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Traditionally, physical models of associative memory assume conditions of equilibrium. Here, we consider a prototypical oscillator model of associative memory and study how active noise sources that drive the system out of equilibrium, as well as nonlinearities in the interactions between the oscillators, affect the associative memory properties of the system. Our simulations show that pattern retrieval under active noise is more robust to the number of learned patterns and noise intensity than under passive noise. To understand this phenomenon, we analytically derive an effective energy correction due to the temporal correlations of active noise in the limit of short correlation decay time. We find that active noise deepens the energy wells corresponding to the patterns by strengthening the oscillator couplings, where the more nonlinear interactions are preferentially enhanced. Using replica theory, we demonstrate qualitative agreement between this effective picture and the retrieval simulations. Our work suggests that the nonlinearity in the oscillator couplings can improve memory under nonequilibrium conditions. [ABSTRACT FROM AUTHOR]

The paper presents a new active control algorithm for the active cancellation of vibroacoustic noise radiated from the compressor installed under each passenger's seat in autonomous bus. The compressor is used for a heating, ventilation, and air-conditioning (HVAC) system which provide air conditioning for each passenger in autonomous bus. The sound radiated from the compressor of the HVAC system is a high-frequency annoyance noise caused by vibroacoustic noise due to the shell vibration of the compressor. The dominant frequency components of the vibroacoustic noise are harmonics of the rotation frequency of the reciprocating compressor. The HVAC system generates vibroacoustic noise dominantly in the frequency range between 200 and 600 Hz. Such noise is not only distinctly perceptible but also contributes to passenger discomfort and negatively impacts the perceived quality of the vehicle. The aim of this paper is to attenuate the vibroacoustic noise of the HVAC system by developing an active noise control (ANC) system. Generally, the widely recognized filtered-X least mean squared (FXLMS) algorithm has been successfully implemented to active noise control of reciprocating compressor. However, its performance was found lacking outside the peak frequency of compressor operation noise. To address this, the conjugate gradient algorithm was employed to enhance ANC performance. The conjugate gradient algorithm has a lower residual error and faster convergence rate compared to the FXLMS algorithm. As a consequence, the implementation of the conjugate gradient-based ANC algorithm resulted in enhanced noise reduction not only at the peak frequencies, which correspond to the compressor operation frequency, but also in frequency ranges outside these peak frequencies. [ABSTRACT FROM AUTHOR]

The filtered-x least mean square (F-xLMS) technique dependent distributed active noise control (DANC) system is less effective against outliers and disturbances that represent impulses. In such an environment, DANC algorithms based on Swish functions are being developed to reduce noise. The Swish function overcomes the vanishing gradient problem and accelerates convergence by utilising the smoothness and unboundedness properties. In light of this, the current manuscript presents a set of robust techniques for DANC systems that utilizes the Swish structure in lieu of diffusion strategy for filtered-x Blake Zisserman, filtered-x logarithmic hyperbolic cosine adaptive filter algorithms, filtered-x least mean fourth, and filtered-x generalized maximum correntropy criterion. In regard to the suggested methods, weight updating rules pertaining utilizing to the introduced algorithms are created and a convergence analysis is done. The proposed methods achieve quantitatively 1–5 dB enhancement in noise mitigation in case of varied impulsive noise models and input reference signals considered with a faster convergence compared to previous strategies. [ABSTRACT FROM AUTHOR]

In this paper, the experimental results on microphone virtualization in realistic automotive scenarios are presented. A Temporal Convolutional Network (TCN) was designed in order to estimate the acoustic signal at the driver's ear positions based on the knowledge of monitoring microphone signals at different positions—a technique known as virtual microphone. An experimental setup was implemented on a popular B-segment car to acquire the acoustic field within the cabin while running on smooth asphalt at variable speeds. In order to test the potentiality of the TCN, microphone signals were recorded in two different scenarios, either with or without the front passenger. Our experimental results show that, when training is performed in both scenarios, the adopted TCN is able to robustly adapt to different conditions and guarantee a good average performance. Furthermore, an investigation on the parameters of the Neural Network (NN) that guarantee the sufficient accuracy of the estimation of the virtual microphone signals while maintaining a low computational complexity is presented. [ABSTRACT FROM AUTHOR]

