Error Signal Differential Term Feedback Enhanced Variable Step Size FxLMS Algorithm for Piezoelectric Active Vibration Control

Li, Weiguang; Wang, Wei; Li, Bin; Yang, Zhichun

doi:10.1155/2020/8832467

Cited by 8 publications

(8 citation statements)

References 19 publications

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“…Gao and Xie proposed an improved VSLMS algorithm based on [14] and the algorithm idea in [17], which improved the anti-noise ability of the algorithm [18]. Combining the advantages of the above algorithms, Li et al proposed a modified DVSLMS algorithm that makes the current step not only related to the current error signal but also to the change rate of the error signal and verified its superiority in the AVC test of a piezoelectric cantilever beam [19]. However, its step in the steady state is easily affected by noise and fluctuates greatly, reducing the stability of the control algorithm.…”

Section: Introductionmentioning

confidence: 99%

Piezoelectric adaptive active vibration suppression for wind-tunnel model support sting

Li,

Yang,

Zhu

et al. 2024

Smart Mater. Struct.

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Due to flow separation and turbulence, the slender cantilever aircraft model support system is prone to low-frequency and large-amplitude resonance in the wind tunnel tests, resulting in a decrease in test data quality, a limited test envelope, and even threatening the safe operation of the wind tunnel. A piezoelectric active damping system based on the filtered-x least mean square algorithm is proposed to effectively suppress the vibration of the wind-tunnel model support sting. Firstly, a modified variable step least mean square algorithm is proposed to address the issue that the fixed-step algorithm limits each other in terms of convergence speed and steady-state error. Following that, a variable step filtered-x least mean square algorithm based on reference signal reconstruction is developed, and the corresponding feedback controller is designed to perform the ground tests of the piezoelectric active damping system for the model support sting. The experimental results show that the proposed algorithm has a faster convergence speed and lower steady-state error than the traditional algorithm, as well as strong anti-noise and adaptive control abilities that significantly improve the active vibration suppression effect of the wind-tunnel model support sting.

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Section: Introductionmentioning

confidence: 99%

Piezoelectric adaptive active vibration suppression for wind-tunnel model support sting

Li,

Yang,

Zhu

et al. 2024

Smart Mater. Struct.

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“…The adaptive filtered x least mean square (Fx-LMS) algorithm based on the LMS (Least Mean Square, LMS) algorithm has been widely used in vibration suppression of practical engineering objects such as ships, pipelines, aircraft vertical tail, and cantilever beams. [9][10][11][12] However, the Fx-LMS algorithm is influenced by the accuracy of secondary channel identification, and the conventional linear system identification method can hardly handle the nonlinear characteristics of the secondary channels, so the identification accuracy is limited, which affects the vibration damping effect. [13][14][15] In order to eliminate the effects of nonlinear factors and system noise, a frequency-domain-based filter-x least mean square adaptive algorithm was proposed, 16 but the frequency-domain control algorithm is computationally intensive and difficult to implement, and it is difficult to meet the requirements of modeling accuracy and control real-time at the same time.…”

Section: Introductionmentioning

confidence: 99%

Active vibration control using nonlinear auto-regressive neural network to identify secondary channel

Chun-sheng,

Xue-chun,

et al. 2023

Journal of Low Frequency Noise, Vibration and Active Control

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The power unit on board the ship generates periodic low-frequency vibration that affects the normal operation of the equipment on board, and the adaptive feedforward control algorithm can effectively suppress such harmful vibration noise. But the adaptive feedforward control algorithm needs to obtain the identification model of the secondary channels, and the frequency domain least squares method based on the linear Extended auto-regressive model (ARX) is difficult to obtain the identification model with nonlinear characteristics. The nonlinear auto-regressive model (NARX) adds nonlinear mapping layers to the topology of the ARX model to enhance the identification capability of the NARX model for complex systems. In this paper, a block diagram of the Fx-LMS feedforward control algorithm based on the NARX model is proposed, then the initial parameters of the NARX neural network are optimized using the Quantum Particle Swarm Optimization (QPSO) algorithm and the secondary channel is identified, and the identification results show that the accuracy of identifying the secondary channel using the NARX neural network is higher than that of the ARX model. The simulation and experimental results show that the vibration damping effect of the proposed method is better than the traditional Fx-LMS method for both single-line spectrum and multi-line spectrum periodic low-frequency disturbances, which provides a new method for the suppression of periodic low-frequency disturbances.

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“…Beijen et al (2018) presented a disturbance feedforward control strategy with internal isolator dynamics, for active vibration isolation systems. In the feedforward control, the Filterer-x least mean square (Fx-LMS) is also used mostly (Li et al, 2020). As the same, Lee et al (2020) applied the Fx-LMS in feedforward controller to minimize the airframe vibration of a MIMO (Multi Input and Multi Output) model.…”

Section: Introductionmentioning

confidence: 99%

Active vibration hybrid control strategy based on multi-DOFs piezoelectric platform

Pu,

Fu,

Wang

et al. 2023

Journal of Intelligent Material Systems and Structures

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With the increasing requirements for vibration isolation in multiple degrees of freedom (multi-DOFs), active control strategy is becoming more meaningful. However, the vibration isolation performance is limited by the time delay of feedback control, and cannot meet higher requirements. Therefore, this paper proposes a multi-DOFs active vibration hybrid control (AVHC) strategy based on a piezoelectric platform. The AVHC integrates the adaptive feedforward control based on the modified recursive least squares (MRLS) algorithm, and the feedback control based on the integral force feedback (IFF) algorithm. To achieve advanced response, the ground-based vibration signal is offset by the MRLS algorithm. To further reduce the coupling of multi-DOFs, the feedback and feedforward coordinates are fused through the matrix transformation, and the signals are linearly superimposed by the AVHC. The experimental results show that the AVHC can further reduce the resonance peaks of the three translational directions ( X/ Y/ Z) compared with the feedback (FB) control. The resonance peaks are reduced from 14.6 dB (FB) to 3.11 dB (AVHC), from 14.56 dB (FB) to 5.14 dB (AVHC), and from 12.44 dB (FB) to 3.78 dB (AVHC) in X/ Y/ Z directions, respectively. The attenuation rates are improved by 73.36%, 66.19%, and 63.10% in X/ Y/ Z directions, respectively.

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Error Signal Differential Term Feedback Enhanced Variable Step Size FxLMS Algorithm for Piezoelectric Active Vibration Control

Cited by 8 publications

References 19 publications

Piezoelectric adaptive active vibration suppression for wind-tunnel model support sting

Piezoelectric adaptive active vibration suppression for wind-tunnel model support sting

Active vibration control using nonlinear auto-regressive neural network to identify secondary channel

Active vibration hybrid control strategy based on multi-DOFs piezoelectric platform

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