Filtered-x LMS Research Articles

Selective collaboration in distributed FxLMS active noise control systems

This paper addresses the selection of the collaboration configuration on distributed active noise control (ANC) systems. ANC systems aim to cancel out acoustic noise within a listening area. In distributed systems, the control task is delegated among multiple acoustic control nodes that generate the control signals by filtering a noise reference signal. The coefficients of each node filter are iteratively calculated using the filtered-X LMS algorithm. The stability is achieved when the adaptive filters computed in each node converge to finite values. However, acoustic coupling among nodes could lead to instability (i.e., divergence). Collaboration among selected nodes may avoid this phenomenon, although not just any collaboration configuration guarantees network stability. On the other hand, a collaborative distributed system presents two drawbacks: the stability assessment is computationally expensive, and communication requirements increase with the number of collaborations among nodes. In this paper, we propose and discuss several methods to establish a collaboration configuration that ensures system stability. The optimal configuration, which is characterized by the minimal number of necessary collaborations between nodes, can be identified through exhaustive search. However, this approach incurs a high computational cost, particularly in networks with many nodes. To address this challenge, we introduce several heuristic methods aimed at efficiently obtaining stable configurations.

A development on the interior noise prediction technology using active noise control algorithm

When developing NVH of a vehicle, identifying a noise transmission vulnerable path is a very important task. The traditional TPA(Transfer Path Analysis) is based on a physical theoretical equation and calculates the contribution of each path by multiplying the ATF(Acoustic Transfer Function) of each path by the driving load. In this case, it is often difficult to calculate the driving load, and M/H consumption is also high. OTPA(Operational Transfer Path Analysis) performs transfer path analysis only with driving signals for each route, but has a disadvantage of insufficient physical causality. In this study, we proposed an efficient indoor noise TPA method that modifies the ANC algorithm by combining the reference signals while driving with the secondary path of Filtered-X LMS. The proposed method uses the secondary transfer function of Fx-LMS as the ATF for each path. It is not necessary to obtain physical load by predicting target noise with the Ref signal for each route. Therefore, it is possible to predict indoor noise with low M/H. In this study, it can be confirmed that the proposed method shows similar results compared to the existing methods and efficiently identifies main noise path for interior noise.

An improved Takagi–Sugeon–Kang-based FxLMS algorithm and its active noise control in electric bus driving position

This paper proposed an improved filtered-x least mean square (FxLMS) algorithm based on the fuzzy control rule of Takagi–Sugeon–Kang (TSK) to solve the drawback of slow convergence for standard FxLMS algorithm. The TSK-FxLMS is a control framework with two inputs and three outputs constructed by the error signal and its integral as input variables. To validate its effectiveness and applicability, in-vehicle active noise control (ANC) modelling and adaptive noise reduction at the driver’s position of an electric bus are thoroughly investigated. Firstly, the four noise signals for the different working conditions at 50 km/h, acceleration, coasting and braking are collected, and their spectral analyses are performed. Secondly, the effects of step size and fuzzy control parameters of ANC model on weight convergence and noise reduction effect are analysed and determined. Finally, the results of calculating and comparing the residual signals’ waveforms, frequency spectra, sound pressure levels and tracking performance indicate that the proposed TSK-FxLMS algorithm outperforms the standard FxLMS algorithm with faster convergence and better noise reduction effect.

A Robust Filtered-x Least Mean Square Algorithm with Adjustable Parameters for Active Impulsive Noise Control

In active noise control (ANC) systems, the traditional filtered-x least mean square (FxLMS) algorithm has poor control effect on impulsive noise. To overcome this drawback, a robust cost function was designed in this paper by embedding the cost function of the FxLMS algorithm into the framework of hyperbolic tangent function; this paper thus proposes a robust filtered-x least hyperbolic tangent (FxLHT) algorithm in ANC systems. Moreover, the value of λ in the FxLHT algorithm greatly affects the robustness and convergence performance of the algorithm. Therefore, a variable λ-parameter was proposed to enhance the performance of the FxLHT algorithm. Simulation results show that in the active control of impulsive noise, compared with the FxLMS algorithm and other robust ANC algorithms, the proposed FxLHT algorithm and variable λ-parameter FxLHT algorithm not only exhibit good robustness and noise reduction performance but also have a better tracking ability.