A tunable active-noise-control (ANC) circuit topology for headsets used in different applications is presented in this article. In the current consumer headset market, ANC is a mature technology that is commonly applied to wireless headsets connected to smartphones for listening to music and making phone calls. The development of ANC headsets has resulted in low-cost and simple devices due to the built-in ANC registers in the digital circuit. Digital circuit implementation is important to provide ultra-low latency processing for different algorithms compared with using digital signal processors (DSPs). However, a limitation associated with these built-in ANC filters is that the ANC digital circuit has been designed only for the wireless headset consumer market. Consequently, ANC headsets can only be used for designated features. ANC tuned for planes by eliminating low-frequency engine noise has been commonly used in the past twenty years. Pass-through (PT) amplification is also commonly tuned to allow users to hear external sounds and have conversations without having to remove their headset. In addition, ANC and PT amplification are also not allowed to operate simultaneously on the existing chipsets. In this paper, an ANC circuit topology with ultra-low latency processing is presented, where the ANC response of the headset can be tuned without any restriction imposed by the built-in ANC filters. The proposed ANC circuit topology consists of a commercial ANC chipset with an external audio codec to provide tunable characteristics. It is achieved by fixed maximum noise cancellation of ANC chipset being modified by summing with the signal from audio-codec path via an audio mixer. Acoustic performance for several headset applications implemented using the proposed circuit is validated. This simple circuit topology can be easily developed and adapted to the current headset consumer market for consumer-selective ANC in different environments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Active road noise control (ARNC) systems have been widely investigated for low-frequency road noise attenuation in vehicle cabins. Multiple reference and error sensors are usually required to ensure noticeable noise reduction. However, this tends to slow down the convergence speed of adaptive algorithms due to the coupling of secondary paths and the cross correlation of reference signals. Furthermore, the high computational burden of normally utilized multichannel control algorithms exacerbates the difficulty of practical implementations. In this paper, an online decoupling-whitening frequency domain filtered-error least mean square (ODW-FDFeLMS) algorithm is proposed to address the aforementioned problems. Secondary path decoupling through inner-outer product decomposition and online reference whitening through spectral factorization effectively accelerate the convergence rate. Additionally, the utilization of the filtered-error algorithm based on frequency domain processing mitigates the computational complexity. Simulations with measured road noise data confirm the superiority of the ODW-FDFeLMS algorithm over existing algorithms in terms of convergence speed and computational complexity. Real-time experiments in a vehicle cabin further confirm the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient extension of functional links artificial neural networks (EE-FLANN) for the active noise control (ANC) application. The developed EE-FLANN controller can upgrade the model accuracy with the actual system thanks to adding the cross-terms to the trigonometric function. Unlike the method in the generalized FLANN (GFLANN) controller, the EE-FLANN exploits include cross-term symmetry. However, this causes the computational burden to increase remarkably. To reduce this disadvantage, we truncate the cross-terms appropriately based on the simplified strategy. Furthermore, the adaptive algorithm is designed to partially update the filter coefficients appropriately. Specifically, the cross-terms that do not satisfy certain magnitude conditions will be omitted during the update process to reduce costs. Experiments have shown that the proposed EE-FLANN controller can achieve comparable performance to the GFLANN controller but the complexity is reduced by up to 20%. [ABSTRACT FROM AUTHOR]

Owing to its impedance properties and piezoelectric effect, the piezoelectric polymer polyvinylidene fluoride (PVDF) finds application in the field of noise control. Traditional sound-absorbing materials have gradually fallen short of the demands of sound-absorbing functionality and environmental adaptability in today's increasingly acute noise pollution environment. When PVDF is added to sound absorption, materials or structures have an additional loss path for the conversion of sound energy to electric energy. This helps to increase the low-frequency sound absorption band and improve low-frequency sound absorption performance. Through the action of viscosity, friction, relaxation, piezoelectric effect, and dielectric loss, the sound energy in the passive sound-absorbing material containing PVDF is transformed into energy dissipation in the form of heat energy, electric energy, and so on under the matching of PVDF low impedance. In active control, secondary sound and force sources can be supplied by PVDF-based transducers and actuators to reduce noise. This paper first provides an overview of the piezoelectric characteristics of β-phase PVDF and its optimization techniques. It then goes on to discuss the use of PVDF in passive sound-absorbing materials, active controls, and hybrid sound absorption systems. Finally, it summarizes the benefits, issues that still need to be resolved, and future directions for these three noise reduction techniques. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we present a high-precision optical frequency noise detection and comparison technique using a two-way transfer method over a 260 km field fiber link. This method allows for the comparison of optical frequencies between remote optical references without the need for data transfer through communication. We extend a previously established two-way comparison technique to obtain all data at the local site. Two optical carrier signals are injected into the bidirectional fiber from both ends, and one carrier is reflected back from the remote end. This enables the phase comparison of the two carrier signals at a single site without the need to transmit experimental data. The common-mode frequency noise induced by the bidirectional fiber link is detected and effectively suppressed without the need for sophisticated active fiber noise control. Our demonstration system, which uses a 260 km field fiber link and a common laser source, achieves a fractional instability of 2.5 × 10 − 17 at 1 s averaging time and scales down to 3.5 × 10 − 21 at 8000 s. This scheme offers the distinct advantage of completing the comparison at a single site, eliminating the need for remote data transfer via communication. This method is expected to enhance reliability for high-precision frequency comparisons between remote optical clocks and advanced atomic clocks. [ABSTRACT FROM AUTHOR]

In the process of turning a maglev motorized spindle, there are problems such as system model time-varying, cross-coupling of control parameters, difficult measurement of system state variables, and nonlinear characteristics of active magnetic bearings, which lead to the inevitable cutting chatter phenomenon, difficult control algorithm design, and then the reduction of workpiece surface quality and accuracy, affecting machining efficiency and tool life. In this paper, the "multimodal-distributed parameter" model is extended to the "magnetic bearing-rotor-workpiece" variable mass turning process. An adaptive backstepping fast dynamic terminal sliding mode control is designed to address the model's time-varying parameters and cross-coupling issues. In view of the difficulty in measuring the vibration displacement at the cutting point, the displacement field reconstruction method was introduced to reconstruct the vibration displacement field online and provide effective feedback for the previously designed control strategy. Finally, the proposed controller is applied to adjust and control the turning process of a maglev motorized spindle and compared with other advanced controllers. The simulation results show that the proposed control method has a better control effect than other control methods in the presence of unmodeled dynamics, uncertainties, and external disturbances. [ABSTRACT FROM AUTHOR]

