一种改进的次级通道辨识算法
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摘要
为提高次级通道的辨识精度、减小辨识误差对自适应控制的影响,以横向滤波器作为估计模型,分别应用带遗忘因子的最小二乘递推算法和变步长最小均方算法来对横向滤波器的权系数进行了更新,并对两种算法的辨识精度和控制效果进行了对比。结果表明:变步长最小均方算法的性能优于带遗忘因子的最小二乘递推算法,但变步长算法仍存在收敛速度过慢、辨识残差较大的问题。为此,提出一种改进的变步长最小均方算法,并对其进行了实验验证。实验结果表明:改进之后的变步长最小均方算法的辨识精度满足控制要求,收敛速度较快。
In order to improve the identification accuracy of the secondary path and reduce the impact on adaptive control, the transversal filter is used as the estimation model in this article. The least squares recursive algorithm with forgetting factor and the least mean square algorithm with variable-step are used in the update of the coefficient. The identification accuracy of two algorithms is compared through numerical simulation, and the results show that the accuracy of the least mean square algorithm with variable-step is better than that of the least squares recursion algorithm with forgetting factor. However, problems of slow convergence rate and large identification residual still exist in the former. Thus an improved least mean square algorithm with variable-step is proposed to solve the problems. The experiment proves that the accuracy of the improved algorithm comes up to the requirements of control with better effect and faster convergence rate.
In order to improve the identification accuracy of the secondary path and reduce the impact on adaptive control, the transversal filter is used as the estimation model in this article. The least squares recursive algorithm with forgetting factor and the least mean square algorithm with variable-step are used in the update of the coefficient. The identification accuracy of two algorithms is compared through numerical simulation, and the results show that the accuracy of the least mean square algorithm with variable-step is better than that of the least squares recursion algorithm with forgetting factor. However, problems of slow convergence rate and large identification residual still exist in the former. Thus an improved least mean square algorithm with variable-step is proposed to solve the problems. The experiment proves that the accuracy of the improved algorithm comes up to the requirements of control with better effect and faster convergence rate.
引文
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[2] 朱石坚,何琳.船舶机械振动控制 [M].北京:国防工业出版社,2006:10-30.
[3] YANG Tie-jun,GU Zhong-quan,LU Ming-yue,et al.Active control of structural vibration with on-line secondarypath modeling [J].Progress in Natural Science,2004,14(6):511-518.
[4] TU Y,FULLER C R.Multiple refefence feed-forward active noise control part :Preprocessing and experimental results [J].Journal of Sound and Vibration,2003,233(5):761-774.
[5] 浦玉学.自适应振动噪声主动控制若干关键问题研究 [D].南京:南京航空航天大学,2015:2-10.
[6] 杨铁军,顾仲权,鲁明月,等.基于误差通道在线辨识的结构振动主动控制系统 [J].振动与冲击,2004,23(3):55-59.YANG Tie-jun,GU Zhong-quan,LU Ming-yue,et al.Active control system for structural vibration with online secondary path identification [J].Journal of Vibration and Shock,2004,23(3):55-59.(in Chinese)
[7] AKHTAR M T,ABE M,KAWAMATA M.A new variable step size LMS algorithm-based me-thod for improved online secondary path modeling in active noise control system [J].IEEE Transactions on Audio Speech and Language Processing,2006,12:720-726.
[8] SNYDER S D,HANSEN C H.The effect of transfer function estimation errors on the filtered-xLMS algorithm [J].IEEE Transactions on Signal Process.1994,42(4):950-953.
[9] 羊箭锋,黄平,肖圣兵.基于Labview振动主动控制实验仿真研究 [J].实验技术与管理,2011,28(7),102-109.YANG Jian-feng,HUANG Ping,XIAO Sheng-bing.Study on experiment and simulation of active vibration based on LabVIEW [J].Experimental Technology and Management,2011,28(7),102-109.(in Chinese)
[10] CHAN S C,CHU Y J.Performance analysis and design of FxLMS algorithm in broadb and ANC system with online secondary-path modeling [J].IEEE Transactions on Audio,Speech and Language Citation Processing,2012,20(3):982-993.
[11] 宋雪.基于DSP的振动主动控制器小型化设计 [D].哈尔滨:哈尔滨工程大学,2010:82-92.
[12] 马倍蓓,吴进华,纪军,等.dSPACE实时仿真平台软件环境及应用 [J].系统仿真学报,2004,16(4):667-670.MA Bei-bei,WU Jin-hua,JI Jun,et al.Software environment and application of dSPACE real-time simulation platform [J].Journal of System Simulation,2004,16(4):667-670.(in Chinese)