1 citation statement)

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“…For a chaotic dynamical system, the sensitivity dependence on initial conditions (SDIC) was first discovered by Poincaré [38], and then this phenomenon was discovered once again by Lorenz [32] with a more familiar name "butterfly-effect". Due to the SDIC, a very weak, small-scale disturbance of the initial condition will give rise to a huge deviation of numerical solution of the chaotic system after a long enough temporal interval [9,16,45,50]. Furthermore, it was found that a chaotic dynamical system not only has the sensitivity dependence on initial conditions (SDIC) but also possesses the sensitivity dependence on numerical algorithms (SDNA), as reported by Lorenz [33,34].…”

Section: Introductionmentioning

confidence: 96%

A Self-Adaptive Algorithm of the Clean Numerical Simulation (CNS) for Chaos

Liao¹

2023

AAMM

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The background numerical noise ε 0 is determined by the maximum of truncation error and round-off error. For a chaotic system, the numerical error ε(t) grows exponentially, say, ε(t) = ε 0 exp(κ t), where κ > 0 is the so-called noise-growing exponent. This is the reason why one can not gain a convergent simulation of chaotic systems in a long enough interval of time by means of traditional algorithms in double precision, since the background numerical noise ε 0 might stop decreasing because of the use of double precision. This restriction can be overcome by means of the clean numerical simulation (CNS), which can decrease the background numerical noise ε 0 to any required tiny level. A lot of successful applications show the novelty and validity of the CNS. In this paper, we further propose some strategies to greatly increase the computational efficiency of the CNS algorithms for chaotic dynamical systems. It is highly suggested to keep a balance between truncation error and round-off error and besides to progressively enlarge the background numerical noise ε 0 , since the exponentially increasing numerical noise ε(t) is much larger than it. Some examples are given to illustrate the validity of our strategies for the CNS.

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Section: Introductionmentioning

confidence: 96%

A Self-Adaptive Algorithm of the Clean Numerical Simulation (CNS) for Chaos

Liao¹

2023

AAMM

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MoS₂ Nanoflakes based Kilohertz Electrochemical Capacitors

Bal,

Baranwal,

Lin

et al. 2023

Batteries & Supercaps

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An ultra‐fast electrochemical capacitor (EC) designed for efficient ripple current smoothing was fabricated using vertically oriented MoS2 (VOM) nanoflakes deposited on freestanding carbonized cellulose (CC) sheets as electrodes. The daily used cellulose tissue sheets were transformed into electrode scaffolds through a rapid pyrolysis process within a preheated furnace, on which VOM nanoflakes were formed in a conventional hydrothermal process. With these ~10 µm thick VOM‐CC electrodes, ultrafast ECs with tunable frequency response and specific capacitance density were fabricated. The ECs with a cell‐level areal capacitance density of 0.8 mF/cm2 at 120 Hz were demonstrated for ripple current filtering from 60 Hz to 60 kHz. At a lower frequency response level, EC cell with a large capacitance density of 4.8 mF/cm2 was also demonstrated. With the facile and easily scaled up process to producing the nanostructured electrode, the miniaturized VOM‐CC based ECs have the potential to substitute the bulky aluminum electrolytic capacitors for current smoothing and pulse power applications.

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Ultra-chaos of square thin plate in low Earth orbit

Zhang,

Yang,

Liao

2024

Acta Mech. Sin.

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Disturbance rejection and performance enhancement of perturbed tri-stable energy harvesters by adaptive finite-time disturbance observer

Cited by 8 publications

References 64 publications

A Self-Adaptive Algorithm of the Clean Numerical Simulation (CNS) for Chaos

A Self-Adaptive Algorithm of the Clean Numerical Simulation (CNS) for Chaos

MoS₂ Nanoflakes based Kilohertz Electrochemical Capacitors

Ultra-chaos of square thin plate in low Earth orbit

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Disturbance rejection and performance enhancement of perturbed tri-stable energy harvesters by adaptive finite-time disturbance observer

Cited by 8 publications

References 64 publications

A Self-Adaptive Algorithm of the Clean Numerical Simulation (CNS) for Chaos

A Self-Adaptive Algorithm of the Clean Numerical Simulation (CNS) for Chaos

MoS2 Nanoflakes based Kilohertz Electrochemical Capacitors

Ultra-chaos of square thin plate in low Earth orbit

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Product

Resources

About

MoS₂ Nanoflakes based Kilohertz Electrochemical Capacitors