The Linear Motor-Driven Aerostatic Stage (LMDAS) has been extensively applied in high-precision measuring instruments and precision equipment requiring exceptional velocity and acceleration performance. This is attributable to its low friction, high precision, and high dynamic performance. Nevertheless, the LMDAS is vulnerable to external disturbances due to weak damping of the aerostatic guideway's gas film. This weakness directly affects the positioning accuracy and dynamic characteristics of the system and results in its flutter. This paper proposed a method for improving the overall damping of the system and its ability to suppress vibrations by enhancing the damping of the driving direction via combining active control and passive regulation. The active control algorithm based on the active disturbance rejection controller and the passive regulation technology based on the linear motor were presented, respectively. Both numerical and experimental studies demonstrated that this approach was superior to the traditional PID controller, commonly employed in LMDAS, regarding steady-state performance, dynamic response, and vibration suppression. In particular, this method demonstrated a significant improvement in reducing the maximum vibration acceleration of the LMDAS in all three directions, achieving a 50 % reduction compared to the conventional PID controller. [Display omitted] • Enhancing driving direction's damping to improve the vibration suppression ability. • The method combines active control and passive regulation to enhance damping. • The active controller based on active disturbance rejection controller was developed. • The passive regulation technology of electromagnetic damping was proposed. • Method's effectiveness for improving vibration suppression is verified. [ABSTRACT FROM AUTHOR]

Asymptotic normalization coefficients (ANCs) and spectroscopic factors (SFs) for proton binding in the ground states of the 1 5 N, 1 6 O, 1 9 F and 3 2 S nuclei were extracted from an analysis of available literature data on the (3He , d) and (n , d) reactions. Modified DWBA and ADWA analyses showed that the proton transfer process is more peripheral in (3He,d) reactions and therefore more reliable for determining the ANCs. As a result of the comparative analysis of peripheral (3He,d) and nonperipheral (n , d) reactions, the uncertainties in the extracted values of SFs were reduced. The data obtained are to be used in calculating astrophysical S-factors for radiative proton capture. [ABSTRACT FROM AUTHOR]

Summary: Most of the existing adaptive filter algorithms pay more attention to improving performance while ignoring the computational complexity and the impact of the impulsive environment. When encountering the impulsive noise environments, the performance of traditional nonlinear adaptive filter may be significantly reduced and usually needs high computational cost. Therefore, this article proposes a hyperbolic tangent Leclerc robust nonlinear adaptive filter based on the low complexity decomposable Volterra model (HTLNAF‐DVM). The filter is implemented by imposing a rank‐one structure on the full Volterra model to get a product of linear filters, and employs a hyperbolic tangent Leclerc function as a robust norm to effectively improve the robustness against the impulsive noise. In addition, we give the theoretical analyses of the steady‐state mean‐square performance of the proposed HTLNAF‐DVM. Finally, the simulation results prove that the proposed HTLNAF‐DVM algorithm has better performance than the existing algorithms and fit well with the theory. [ABSTRACT FROM AUTHOR]

In view of the typical requirements of large high-clearance sprayers, such as those operating in poor road conditions for farmland plant protection and at high operation speeds, reducing the vibration of sprayer suspension systems has become a research hotspot. In this study, the hydro-pneumatic suspension (HPS) of large high-clearance sprayers was taken as the object, and a variable universe T-S fuzzy controller with real vehicle vibration data as input was proposed to control suspension motion in real time. Different from traditional semi-active suspension, based on the characteristics of variable universe extension factors, a training method combining the artificial fish swarm algorithm and the back propagation algorithm was used to establish a fuzzy neural network controller with precise input to optimize the variable universe. Then, the time-domain and frequency-domain response characteristics of HPS were analyzed by simulating the special road conditions typical of farmland. Finally, the field performance of the sprayer equipped with the new controller was tested. The results show that the error rate of the AFSA-BP algorithm in training the FNN could be reduced to 3.9%, and compared with a passive suspension system, the T-S fuzzy controller improved the effects of spring mass acceleration, pitch angle acceleration, and roll angle acceleration by 18.3%, 23.3%, and 27.7%, respectively, verifying the effectiveness and engineering practicality of the active controller in this study. [ABSTRACT FROM AUTHOR]

Constrained by the computing power, adaptive active noise control systems often have a low sampling rate. Therefore, reconstruction filters and anti-aliasing filters with fixed parameters are generally adopted to eliminate the mirror noise and aliasing noise, respectively; however, they may boost the group delay of the system. A dynamic matching method based on dual sampling rates is proposed to dynamically adjust the parameters of the reconstruction and anti-aliasing filters, according to the characteristics of the primary sound source, for a compromise between high-frequency noise and group delay. In digital high-sampling-rate regions, data that include high-frequency information are analyzed regularly, following which the parameters of the reconstruction filters and those of the anti-aliasing filters are dynamically matched. In digital low-sampling-rate regions, the estimation of the secondary path transfer function is updated. The results of laboratory experiments show that the proposed method not only can suppress the mirror and aliasing noise for primary sound sources with different spectra, but can also effectively reduce the group delay and improve the noise reduction performance of a system. [ABSTRACT FROM AUTHOR]

