Comparison of LMS and RLS Algorithm for Active Vibration Control of Smart Structures

Control algorithm is one of the key elements for active vibration suppression of the space flexible structure based on smart materials. As least mean square (LMS) algorithm and recursive least square (RLS) algorithm are two fundamental algorithms for adaptive feed-forward filter algorithm drawing wide attention, this paper focuses on the algorithm process analysis and performance comparison of the two adaptive algorithms based on finite impulse response (FIR) structure. The design and related characteristic analysis of the controller is given as well as the realization process based on the theoretical analysis. Simulation is done using Matlab 7.0 to verify the correctness of the theoretical analysis. The simulation comparison between the two type algorithms shows that RLS algorithm has faster convergence and better control performance than LMS algorithm. On the basis of the simulation analysis, the actual vibration suppression experiment is done on the constructed experimental platform for piezoelectric flexible beam, and experiment result confirms the simulation effects.

[1]  Markus Glugla,et al.  Active Vibration Control Using Delay Compensated LMS Algorithm by Modified Gradients , 2008 .

[2]  Qitao Huang,et al.  Adaptive inverse control of random vibration based on the filtered-X LMS algorithm , 2010 .

[3]  L. Leniowska,et al.  Self-Tuning Control with Regularized RLS Algorithm for Vibration Cancellation of a Circular Plate , 2009 .

[4]  Wei Xing Zheng,et al.  Fast RLS-type algorithm for unbiased equation-error adaptive IIR filtering based on approximate inverse-power iteration , 2005 .

[5]  Dah-Chung Chang,et al.  A Stabilized Multichannel Fast RLS Algorithm for Adaptive Transmultiplexer Receivers , 2009, Circuits Syst. Signal Process..

[6]  Chun-Liang Lin,et al.  Structure-specified IIR filter and control design using real structured genetic algorithm , 2009, Appl. Soft Comput..

[7]  Dayong Zhou,et al.  Hybrid filtered error LMS algorithm: another alternative to filtered-x LMS , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[8]  Ming Zhang,et al.  A weight-constrained FxLMS algorithm for feedforward active noise control systems , 2002, IEEE Signal Process. Lett..

[9]  L. Leniowska,et al.  Self-Tuning Control with Regularized RLS Algorithm for Vibration Cancellationof a Circular Plate , 2009 .

[10]  Debi Prasad Das,et al.  Fast exact multichannel FSLMS algorithm for active noise control , 2009, Signal Process..

[11]  H.-W. Hoffmeister,et al.  Multiple channel Filtered-X LMS-RLS vibration control in wood machining , 2009, 2009 IEEE International Conference on Control and Automation.