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Intelligent Learning Algorithms for Active Vibration Control
YesThis correspondence presents an investigation into the
comparative performance of an active vibration control (AVC) system
using a number of intelligent learning algorithms. Recursive least square
(RLS), evolutionary genetic algorithms (GAs), general regression neural
network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS)
algorithms are proposed to develop the mechanisms of an AVC system.
The controller is designed on the basis of optimal vibration suppression
using a plant model. A simulation platform of a flexible beam system
in transverse vibration using a finite difference method is considered to
demonstrate the capabilities of the AVC system using RLS, GAs, GRNN,
and ANFIS. The simulation model of the AVC system is implemented,
tested, and its performance is assessed for the system identification models
using the proposed algorithms. Finally, a comparative performance of the
algorithms in implementing the model of the AVC system is presented and
discussed through a set of experiments
Adaptive digital signal processing Java teaching tool
This publication presents a JAVA program for teaching the rudiments of adaptive digital signal processing (DSP) algorithms and techniques. Adaptive DSP is on of the most important areas of signal processsing, and provides the core algorithmic means to implement applications ranging from mobile telephone speech coding, to noise cancellation, to communication channel equalization. Over the last 30 years adaptive digital signal processing has progressed from being a strictly graduate level advanced class in signal processing theory to a topic that is part of the core curriculum for many undergraduate signal processing classes. The JAVA applet presented in this publication has been devised for students to use in combination with lecture notes and/or one of the recognised textbooks such that they can quickly and conveniently simulate algorithms such as the LMS (least mean squares), RLS (recursive least squares) and so on in a variety of applications without requiring to write programs or scripts or using any special purpose software. By the very nature of the JAVA code therefore, the applet can be run from any browser, even over a low bandwidth modem connection
Comparative performance of intelligent algorithms for system identification and control
This paper presents an investigation into the comparative performance of intelligent system identification and control algorithms within the framework of an active vibration control (AVC) system. Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an AVC system, where the controller is designed based on optimal vibration suppression using the plant model. A simulation platform of a flexible beam system in transverse vibration using finite difference (FD) method is considered to demonstrate the capabilities of the AVC system using GAs and ANFIS. MATLAB GA tool box for GAs and Fuzzy Logic tool box for ANFIS function are used to design the AVC system. The system is men implemented, tested and its performance assessed for GAs and ANFIS based algorithms. Finally, a comparative performance of the algorithms in implementing system identification and corresponding AVC system using GAs and ANFIS is presented and discussed through a set of experiments
Stochastic Analysis of the LMS Algorithm for System Identification with Subspace Inputs
This paper studies the behavior of the low rank LMS adaptive algorithm for the general case in which the input transformation may not capture the exact input subspace. It is shown that the Independence Theory and the independent additive noise model are not applicable to this case. A new theoretical model for the weight mean and fluctuation behaviors is developed which incorporates the correlation between successive data vectors (as opposed to the Independence Theory model). The new theory is applied to a network echo cancellation scheme which uses partial-Haar input vector transformations. Comparison of the new model predictions with Monte Carlo simulations shows good-to-excellent agreement, certainly much better than predicted by the Independence Theory based model available in the literature
Solar Magnetic Tracking. I. Software Comparison and Recommended Practices
Feature tracking and recognition are increasingly common tools for data
analysis, but are typically implemented on an ad-hoc basis by individual
research groups, limiting the usefulness of derived results when selection
effects and algorithmic differences are not controlled. Specific results that
are affected include the solar magnetic turnover time, the distributions of
sizes, strengths, and lifetimes of magnetic features, and the physics of both
small scale flux emergence and the small-scale dynamo. In this paper, we
present the results of a detailed comparison between four tracking codes
applied to a single set of data from SOHO/MDI, describe the interplay between
desired tracking behavior and parameterization of tracking algorithms, and make
recommendations for feature selection and tracking practice in future work.Comment: In press for Astrophys. J. 200
Estimation-based synthesis of H∞-optimal adaptive FIR filtersfor filtered-LMS problems
This paper presents a systematic synthesis procedure for H∞-optimal adaptive FIR filters in the context of an active noise cancellation (ANC) problem. An estimation interpretation of the adaptive control problem is introduced first. Based on this interpretation, an H∞ estimation problem is formulated, and its finite horizon prediction (filtering) solution is discussed. The solution minimizes the maximum energy gain from the disturbances to the predicted (filtered) estimation error and serves as the adaptation criterion for the weight vector in the adaptive FIR filter. We refer to this adaptation scheme as estimation-based adaptive filtering (EBAF). We show that the steady-state gain vector in the EBAF algorithm approaches that of the classical (normalized) filtered-X LMS algorithm. The error terms, however, are shown to be different. Thus, these classical algorithms can be considered to be approximations of our algorithm. We examine the performance of the proposed EBAF algorithm (both experimentally and in simulation) in an active noise cancellation problem of a one-dimensional (1-D) acoustic duct for both narrowband and broadband cases. Comparisons to the results from a conventional filtered-LMS (FxLMS) algorithm show faster convergence without compromising steady-state performance and/or robustness of the algorithm to feedback contamination of the reference signal
Adaptive multichannel control of time-varying broadband noise and vibrations
This paper presents results obtained from a number of applications in which a recent adaptive algorithm for broadband multichannel active noise control is used. The core of the algorithm uses the inverse of the minimum-phase part of the secondary path for improvement of the speed of convergence. A further improvement of the speed of convergence is obtained by using double control filters for elimination of adaptation loop delay. Regularization was found to be necessary for robust operation. The regularization technique which is used preserves the structure to eliminate the adaptation loop delay. Depending on the application at hand, a number of extensions are used for this algorithm. For an application with rapidly changing disturbance spectra, the core algorithm was extended with an iterative affine projection scheme, leading to improved convergence rates as compared to the standard nomalized lms update rules. In another application, in which the influence of the parametric uncertainties was critical, the core algorithm was extended with low authority control loops operating at high sample rates. In addition, results of other applications are given, such as control of acoustic energy density and control of time-varying periodic and non-periodic vibrations
Wiener filtering with a seismic underground array at the Sanford Underground Research Facility
A seismic array has been deployed at the Sanford Underground Research
Facility in the former Homestake mine, South Dakota, to study the underground
seismic environment. This includes exploring the advantages of constructing a
third-generation gravitational-wave detector underground. A major noise source
for these detectors would be Newtonian noise, which is induced by fluctuations
in the local gravitational field. The hope is that a combination of a low-noise
seismic environment and coherent noise subtraction using seismometers in the
vicinity of the detector could suppress the Newtonian noise to below the
projected noise floor for future gravitational-wave detectors. In this paper,
we use Wiener filtering techniques to subtract coherent noise in a seismic
array in the frequency band 0.05 -- 1\,Hz. This achieves more than an order of
magnitude noise cancellation over a majority of this band. We show how this
subtraction would benefit proposed future low-frequency gravitational wave
detectors. The variation in the Wiener filter coefficients over the course of
the day, including how local activities impact the filter, is analyzed. We also
study the variation in coefficients over the course of a month, showing the
stability of the filter with time. How varying the filter order affects the
subtraction performance is also explored. It is shown that optimizing filter
order can significantly improve subtraction of seismic noise, which gives hope
for future gravitational-wave detectors to address Newtonian noise
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