Active noise reduction in the existential of impulsive noise poses a serious challenge in a distributed scenario. The conventional centralized multichannel active noise control (MANC) and diffusion filtered-x least mean square (F-xLMS) based distributed active noise control (DANC) systems exhibit deteriorated performance in the existence of impulse noise and outliers. In this regard, a diffusion least logarithmic hyperbolic cosine function (D-Llncosh) based algorithm for DANC system is developed with the goal of mitigating noise in such an environment. It utilizes natural logarithmic of hyperbolic cosine function integrating the mean square error (MSE) and mean absolute error (MAE) as the cost function depending upon the parameter value. The weight update rule of the introduced D-Llncosh-based DANC system is hereby derived. The convergence analysis of the suggested method is also carried out. The proposed system is validated for various types of impulsive noise under different scenarios. The extensive simulation results assert the efficacy of the posited DANC system in comparison with the existing techniques. The suggested method achieves quicker convergence and approximately a 1–5 dB improvement is the noise reduction for different noise scenarios as evident from the obtained results. [ABSTRACT FROM AUTHOR]

The work tests and analyzes the engine noise distribution in a car with and without active control, where the car's built-in loudspeakers are used as secondary sources to attenuate the engine noise inside the whole cabin. To analyze the sound field in detail, the noise level at 52 microphones on the passenger ears' plane is measured and presented. The distributions of the primary overall noise, first four order noise of the engine, and the sound field generated by the subwoofer at engine orders' frequencies are reported, and the noise reductions corresponding to overall engine noise and each order's noise are analyzed. Besides, jury test is presented and compared with the calculated loudness of residual noise to evaluate the acoustic comfort improvement of the system. The experiment results show that the overall noise reduction is achieved inside the whole cabin by controlling the first four orders' engine noise. The average noise reduction of the measurement area inside the cabin is 6.2 dB and 2.9 dBA. Besides, 93% of the participants feel the acoustic comfort is improved with the active noise control system on. [ABSTRACT FROM AUTHOR]

The performance of a narrowband active noise control (NANC) system can be significantly degraded due to the frequency mismatch (FM). In this paper, the statistical performance of a typical FxLMS-based NANC system in the presence of FM is analyzed in detail. Difference equations governing the system dynamics and closed-form steady-state mean-square error expressions are derived and discussed. The stochastic analysis results reveal that the FM introduces small troublesome sinusoids into the NANC system. The controller has to track these sinusoids to minimize the residual noise, which leads to a serious performance deterioration. As a by-product, the optimal step sizes that minimize the effect of a relatively small FM are also derived. The findings significantly enrich our understanding of the stochastic behavior of the FxLMS algorithm in the presence of FM and also provide some useful information for NANC system design. Extensive simulations are conducted to confirm the validity of the analytical findings. [ABSTRACT FROM AUTHOR]

The fixed feedback control has been widely used for active noise-canceling earphones due to its simple structure and higher performance for low-frequency noise reduction. Although some disk-based models have been done to design robust controllers, they usually overestimate the uncertainty and thus limit noise reduction. In this article, we analyze the distribution of measured secondary paths and propose the tri-rectangle model to provide the uncertainty bound. The cascade biquad controllers are optimized by solving the multi-constrained optimization problem, with H2 performance criteria and H∞ performance criteria and constraints of secondary path uncertainty and noise amplification. Simulation and experiment results show that the proposed tri-rectangle model provides a more accurate estimate of the uncertainty than disk-based models, and the designed controller achieves good noise attenuation with acceptable noise enhancement and strong stability. [ABSTRACT FROM AUTHOR]

In this paper, the novel fixed-time anti-disturbance control scheme is proposed for a class of stochastic systems subjected to multiple disturbances and faults, and the disturbances consist of derivative-bounded disturbances and multiply noise. Based on the pole placement method, the fixed-time disturbance observer (Fixed-time DO) is devised to estimate derivative-bounded disturbances and an adaptive law is employed to approach the time-varying fault. Then a composite fixed-time controller is constructed to make the system converge to equilibrium position within a pre-specified time. At the same time, simulation examples illustrate that the proposed controller is effective and the convergence time is not related to the initial states of the system. • Disturbances, fault and multiply noise are considered simultaneously, and the disturbancesare assumed to be derivative-bounded, which expands the application scopes of the control system. • The fixed-time DO is designed to estimate the derivative-bounded disturbances. At the same time, the time-varying fault is approximated by designing an adaptive law. • A novel fixed-time anti-disturbance control strategy is proposed, which can make the system stable within a pre-specified time. Moreover, the convergence time is irrelevant to the initial state. [ABSTRACT FROM AUTHOR]