Enhanced nonlinear active noise control: A novel approach using brain storm optimization algorithm

Active Noise Control (ANC) systems play a crucial role in reducing unwanted noise in various settings. Traditional ANC methods, like the Filtered-x Least Mean Squares (FxLMS) algorithm, are effective in linear noise scenarios. However, they often struggle with more nonlinear and complex noise patterns. This paper introduces a novel approach using the brain storm optimization (BSO) algorithm in nonlinear ANC systems, which represents a significant departure from conventional techniques. The BSO algorithm, inspired by human brainstorming processes, excels in addressing the complexities of nonlinear noise by incorporating principles, such as delayed evaluation, free imagination, quantity and quality, and comprehensive improvement. By combining the BSO algorithm with an Extended Kalman Filter (EKF), a new ANC system is proposed that can adapt to a wide range of noise types with improved speed and accuracy. Experimental results showcase the superior performance of the BSO algorithm, achieving an impressive noise reduction of up to 48 dB (dB) in a 500Hz sinusoidal noise scenario, with a convergence time as fast as 0.01 s, outperforming the FxLMS algorithm by a significant margin. Moreover, in complex environments with multi-frequency and random noise, the BSO algorithm consistently demonstrates better noise reduction and quicker convergence, reducing noise levels by up to 27 dB within 0.001 s. The innovative use of the BSO algorithm in ANC systems not only enhances noise reduction capabilities, especially for nonlinear and complex noise signals, but also improves convergence times, paving the way for future advancements in ANC technologies.

Projection FxLMS framework of active noise control against impulsive noise environments

This paper focuses on active noise control in the presence of disturbance from an α-stable distribution. The filtered-x fractional lower-order covariance algorithm has been introduced to enhance the performance of traditional algorithms in impulsive noise environments. For minimal adjustment of the weight vector, another filtered-x normalized fractional lower-order covariance (FxNFLOC) algorithm is proposed. Furthermore, the projection approach of the filtered-x least mean square (FxLMS) algorithm is explored. Within this framework, a new algorithm called filtered-x least mean logarithmic square (FxLMLS), which employs a logarithmic function, is devised. The convergence conditions of the developed schemes are also derived. Numerical examples demonstrate the effectiveness and superior performance of the FxNFLOC and FxLMLS algorithms in handling impulsive noise over existing competitors in various scenarios.

Enhancing active noise control through stacked autoencoders: Training strategies, comparative analysis, and evaluation with practical setup

This work is motivated by the imperative to overcome the limitations of conventional active noise control (ANC) techniques. These limitations are rooted in linear systems like the least mean square algorithm when faced with nonlinear distortions in the acoustic environment and system electronics, such as loudspeakers. This work addresses the nonlinear ANC problem by conceptualizing ANC as a deep learning problem. A novel stacked autoencoder (SAE) model is proposed, which is trained to estimate the noise that should be played at a loudspeaker so that the noise received at an error microphone may be attenuated. ANC experiments are initially set up in an anechoic room to study the fundamental performance characteristics of the architecture without extraneous considerations of room impulse response. This is followed by performance analysis in a normal reflective room. Actual noise, comprising broadband and tonal noise, is recorded in both an anechoic room and a normal reflective room. The importance of these experiments in the design process lies in their ability to account for nonlinear effects in actual setups, surpassing the limitations of relying solely on models. Experiment outcomes demonstrate that the proposed SAE model yields a noise reduction (NR) of up to 26.44 dB for wideband noise and up to 35.74 dB for tonal noise types. Additionally, there is an improvement in the extent of the quiet zone provided by the proposed SAE for tonal noise cases as compared to the traditional filtered-x least mean square (FxLMS) technique.

