Enhanced energy harvesting using time-delayed feedback control from random rotational environment

This paper focuses on the stochastic analysis of a viscoelastic bistable energy harvesting system under colored noise and harmonic excitation, and adopts the time-delayed feedback control to improve its harvesting effciency. Firstly, to obtain the dimensionless governing equation of the system, the original bistable system is approximated as a system without viscoelastic term by using the stochastic averaging method of energy envelope, and then is further decoupled to derive an equivalent system. The credibility of the proposed method is validated by contrasting the consistency between the numerical and the analytical results of the equivalent system under different noise conditions. The influence of system parameters on average output power is analyzed, and the control effect of the time-delayed feedback control on system performance is compared. The output performance of the system is improved with the occurrence of stochastic resonance(SR). Therefore, the signal-to-noise ratio expression for measuring SR is derived, and the dependence of its SR behavior on different parameters is explored.

“…Therefore, according to (d/dH) ln T (H) ẋ2 t = ẋ2 −1 t , [53] the joint steady-state PDF of the equivalent system Eq. ( 14) can be obtained by the following derivation:…”

Section: Stochastic Responsementioning

confidence: 99%

Performance enhancement of a viscoelastic bistable energy harvester using time-delayed feedback control

Huang 黄,

Yang 杨,

Liu 刘

2024

“…[2] Under certain conditions (the combination of external periodic forces, noise and the nonlinearity of the system), an appropriate noise input maximizes the response of the nonlinear system to a weakly periodic signal, giving rise to a 'resonance' phenomenon. SR has been applied to many fields, including weak signal detection, [3] image processing, [4] biological systems, [5,6] energy harvesting, [7,8] neural networks, [9][10][11] mechanical fault diagnosis, [12,13] etc.…”

Section: Introductionmentioning

confidence: 99%

Logical stochastic resonance in a cross-bifurcation non-smooth system

Zhang 张,

Lei 雷

2024

This paper investigates logical stochastic resonance (LSR) in a cross-bifurcation non-smooth system driven by Gaussian colored noise. In this system a bifurcation parameter triggers a transition between monostability, bistability and tristability. Using the Novikov’s theorem and the unified colored noise approximation method, the approximate Fokker-Planck equation is obtained. Then we derive the generalized potential function and the transition rates to analyze the LSR phenomenon with numerical simulations. We simulate the logical operation of the system in the bistable and tristable regions respectively. We assess the impact of Gaussian colored noise on the LSR and discover that the reliability of logical response depends on noise strength and bifurcation parameter. Furthermore, it is found that the bistable region has a more extensive range of the parameter to produce reliable logic operation compared to the tristable region since the tristable region is more sensitive to noise than the bistable one.

“…The vibration energy harvester (VEH) can produce electric energy from ambient vibrations [1][2][3] to provide wireless sensors with sustaining energy. There are many types of energy harvesting techniques, such as piezoelectric, [4,5] electromagnetic, [6] etc.…”

Section: Introductionmentioning

confidence: 99%

Sparse identification method of extracting hybrid energy harvesting system from observed data

Sun¹,

Zeng²,

Yang³

2022

Hybrid energy harvesters under external excitation have complex dynamical behavior and the superiority of promoting energy harvesting efficiency. Sometimes, it is difficult to model the governing equations of the hybrid energy harvesting system precisely, especially under external excitation. Accompanied with machine learning, data-driven methods play an important role in discovering the governing equations from massive datasets. Recently, there are many studies of data-driven models done in aspect of ordinary differential equations and stochastic differential equations (SDEs). However, few studies are to discover the governing equations for hybrid energy harvesting system under harmonic excitation and Gaussian white noise (GWN). Thus, in this paper, a data-driven approach, with least square and sparse constraint, is devised to discover governing equations of the systems from observed data. Firstly, the algorithm processing and pseudo code are given. Then, the effectiveness and accuracy of the method are verified by taking two examples with harmonic excitation and GWN, respectively. For harmonic excitation, all coefficients of the system can be simultaneously learned. For GWN, we approximate the drift term and diffusion term by using Kramers-Moyal formulas, and separately learn the coefficients of the drift term and diffusion term. Cross-Validation (CV) and Mean-square error (MSE) are utilized to obtain the optimal number of iterations. Finally, the comparisons between true values and learned values are depicted to demonstrate that the approach is well utilized to obtain the governing equations for hybrid energy harvester under harmonic excitation and GWN.