In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo motor. Firstly, a negative feedback mathematical model was established for the obstacle avoidance control system. Then, the nonlinear state error feedback (NLSEF) parameters in the fuzzy ADRC were intelligently optimized by the BPNN algorithm. In this way, a fuzzy ADRC controller based on BPNN optimization was formed to optimize the control process of a servo motor. Matlab/Simulink (R2022b) was used to complete the simulation model design and parameter adjustment. Consequently, the response time was 0.089 s using the BPNN fuzzy ADRC controller, which was shorter than the 0.303 s of the ADRC controller and the 0.100 s of the fuzzy ADRC controller. The overshoot was 0.1% using a BPNN fuzzy ADRC controller, which was less than the 2% of the ADRC controller and the 1% of the fuzzy ADRC controller. After noise signal interference was introduced into the control system, the regression steady state time of the BPNN fuzzy ADRC controller was 0.22 s, which was shorter than the 0.56 s of the ADRC controller and the 0.45 s of the fuzzy ADRC controller. A hardware-in-the-loop simulation experimental platform of the obstacle avoidance control system was constructed. The experiment results show that the servo motor control system has a fast dynamic response, small steady-state error and strong anti-interference ability for obstacle avoidance at the target height. Then, the control system error was within the allowable range. The servo motor control effect of the BPNN fuzzy ADRC was better than the ADRC and fuzzy ADRC. This optimized servo motor control method can provide a reference for improving the obstacle avoidance control effect problem of no-tillage seeders in stubble breaking operations on rocky desertification areas. [ABSTRACT FROM AUTHOR]

Tire–road noise deteriorates the sound quality of a vehicle's interior and affects the driving safety and comfort. Obtaining low interior noise is a challenge for passenger car manufacturers. Traditional passive noise control (PNC) is efficient for canceling high frequency noise but not useful for low frequency noise, while active noise control (ANC), according to the residual error signal, can generate an anti-noise signal to reduce the original noise. Most research has focused on improving the control effect for a feedforward ANC system. However, this paper emphasizes a feedback ANC system based on a signal microphone sensor. There are two main contributions in this study to improve automotive cabin sound comfort. One is that the algorithm of the feedback ANC system using a single microphone sensor without a reference noise signal is proposed based on the Filtered-x Least Mean Square method. The other is that the algorithm applies additive random noise online to estimate the secondary path model. A simulation was implemented based on measured real road noise data, and the simulation results indicate that the proposed feedback ANC system with the single microphone sensor can effectively attenuate road noise. This study shows the feasibility of applying a feedback ANC system in automobiles to increase the cabin sound quality. [ABSTRACT FROM AUTHOR]

Introduction: With the rapid development of the gearbox manufacturing industry, the internal gear response has received attention, and the control of meshing noise during gear operation has been studied. Conventional noise reduction methods are usually based on gear order, and with the improvement of gearbox manufacturing technology, this method gradually becomes difficult to cope with a wide range of data. Methods: To expand the search domain of noise control systems, this study combines gear response and gear order, and adds the condition of gear uniform load. For common noise reduction problems in composite systems, this study improves the time-varying stiffness excitation mechanism and generates a coupled system. Results: Finally, this study conducts experiments on the Gmnoi dataset and compares it with three systems including quantum genetics to verify the superiority of the proposed system. The suppression effects of the four systems on gear meshing noise were 98.4%, 95.8%, 93.5%, and 92.7%, respectively. Their highest performance for different gear groups was 623, 514, 406, and 423, respectively. Discussion: The experimental results showed that the proposed coupling system has strong robustness and high accuracy in controlling gearbox meshing noise, and is of great significance in reducing noise pollution and improving the working environment of the gearbox. [ABSTRACT FROM AUTHOR]

An extensive experimental investigation was undertaken to control the flow and noise characteristics influenced by vortex shedding from a circular cylinder by implementing air blowing at the base of the cylinder. The study synchronised near-field pressure and far-field noise measurements with the wake velocity field to understand the noise reduction mechanism of base blowing. Surface pressure fluctuations were measured using pressure taps distributed around the cylinder's circumference through a remote-sensing method, while velocity measurements were obtained using planar particle image velocimetry at the midspan to examine the flow dynamics. The study unveiled the crucial role of near-field pressure, particularly induced at the shoulders of the cylinder, in generating far-field noise. The rapid vertical flow movement, arising from the interaction between shear layers, was identified as a mechanism responsible for inducing surface pressure fluctuations. This phenomenon occurred as high-momentum fluid moved from the free stream into the interior of the vortex-formation region. By applying base blowing, a remarkable reduction in both near-field pressure and far-field noise was achieved at the fundamental vortex-shedding frequency, with reductions of approximately 20 and 25 dB, respectively, compared with the baseline. Additionally, base blowing caused the shear layers to roll up farther downstream than in the baseline by decreasing the entrainment of fluid-bearing opposite vorticity by the shear layer upstream of the growing vortex. Consequently, there was a substantial decrease in turbulent kinetic energy and Reynolds stress near the cylinder, resulting in slower vertical flow movement and weaker near-field pressure. [ABSTRACT FROM AUTHOR]

This paper introduces an energy conversion inspired vibration control methodology and presents a representative prototype of tunable bi-stable energy converters. This work is concerned on improving energy conversion and absorption performance of tunable bi-stable clustered energy conversion inspired dynamic vibration absorbers (EC-DVAs). The deterministic parametric analysis of the energy transfer performance of clustered EC-DVAs is conducted. The nonlinear energy capture behaviors including transient energy transfer and snap-through motions are studied. It can be uniformly understood that the excellent energy transfer and conversion efficiency can be achieved by modulating the length ratio from 0.6 to 0.8. It is found that clustered EC-DVAs with four uniform cells and constant length ratio σ = 0.8 can obtain significant energy conversion and targeted energy transfer performance without requiring tedious active regulation when the system is subjected to wide-amplitude impulsive scenarios. Therefore, the proposed tunable bi-stable system composed of clustered EC-DVAs with state-of-the-art adaptive control strategies is a potential alternative for energy reuse and vibration suppression of mechanical components exposed to kaleidoscopic impulses. [ABSTRACT FROM AUTHOR]

