Real-time Kalman filter implementation for active feedforward control of time-varying broadband noise and vibrations

Tracking behavior and the rate of convergence are critical properties in active noise control applications with time-varying disturbance spectra. As compared to the standard filtered-reference Least Mean Square (LMS) algorithm, improved convergence can be obtained with schemes based on preconditioning, affine projections, Recursive Least Squares (RLS), and other methods. The RLS method potentially leads to very fast convergence but straightforward implementations may suffer from round-off errors and suboptimal tracking behavior. In this paper a Kalman filter approach is used which, as compared to RLS, includes a covariance weighting for model output errors in order to improve tracking behavior and robustness. An orthogonal transformation scheme was used to reduce the influence for round-off errors of the Kalman recursions. In this paper an extension is given for multiple input multiple output systems, and results of a real-time implementation are presented. Conclusions are given regarding the suitability for real-time implementation of previous formulations. Different parameterizations of the secondary path model between the active control source and the error sensor are compared. Real-time results demonstrate the rapid convergence for reduction of noise in a duct, as well as numerical stability during extended operation intervals.status: publishe

Spectrogram Image Analysis of Error Signals for Minimizing Impulse Noise

This paper presents the theoretical and experimental study on the spectrogram image analysis of error signals for minimizing the impulse input noises in the active suppression of noise. Impulse inputs of some specific wave patterns as primary noises to a one-dimensional duct with the length of 1800 mm are shown. The convergence speed of the adaptive feedforward algorithm based on the least mean square approach was controlled by a normalized step size which was incorporated into the algorithm. The variations of the step size govern the stability as well as the convergence speed. Because of this reason, a normalized step size is introduced as a new method for the control of impulse noise. The spectrogram images which indicate the degree of the attenuation of the impulse input noises are considered to represent the attenuation with the new method. The algorithm is extensively investigated in both simulation and real-time control experiment. It is demonstrated that the suggested algorithm worked with a nice stability and performance against impulse noises. The results in this study can be used for practical active noise control systems

Novel methods for active reservoir monitoring and flow rate allocation of intelligent wells

The value added by intelligent wells (I-wells) derives from real-time, reservoir and production performance monitoring together with zonal, downhole flow control. Unfortunately, downhole sensors that can directly measure the zonal flow rates and phase cuts required for optimal control of the well’s producing zones are not normally installed. Instead, the zonal, Multi-phase Flow Rates (MPFRs) are calculated from indirect measurements (e.g. from zonal pressures, temperatures and the total well flow rate), an approach known as soft-sensing. To-date all published techniques for zonal flow rate allocation in multi-zone I-wells are “passive” in that they calculate the required parameters to estimate MPFRs for a fixed given configuration of the completion. These techniques are subject to model error, but also to errors stemming from measurement noise when there is insufficient data duplication for accurate parameter estimation. This thesis describes an “active” soft-sensing technique consisting of two sequential optimisation steps. First step calculates MPFRs while the second one uses a direct search method based on Deformed Configurations to optimise the sequence of Interval Control Valve positions during a routine multi-rate test in an I-well. This novel approach maximises the accuracy of the calculated reservoir properties and MPFRs. Four “active monitoring” levels are discussed. Each one uses a particular combination of available indirect measurements from well performance monitoring systems. Level one is the simplest, requiring a minimal amount of well data. The higher levels require more data; but provide, in return, a greater understanding of produced fluids volumes and the reservoir’s properties at both a well and a zonal level. Such estimation of the reservoir parameters and MPFRs in I-wells is essential for effective well control strategies to optimise the production volumes. An integrated, control and monitoring (ICM) workflow is proposed which employs the active soft-sensing algorithm modified to maximise I-well oil production via real-time zonal production control based on estimates of zonal reservoir properties and their updates. Analysis of convergence rate of ICM workflow to optimise different objective functions shows that very accurate zonal properties are not required to optimise the oil production. The proposed reservoir monitoring and MPFR allocation workflow may also be used for designing in-well monitoring systems i.e. to predict which combination of sensors along with their measurement quality is required for effective well and reservoir monitoring

