The error of the secondary path model has a significant impact on the stability and convergence of the adaptive control algorithm, so an accurate secondary path model is required. However, because it is difficult to remove the influence of dense frequency interference, the identification process is usually undertaken while the primary vibration source is turned off and the background noise is low. However, in actual engineering, the vibration transfer characteristics of the secondary path are time-varying, and there is a large difference between the model and the actual working state secondary path, resulting in poor adaptive control effect or even divergence. In order to solve this problem, this paper firstly proposes a method based on the second-order time-frequency filtering method to identify the real part and imaginary part of the frequency response function of the secondary path with low computational complexity and high precision. Then, the fluctuation characteristics of real-time identification of real and imaginary parameters in the presence of dense frequency interference are analyzed. Finally, a real-time secondary path identification method against dense frequency interference is proposed, which removes the fluctuation of identification parameters caused by dense frequency interference, and obtains stable and high-precision real-time secondary path identification results. Through the research of this paper, a real-time secondary path identification method against dense frequency interference is provided, which can effectively improve the adaptive control performance of the vibration isolation device. This method not only has the advantages of high precision and low calculation amount, but also can adapt to the time-varying environment in actual engineering, and has high practical value and promotion prospect.
Key words
active-passive vibration isolation /
adaptive control /
real-time secondary path identification /
phase error, dense frequency interference
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
1 S.D. Snyder, C.H. Hansen. The effect of transfer function estimation errors on the filtered-x LMS algorithm [J]. IEEE Transactions on Signal Processing, 1994, SP-42(4): 950-953.
2 Ljung L. System identification: theory for the user, 2nd edn [M]. Prentice Hall PTR, Upper Saddle River, NJ, 1999.
3 C. Hansen, S. Snyder, et al. Active control of noise and vibration, 2nd edn [M]. CRC Press, London, 2012.
4 M. Zhang. An improved secondary path modeling method for active noise control [J]. IEEE Signal Processing Letters, 7: 73-74, 2000.
5 王冬青. 一类有色噪声干扰随机系统最小二乘递推辨识 [J]. 北京航空航天大学学报, 2008, 34(8): 935-939.
Wang dongqing. Least square based recursive identification for stochastic systems with colored noised [J]. Journal of Beijing University of Aeronautics and Astronautics, 2008, 34(8): 935-939.
6 R. Delega, G. Bernasconi, L. Piroddi. A novel cost-effective parallel narrowband ANC system with local secondary-path estimation [J]. Journal of Sound and Vibration, 2017, 401:311-325.
7 李彦,何琳,帅长庚等.多通道窄带Fx-Newton 时频算法及动力机械主动隔振实验[J]. 声学学报, 2015, 40(3): 391- 403.
LI Yan, HE Lin, SHUAI Zhanggeng. MIMO Fx-Newton narrowband algorithm and experiment of active vibration isolation on diesel generator[J]. ACTA ACUSTICA, 2015, 40(3): 391- 403.
8 C. Chang, S. Kuo, C. Huang. Secondary path modeling for narrowband active noise control systems [J]. Applied Acoustics, 2018, 131:154-164.
9 John R. Glover, JR. Adaptive noise canceling applied to sinusoidal interferences [J]. IEEE Trans. Acoustics, Speech, and Signal Processing, 1977, ASSP-25(6): 484-491.
10 赵佳锡, 何琳, 徐荣武. 管路脉动有源控制临频插值次级通路建模方法 [J]. 振动与冲击, 2020, 39(19): 101-106.
Zhao Jiaxi, He Lin, Xu Rongwu. .Secondary path modeling method in active control of pipe pulsation using adjacent frequencies interpolation[J]. Journal of Vibration and Shock, 2020, 39(19): 101-106.
11 赵洪亮. 选频有源噪声控制系统控制算法的理论与实验研究[D]. 中国科学院研究生院博士论文,2004.
Zhao Hongliang. Theoretical and experimental research on the control algorithm of frequency-selective active noise control system[D]. University of Chinese Academy of Science, 2004.
{{custom_fnGroup.title_en}}
Footnotes
{{custom_fn.content}}