In order to improve the interior sound quality of Electric Vehicles (EV), solve the problem of low sense of power and comfort of the interior sound as well as the large electromagnetic excitation order noise of motor and the sharp interior sound, this article designs a dynamic active sound control system for EV under accelerated driving conditions. Firstly, by comparing and analyzing the sound spectrum characteristics of fuel vehicle (FV) and EV during acceleration, a short-time Fourier transform (STFT) is adopted to extract and synthesize the engine sound. Secondly, the influence of the engine order composition and the energy distribution in the frequency domain on the sound quality of the vehicle is analyzed, and an active control system for sound quality is proposed. And the software and hardware development of the active control sound system is completed. Finally, through real-vehicle testing and verification, the sense of comfort and power of the EV interior sound has been greatly improved during acceleration, and the total value of interior sound can meet the requirement. The sound pressure level and loudness of interior sound have been increased, and the sharpness of the sound inside the vehicle has been improved, with a maximum reduction of 1.0acum. [ABSTRACT FROM AUTHOR]

To improve the convergence speed and noise cancellation performance of the Delayed Filtered-x Least Mean Square (DFxLMS) algorithm in the hardware implementation of compressor noise cancellation system, an improved Delayed Filtered-x Least Mean Square algorithm based on field-programmable gate array (FPGA) is proposed. The algorithm uses a parallel finite impulse response (FIR) filter structure and DSP48E1 module to implement the parallel multiplication and addition operation, which decreases hardware resource consumption and increases the system's processing speed. The cut set retiming technique reallocates the location of delay units to streamline the algorithm structure, reduce the critical path delay and increase the system data throughput. Moreover, through the adaptive filtering of input and feedback signals of the filter module, the noise suppression effect of the algorithm is improved. The results show that the maximum noise cancellation of the algorithm is 10 dB in the compressor noise cancellation system, which is 3 dB higher than the DFxLMS algorithm, and has 1.5 times higher data throughput. In addition, the Lookup Table (LUT), Flip-Flop (FF), and power consumption decreased by 18.5%, 33.6%, and 26.6%, respectively, and converged 1.8 times as fast. [ABSTRACT FROM AUTHOR]

The control of systems like: bipedal locomotion robots, space launch vehicle, offshore wind turbines, and active vibration control systems in buildings and bridges, have to ensure, besides stability and accuracy, the system's insensitivity to parameters' uncertainties, unmodeled dynamics, external disturbances, and measurements noise. In such systems analysis and controller design, a triple inverted pendulum can be used as a benchmark to mimic systems characteristics and effect of different sources of uncertainty. μ-synthesis is a robust control method which seeks a controller that minimizes the robust H-infinity performance of the closed-loop system through D-K iteration. The D-K iteration is not guaranteed to converge to a global, or even local minimum. Hence this paper proposes the enhancement of controller design by applying gazelle optimization technique to shape the fictitious output by determining the parameters of the performance weighting matrix. The incorporation of optimization with controller design allows avoiding getting unnecessarily conservative system at the expense of performance. The developed control system is simulated using Matlab R2023b for different scenarios of system uncertainty. The results show that the requirements of robustness and performance can be balanced through the right choice of cost function. The robust performance measure obtained is 0.6432 which leads to good response for both stabilization and tracking in the presence of uncertainty. The results also show that even the baseline μ-synthesis design achieves higher robust stability margin about 2.818, the proposed optimized method stabilizes the system with overshoot been reduced by 67.65% and steady state error reduced by 5.69% without sacrificing robustness. [ABSTRACT FROM AUTHOR]

When the adaptive filter length is increased, the calculation complexity increases rapidly because the relationship between the calculation and the adaptive filter length N contains a power function with no secondary path identification algorithm. Under the basic premise of unreduced noise reduction, herein, a simplified frequency-domain feedback active noise control algorithm is proposed. To reduce the computation complexity, the total delay is adopted as the estimated secondary path; the filtered reference signal is produced in the frequency domain by using multiplication to replace convolution calculation in the time domain and then updating the adaptive filter coefficients in the frequency domain. Therefore, the computational complexity has a logarithmic function with the increased adaptive filter length in the proposed algorithm. If the adaptive filter length is 512, the existing WSMANC algorithm's calculation is 271,360 real number multiplications, while that of the proposed algorithm is only 38,912 real number multiplications. To verify the proposed algorithm's stability, convergence speed, and noise reduction, the single-frequency noise, narrowband white noise, and narrowband pink noise, respectively, are used as the primary noise types in the simulations. The results show that (1) the proposed SFDFBANC algorithm can obtain similar noise reduction performance to existing algorithm, (2) the convergence rate is faster than existing algorithm, and (3) if the adaptive filter length is more than 64, the proposed algorithm exhibits a lower computational complexity. [ABSTRACT FROM AUTHOR]