Research on active vibration control of blade using variable-step-size filtered-x least mean square algorithm

The engine blade is one of the important parts of the aero engine, vibration can lead to engine blade off or fracture. Active Vibration Control (AVC) has a higher flexibility is increasingly used to suppress the vibration of the blade. The filtered-x least mean square (FxLMS) algorithm is an adaptive algorithm commonly used in active vibration control, but its fixed step size has a contradiction between convergence speed and steady-state error. A new variable-step-size FxLMS (VSSFxLMS) algorithm based on hyperbolic secant function was proposed, and the sech function ensures the control signal is bounded. In the early stage, a larger step size enables the error to decrease rapidly. The step size decreases as the error decreases, and a smaller step size ensures a smaller steady-state error. Firstly, this paper summarizes three VSSFxLMS algorithms and proposes an improved algorithm; Secondly, it compares and analyses the mentioned algorithms; Then, a joint simulation platform using ADAMS and Simulink is built to verify the effectiveness of the proposed algorithms; Finally, an experimental platform was built to complete the blade vibration active control experiments using several VSSFxLMS algorithms, and the results of the experiment verify the better vibration suppression effect of the proposed algorithm.

Active control method for fluid borne noise in aerospace fluid systems of variable operation statuses

In the fluid system of aerospace vehicles, the fluid-borne noise (FBN) may induce vibration and damage to components. The FBN may limit the performance of the entire onboard system. To achieve a stable active elimination for the FBN is not easy, for many variable operation statuses exist in practical systems, such as varying-frequency and varying-load status. In this work, an improved feed-forward active control method based on narrowband adaptive notch filters, with an online identification module, is proposed. The algorithm includes three adaptive filters: an adaptive notch filter for producing cancelling signal by using filtered-X least mean square (FXLMS) algorithm, one for online identification to model the real-time secondary path, and an error prediction filter for extracting the precise error for online identification. In the aircraft hydraulic system of high pressure, a so-called relief scheme, also known as the bypass structure, is used for the FNB, controlling an active control valve in the branched line to overflow the main fluid line. The system is modelled with software AMESim for the fluid part and Matlab for the control algorithm. Simulation results show that the control method can performance well in the situations of varying frequency and load. The test rig of a hydraulic system is established, which could change pump speed and fluid pressure. The test results show that more than 90% reduction for a certain frequency can be achieved for stable pressure statuses. And more than 50% reduction is achieved for varying frequency with step changes. As for the load-varying status, the reduction of 82.7% and 65.8% are obtained before and after pressure change. The method proposed is validated to be effective with both numerical and experimental tests.

Active noise control modeling for an electric bus driving position and its effect on sound quality

Current electric bus has become an important public transport for people in China, and its interior sound quality directly affects the driver’s physical and mental health for a long working time and the comprehensive performance of the vehicle. Therefore, it is an inevitable and urgent trend to research electric bus interior sound quality. Aiming at the problem of subjective uncomfortable auditory sensation caused by different speeds, the noise signals near the driver’s ears were collected under two constant speed conditions. On the basis of principle analysis of feed-forward adaptive control system, the primary and secondary path transfer functions were presented using Simulink module in MATLAB to construct three active noise control models for an electric bus based on least mean square (LMS), normalized LMS (NLMS) and filtered-x LMS (FxLMS). Numerical simulation results indicate that the established control models are convergent, and four acoustic parameters are effectively reduced after controls to achieve the effect of improving the sound quality.

Insights into the Filtered-x LMS Algorithm in the Presence of Frequency Mismatch

Insights into the Filtered-x LMS Algorithm in the Presence of Frequency Mismatch

FxLMS and Lateralization: A Combined Strategy for Crosstalk Cancellation in Bone Conduction

This study aimed to address the issue of "crosstalk" in bone conduction hearing aids, where sound meant for one ear is mistakenly perceived by the other ear via bone conduction. We explored a potential solution by canceling the crosstalk sound at the cochlea. To achieve this, an accelerometer was placed on the mastoid to monitor the crosstalk sound produced by an audiometric bone transducer on the opposite side of the head, while a second transducer on the same side as the accelerometer was used to cancel it out. The filtered-x least mean square (FxLMS) algorithm was used to optimize the crosstalk compensation (CTC) filter for cancellation at the mastoid. Then, the subjects manually adjusted the filter coefficients through a lateralization task to achieve crosstalk cancellation at the cochlea. This task involved modifying phase and level differences between pure-tone sounds from the two transducers, making the sound seem to originate from the leftmost or rightmost side of the head. Our results indicated successful cancellation of crosstalk sound at the cochlea, as subjects' hearing thresholds under noise masking were lower with crosstalk cancellation.

