Analysis and compensation of reference frequency mismatch in multiple-frequency feedforward active noise and vibration control system

Liu, Jinxin; Chen, Xuefeng; Yang, Liangdong; Gao, Jiawei

doi:10.1016/j.jsv.2017.06.005

Cited by 41 publications

(13 citation statements)

References 23 publications

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“…Furthermore, some computational based frequency control methods are: particle swarm optimisation (PSO) used and simulated on MATLAB [94], non‐linear benchmark method has been presented on MATLAB/Simulink for wind farms in [95], a novel narrowband noise and vibration control algorithm is proposed and results verify robustness and effectiveness of the algorithm [96].…”

Section: Vpp Grid Servicesmentioning

confidence: 99%

Transformation of microgrid to virtual power plant – a comprehensive review

Yavuz

Önen

Muyeen

et al. 2019

IET Generation, Transmission & Distribution

112

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Section: Vpp Grid Servicesmentioning

confidence: 99%

Transformation of microgrid to virtual power plant – a comprehensive review

Yavuz

Önen

Muyeen

et al. 2019

IET Generation, Transmission & Distribution

112

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“…Another approach was developed by Ziegler for active noise control (ANC) (Liu et al, 2017). It connects multiple adaptive notch filters in parallel structure.…”

Section: Introductionmentioning

confidence: 99%

Multi-frequency micro-vibration control with hybrid adaptive control algorithm

Fang

Zhu

et al. 2020

Transactions of the Institute of Measurement and Control

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As a popular adaptive algorithm for active vibration control (AVC), filtered-x least mean square (FxLMS) algorithm sometimes fails to suppress multiple narrowband disturbance. To control multi-frequency structure vibration, parallel-form FxLMS algorithm can be employed. However, the conventional parallel-form FxLMS does not perform well in the situation of disturbance frequency mismatch and wideband measurement noise. For this issue, the majority of the improvement approaches are based on adaptive frequency estimator. It increased the computational burden of the parallel-form FxLMS algorithm. Therefore, taking the advantage of feedback control, a hybrid adaptive control for AVC is proposed in this paper. And the “posterior” residual error is adopted for better robustness. The convergence analysis of the hybrid AVC algorithm is given. Co-simulations with ADAMS and SIMULINK are implemented to verify the effectiveness in micro-vibration control system. Some comparison experiments are implemented in a micro-vibration AVC experimental system for several typical disturbances. Experimental results confirm the feasibility and effectiveness of the proposed algorithm.

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“…With the development of computer, digital signal processing, and active vibration control technology [20][21][22][23][24][25][26][27][28][29], the SRS test with electrodynamic shaker has become the mainstream. Compared with the traditional shock test equipment, the electrodynamic shaker has the advantages of a simple setup, better controllability and repeatability, convenient adjustment, and high accuracy, which helps in easy realization of the control of SRS [4].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Time‐Domain Parameters Matching for Shock Response Spectrum and Its Experimental Validation in Active Vibration Control Systems

Liu

Yang

Zhang

et al. 2019

Shock and Vibration

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Shock response spectrum (SRS) test is a kind of vibration test that uses the principle of equivalent damage to simulate complex shock and vibration environment for evaluating product fragility. However, for a certain SRS, neither an analytical nor a unique inverse time-domain shock waveform (TSW) exists, making the SRS test a very challenging problem. Synthesis of a TSW using a group of wavelets with different frequencies, amplitudes, and phases is considered to be a very promising method. However, it is challenging to find an optimal group of wavelets since there are hundreds of optimization variables and objective functions. In this paper, a novel intelligent parameter matching method for TSW synthesis is proposed for the SRS test. A variable-weight method has been introduced to combine the effects of hundreds objective functions so that the optimization problem can be solved by using the genetic algorithm. The frequency response function of the shaking system has been taken into consideration in the calculation of the objective function, so the synthesized TSW can be applied directly to the SRS test. In the optimization process, normalized control factors have been formulated for the optimization variables so that they can be tuned in a reasonable small range to avoid irrationality and accelerating convergence in searching process. The effectiveness of the proposed method has been verified experimentally in two active vibration control systems, in which one contains a 500 N minishaker and the other contains a 1-ton shaking table. It can be seen from the experimental results that the proposed method can accurately synthesize the input TSW for the shaking system, where the output TSW and SRS can accurately meet the time- and frequency-domain specification.

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Analysis and compensation of reference frequency mismatch in multiple-frequency feedforward active noise and vibration control system

Cited by 41 publications

References 23 publications

Transformation of microgrid to virtual power plant – a comprehensive review

Transformation of microgrid to virtual power plant – a comprehensive review

Multi-frequency micro-vibration control with hybrid adaptive control algorithm

Intelligent Time‐Domain Parameters Matching for Shock Response Spectrum and Its Experimental Validation in Active Vibration Control Systems

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