The audio codecs in conventional active noise control systems can only use a high-speed single sampling rate, so the modeling filters and adaptive filters in the system require a longer filter length and the time reserved for iterations is very short, which leads to high computational complexity and difficulty in real-time online computation. To address these problems, a multi-sampling rate system is firstly proposed, which uses extraction and interpolation methods to provide lower sampling rates for active noise control system, reducing the computational complexity while increasing the time available for iterative computation. Based on the minimum mean square algorithm, the secondary sound channel is modeled and a multi-sampling rate double-channel feedback active noise control system is modeled too. Finally, an experimental platform based on seat headrest and digital signal processor is built to experimentally verify the effectiveness of the double-channel active noise feedback control system with multi-sampling rates. The experimental results show that for low frequency noise, the noise reduction system can achieve a noise reduction of about 15 dB in the corresponding frequency band, and the noise reduction is obvious. [ABSTRACT FROM AUTHOR]

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.

Active noise control (ANC) aims to reduce a noise source at a listening point by destructive interference with a reverse phase noise emitted by one or more control devices. ANC systems cover a wide range of applications, such as industrial plants, vehicle cabins, headphones. Usually, in these applications the control devices are loudspeakers, and the error signals come from microphones. In outdoor applications, this solution comes with some limitations, such as the ageing of the loudspeakers due to humidity, temperature, rain and dust, and the presence of wired connections between the error sensors and the control system. This paper presents a noise reduction solution for a cogeneration plant, consisting of an Active Vibration Control (AVC) system applied to the cogenerator enclosure. To overcome the above limitations, electrodynamic shakers are used as controllers and an accelerometer as error signal. The control algorithm is a single reference, Single Input, Multiple Output (SIMO) Filtered-X Normalized Least Mean Square (FxNLMS), implemented in a Virtual Studio Technology (VST) plugin. VST plugins work in real-time in Digital Audio Workstation (DAW) software installed on personal computers, therefore without the need for low-level implementation on dedicated electronic boards, thus reducing the development time and the cost of the equipment. First, the effectiveness of the solution is demonstrated in a laboratory experiment. Subsequently, the results obtained on the cogenerator are presented, which show remarkable performance, with a noise reduction up to −17.5 dB on the main disturbing frequencies.

Active Noise Control (ANC) is an effective technique for removing undesirable disturbances based on destructive interference between two noises (i.e., the superposition principle). To reduce the Non-Gaussian distribution of impulsive noises, the ANC is implemented using a prominent Filtered Cross Least Mean Square (FxLMS) method that relies on reducing the unwanted noise. The standard FxLMS method fails to adapt to its specifications, resulting in poor convergence and instability in the presence of impulsive noises and a non-linear response from the ANC system’s components. The Least Square family of Recursive Least Square (RLS) increases ANC performance by offering superior convergence performance to traditional stochastic algorithms. This paper proposes a novel technique called the recursive non-linear active threshold-based and modified gain FXRLS (NAMGFxRLS) algorithm to overcome the inadequacies of impulsive noise and non-linearity issues in the ANC. The suggested technique aims to automatically modify weights by the various sample processes, i.e., employing the FxRLS algorithm’s updated gain to adjust the error and reference signals and appropriately deploying the threshold. The potential of the suggested strategy is proved by simulated results of convergence speed, stability, and excellent Mean Noise Reduction of roughly 52.8 % for various noises when compared to previous approaches, notably large impulsive noises.

The filtered-x normalized least mean square (FxNLMS) algorithm is widely used in active noise control (ANC) systems. However, for cyclostationary input signals, the noise reduction performance of the FxNLMS algorithm is affected by the periodic power of cyclostationary inputs, which leads to a larger steady-state mean square deviation (MSD) than the filtered-x least mean squares (FxLMS) algorithm. In this paper, a new adaptive combination algorithm of FxNLMS and FxLMS is proposed to suppress cyclostationary input noise with fast convergence and low steady-state MSD. The mixing parameter for combining the two algorithms is obtained by minimizing the energy of the posterior error and updated using the time-average method. The mathematical analysis including the mean square convergence and computational complexity are performed. The simulation results show that the proposed combined-FxNLMS-FxLMS algorithm can efficiently combine the respective advantages of FxNLMS and FxLMS algorithms and improve the noise reduction performance of the ANC system.

Given an unstable (or stable) impulsive Markovian switching system (IMSS), can it be stabilized (or destabilized) by noise? This problem has not been studied. In view of this question, this article will establish the noise stabilization and destabilization strategies of IMSS, and also study the impulsive control realized by adjusting the impulse intensity. It is worth noting that the impulse jump is the random impulse whose jump value depends on the mode, and the noise stabilization and destabilization are realized by periodic stochastic control based on Lévy noise. In order to establish these new results, we use the time‐inhomogeneous property of coefficients of the controlled system and the ergodicity of discrete‐time Markov chains based on impulse trigger time. In addition, the periodic stochastic control on diffusion and jump terms can be applied to design intermittent stochastic control. [ABSTRACT FROM AUTHOR]