A technique for improved stability of adaptive feedforward controllers without detailed uncertainty measurements

Model errors in adaptive controllers for reduction of broadband noise and vibrations may lead to unstable systems or increased error signals. Previous work has shown that the addition of a low-authority controller that increases damping in the system may lead to improved performance of an adaptive, high-authority controller. Other researchers have suggested to use frequency dependent regularization based on measured uncertainties. In this paper an alternative method is presented that avoids the disadvantages of these methods namely the additional complex hardware, and the need to obtain detailed information of the uncertainties. An analysis is made of an active noise control system in which a difference exists between the secondary path and the model as used in the controller. The real parts of the eigenvalues that determine the stability of the system are expressed in terms of the amount of uncertainty and the singular values of the secondary path. Based on these expressions, modifications of the feedforward control scheme are suggested that aim to improved performance without requiring detailed uncertainty measurements. For an active noise control system in a room it is shown that the technique leads to improved performance in terms of robustness and the amount of reduction of the error signals

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

Active Noise Control using Variable step-size Griffiths’ LMS (VGLMS) algorithm on Real-Time platform

This paper proposes implementation of Griffith’s Variable step-size algorithm for Active Noise Control (ANC) on ADSP-TS201 EZ-Kit Lite. The dual computational units and execution of up to four instructions per cycle which are special features over other processors are best utilized to generate an optimized code. The VGLMS provides improved secondary path estimation and computations involved are marginal as the same gradient is used for step-size computation and coefficient adaptation. The improved secondary path estimate, in turn improves the ANC performance. Further, variable step-size algorithm is used for the main-path to achieve faster convergence. Both for narrowband (fundamental and its harmonics) and broadband noise fields, for a duct the attenuation achieved is 25 dB and 15 dB respectively. The program execution time was only 1.25% for an input sampling rate of 1 KHz which indicates the utility of the special features of the processor considered. Further these features have enabled in bringing down the program memory requirement in the implementation of the algorithm

Secure and Privacy-Preserving Average Consensus

Average consensus is fundamental for distributed systems since it underpins key functionalities of such systems ranging from distributed information fusion, decision-making, to decentralized control. In order to reach an agreement, existing average consensus algorithms require each agent to exchange explicit state information with its neighbors. This leads to the disclosure of private state information, which is undesirable in cases where privacy is of concern. In this paper, we propose a novel approach that enables secure and privacy-preserving average consensus in a decentralized architecture in the absence of any trusted third-parties. By leveraging homomorphic cryptography, our approach can guarantee consensus to the exact value in a deterministic manner. The proposed approach is light-weight in computation and communication, and applicable to time-varying interaction topology cases. A hardware implementation is presented to demonstrate the capability of our approach.Comment: 7 pages, 4 figures, paper is accepted to CPS-SPC'1

Active control of sound inside a sphere via control of the acoustic pressure at the boundary surface

Here we investigate the practical feasibility of performing soundfield reproduction throughout a three-dimensional area by controlling the acoustic pressure measured at the boundary surface of the volume in question. The main aim is to obtain quantitative data showing what performances a practical implementation of this strategy is likely to yield. In particular, the influence of two main limitations is studied, namely the spatial aliasing and the resonance problems occurring at the eigenfrequencies associated with the internal Dirichlet problem. The strategy studied is first approached by performing numerical simulations, and then in experiments involving active noise cancellation inside a sphere in an anechoic environment. The results show that noise can be efficiently cancelled everywhere inside the sphere in a wide frequency range, in the case of both pure tones and broadband noise, including cases where the wavelength is similar to the diameter of the sphere. Excellent agreement was observed between the results of the simulations and the measurements. This method can be expected to yield similar performances when it is used to reproduce soundfields.Comment: 28 pages de text