Application of Active Impulsive Noise Control (AINC) for Excavator Cabin Using Advanced Convex-Combined Normalized step size Algorithm and Verification of Robustness

Conventional Active Noise Control (ANC) algorithms, such as Filtered-x least mean square (FxLMS), is not suitable for Active Impulse Noise Control (AINC). Because it lacks robustness to the impulsive sample input, it is unstable and diverges very easily. Therefore, AINC algorithm must be robust to impulse. The heavy equipment such as excavators often occur frequent impulse noise during operations. Also, in working environment of operating excavator, impulsive noise is prone to occurred by other heavy equipment. Therefore, it is necessary to examine the AINC algorithm that is suitable for excavators and its robustness. In this study, Convex Combined Step Size (CCSS), which is simulated better effects in AINC than conventional ANC algorithms, is tried to AINC of excavator. In addition, the noise reduction performance and robustness to undesirable sound were verified in the simulated experiment environment of the excavator cabin.

Active vibration control for reduction of interior noise caused by R-MDPS

This paper presents an active vibration control (AVC) to reduce the vibration of motor driven power steering (MDPS). The MDPS used in the experiment was R-MDPS, an MDPS connected to Rack Gear. When the R-MDPS generates vibration in the operating state, the vibration is transmitted through the subframe. The vibration transmitted through the subframe generates indoor noise and reduces the driving quality of the driver. To reduce vibration, the AVC was used by inserting an actuator between R-MDPS and subframe. The actuator used Piezo material is called a Piezo actuator. To select the location of the actuator, Transfer path analysis (TPA) was performed. the effect was confirmed by setting the main path to error point. The experiment was conducted for feasibility by connecting R-MDPS and subframe to a test bench manufactured in the laboratory. The algorithm used for vibration control used the Filtered-X Least Mean Square (FxLMS) algorithm and the control result was reduced from 4 to 5 dB. It is shown that proposed method is an effective vibration reduction effect of R-MDPS.

Active Noise Control for Kitchen Hood

A kitchen hood is an appliance that is powered by a fan motor to suck the exhaust in the kitchen while cooking. When turned on, it produces low-frequency noise that cannot be reduced by only a passive material. There is a direct relationship between the speed settings in the hood and in the amount of noise that is produced. The higher the speed setting, the higher the noise. This broadband noise is reduced using the filtered-x least mean square (FXLMS) method with a feedforward active noise control (ANC) structure. To ensure a good reduction, the microphones must have good coherence and must not be affected by the turbulent wind. The whole ANC system will use one reference microphone, several error microphones connected as one, and two anti-noise speakers connected as one.

Active noise control performance in the high-frequency range

Noise control for vehicle mainly uses a reduction method through sound absorbing and insulating materials but has disadvantages of increasing cost and weight. It is known that the noise reduction performance of Active Noise Control (ANC) technology that can be considered as an alternative is limited to the low frequency range. However, there could be a case when the use of active noise cancellation is required at higher frequency range. Therefore, the purpose of this paper is to find out how much high-frequency noise can be controlled by applying active noise cancellation and the performance of ANC according to the equipment used. For this end, an ANC experimental environment was installed at the passenger's seat interior vehicle. By using Filtered-x Least Mean Square (FxLMS) algorithm based on adaptive control, ANC was conducted to reduce the high frequency noise in the 2kHz~4kHz and its noise reduction performance was compared according to the following variables: Filter length, step size, sampling frequency, etc. As a result of selecting the audio DSP board as the control system and applying appropriate filter length, step size, and sampling frequency, noise reduction of up to 40 dB(A) was obtained at the position of both ears of the occupant.