The uncertainty in the secondary path of active noise control (ANC) headphones affects the waterbed effect and stability of the feedback system. This study focuses on the uncertainty of the secondary path when real users wear headphones and proposes a new uncertainty constraint based on the measured results of the secondary path transfer function under different wearing conditions of a dummy head and limited subjects. This constraint and a cascaded second-order infinite impulse response filter with fixed coefficients are used to formulate a control strategic function, which is optimized using the Improved Grey Wolf Optimizer (IGWO) algorithm to obtain the optimal controller with better noise reduction performance. The proposed method and simulation model are validated based on the experimental test results. The results demonstrate that the safety factor and waterbed suppressing factor contained in the proposed uncertainty constraint ensure more stable noise reduction and effective suppression of the waterbed effect for new subjects without a priori data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, a new approach for the solution of the electromagnetic interference impact problem in closed-loop control systems with proportional-integral-derivative (PID) controllers is proposed. The approach is based on the application of a combined controller that consists of three controllers: PID, PI and I, when only one of them is operating at a time, and which one is operating determines the control error value. The proposed combined controller allows for achieving better resistance of the control system to the noise signals produced by electromagnetic interference compared to PID and PID with low-pass filters in derivative term controllers without deteriorating the dynamic performance of the control system. The operation of the controller has been analysed using simulation and experiments with plants, the dynamics of which are described by first-order plus dead-time transfer functions. [ABSTRACT FROM AUTHOR]

Due to the increasing noise pollution, noise control has drawn more and more public attention. Different from traditional methods to reduce noise energy such as active and passive noise control, suppressing noise annoyance through adding sounds is another choice. In previous studies, water sounds are usually used to adjust the annoyance of traffic noise, but the suppression effect of the water sounds varies from person to person. Low-frequency tonal noise is one kind of typical and common mechanical noise, i.e., substation noise, whose annoyance is caused by tonal perception and higher energy in low frequency is difficult to suppress even though adding sounds. In this study, different controllable sounds (water sounds and narrow-band color noises) with different masker-to-noise ratios (MNRs) were added to low-frequency tonal noise to investigate the annoyance suppression effect through listening tests. Furthermore, in order to evaluate the quantitative suppression effect, the standard sample method (SSM) was applied in the listening tests to convert the annoyance difference into the difference of equivalent sound pressure level of 1 kHz and 70 dB pure tone ( Δ S P L 1 k H z , 70 d B ). Results show that different kinds of water sounds and narrow-band color noises effectively reduce noise annoyance. The optimal result comes from adding one kind of water sound, which reduces the annoyance of substation noise by 27.24%, equivalent to reducing Δ S P L 1 k H z , 70 d B by 6.5 dB. Finally, the annoyance prediction model of combined noises is established to choose controllable sounds and predict the suppression effect of annoyance. [ABSTRACT FROM AUTHOR]

Purpose: The goal of this work is to propose a local Active Noise Control system for an aircraft seat's headrest. This system should be able to extend the quiet zone beyond the physical microphones that can be placed behind the passenger's head due to space constraints. Furthermore, the control algorithm should perform efficiently when non-linear phenomena exist during the sound propagation. Methods: A functional link neural network and a computationally efficient Multiple Input Multiple Output approach are used for the system's implementation. The quiet zone is extended by linearly estimating the acoustic pressure in front of the headrest surface. The main novelty of this paper is the combination of these methods in an attempt to improve acoustic pressure attenuation performance, while keeping computational complexity low. Results: The proposed control algorithm has been evaluated through numerical simulations, including Finite Element Method and experimental tests at an aircraft cabin mock-up. The results show that for a real-world acoustic disturbance, a 10 dB reduction in sound pressure level was achieved 10 cm away from the headrest surface. In addition, the attenuation of some harmonics can reach 20 dB and in most cases is bigger than the linear FxLMS algorithm. Conclusions: To summarize, it has been demonstrated that a multichannel functional link neural network using a simple virtual sensing technique can efficiently attenuate synthesized and real world acoustic disturbances captured in a tilt-rotor aircraft's cabin. Finally, it can create an adequate quiet zone for gentle head movement while maintaining its stability. [ABSTRACT FROM AUTHOR]

Maglev transportation is a highly promising form of transportation for the future, primarily due to its friction-free operation, exceptional comfort, and low risk of derailment. Unlike conventional transportation systems, maglev trains operate with no mechanical contact with the track. Maglev trains achieve levitation and guidance using electromagnetic forces controlled by a magnetic levitation control system. Therefore, the magnetic levitation control system is of utmost importance in maintaining the stable operation performance of a maglev train. However, due to the open-loop instability and strong nonlinearity of the control system, designing an active controller with self-adaptive ability poses a substantial challenge. Moreover, various uncertainties exist, including parameter variations and unknown external disturbances, under different operating conditions. Although several review papers on maglev levitation systems and control methods have been published over the last decade, there has been no comprehensive exploration of their modeling and related control technologies. Meanwhile, many review papers have become outdated and no longer reflect the current state-of-the-art research in the field. Therefore, this article aims to summarize the models and control technologies for maglev levitation systems following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) criteria. The control technologies mainly include linear control methods, nonlinear control methods, and artificial intelligence methods. In addition, the article will discuss maglev control in other scenarios, such as vehicle–guideway vibration control and redundancy and fault-tolerant design. First, the widely used maglev levitation system modeling methods are reviewed, including the modeling assumptions. Second, the principle of the control methods and their control performance in maglev levitation systems are presented. Third, the maglev control methods in other scenarios are discussed. Finally, the key issues pertaining to the future direction of maglev levitation control are discussed. [ABSTRACT FROM AUTHOR]