A diffusion filtered-x affine projection algorithm for distributed active noise control

The well-known Filtered-x Affine Projection (FxAP) algorithm is usually deemed a better choice than the conventional Filtered-x Least-Mean-Square (FxLMS) algorithm when faster convergence speed is desired in active noise control applications. However, the improvement of its convergence performance is obtained at the expense of increased computational complexity. It is usually unrealistic to regulate multichannel ANC systems using the centralized AP algorithm. Recently, distributed diffusion adaptation schemes over acoustic sensor and actuator networks have been introduced to multichannel ANC systems, such as the Diffusion FxLMS algorithms. Distributed processing based on diffusion cooperation strategy allocates the computation load to the network nodes in a scalable manner. This paper proposes a distributed Diffusion Filtered-x AP (DFxAP) algorithm for multichannel ANC systems. Simulation results and computational complexity analysis show that the DFxAP algorithm outperforms the Diffusion FxLMS algorithm in convergence performance and has lower computational complexity compared to the centralized AP algorithm.

An intelligent momentum perturbed variable step-size adaptive algorithm for fast converging HNANC systems

The existing methodologies for hybrid narrow-band active noise control (HNANC) systems utilises variants of the FXLMS algorithm, which typically resulted in degraded convergence and steady-state performance. In this paper, a class of variable step size (VSS) adaptive algorithms are explicitly developed for a HNANC system analyses and the convergence as well as the steady-state performance of the proposed framework are evaluated in comparison to the existing state-of-the-art. For an HNANC system designed to operate in a multiple noise environment, the VSS filtered-x LMS (VSSFXLMS) and momentum VSS filtered- x weight accumulated least mean square (MVSS-FXWALMS) algorithms are proposed. The proposed algorithm employs a variable step size that is inspired by the variable momentum factor, and combines these two parameters to drive the FXWALMS algorithm for an HNANC system in order to accomplish maximum noise reduction. In a multiple noise environment setup for an HNANC system, the proposed algorithm surpasses the state-of-the-art in nearly all conventional operating conditions. A detailed performance analysis of the proposed algorithm has been carried out using mean noise reduction ratio, steady-state power spectra at different conditions. The results demonstrate a significant improvement in noise reduction performance compared to the counterparts.

A novel parallel multi-harmonic global multi-channel control algorithm for helicopter active vibration control

The vibrations of the helicopter fuselage are mainly induced by the rotor with blade pass frequency and the high order harmonics. The conventional filtered-x least mean square (FxLMS) control algorithm usually used a uniform convergence factor for all the harmonics which leads poor effect in controlling multi-harmonic vibration. In this paper, a novel parallel multi-harmonic global multi-channel (MHGMC) control method is proposed to suppress the multi-harmonic vibration components transmitted from the main rotor to the fuselage. Bandpass filters are used to separate multiple frequency components of the error signals, the control channel models are identified for the corresponding frequency range to independently control each frequency component, and the reconstruction of the reference input signals can be obtained from the separated error signal, effectively improving the performance of tracking interference signals. In addition, the coupling of the control channels is considered, and the global vibration optimization is taken as the control objective to update the weight coefficient of the controller. An active vibration control system of the helicopter main gearbox eight-strut installation configuration has been established. The simulation and experiment results show that the proposed controller has faster control convergence speed and better control effect of multiple harmonics than the conventional filtered-x least mean square algorithm, and the attenuations of the disturbing frequency harmonic components at all measurement points in the experiment are over 38dB. The experimental results of variable excitation also verify that the proposed control algorithm has excellent adaptability and strong robustness.

Adaptive active vibration control for composite laminated plate: Theory and experiments

In this paper, an analysis model of adaptive active vibration control system of composite laminated plate using macro fiber composite (MFC) piezoelectric patches is presented, and the experimental testing is carried out. Electromechanical coupling equations of the composite laminated plate are established by considering the influence of the mass and stiffness of MFC piezoelectric patches. The adaptive active vibration control scheme for composite laminated plate based on the filtered-x least mean square (FxLMS) algorithm is proposed. The accuracy of the present analysis model is validated by the results of experimental test and finite element method (FEM). It can be found that the vibration responses nearby the first and second natural frequencies of the composite laminated plate can be rapidly and greatly suppressed by the adaptive active control system and experimental test. Moreover, the dynamic responses of the composite plate under multi-frequency excitation and random excitation can be fast controlled by the presented adaptive active control system. The novelty of this work is that a theoritical analysis model of adaptive closed-loop control system based on the MFC piezoelectric patches is presented for vibration suppression of composite laminated plate, and the effectiveness of the proposed adaptive active model are validated by experimental testing.