723 秋の虫の鳴き音を規範とした機械騒音の快音化能動制御 : 快音関数の検討と制御性能向上の試み(振動と音源,OS-5 サイレント工学,総合テーマ「伝統を,未来へ!」)

This paper presents a novel and advanced remotely controlled laboratory for conducting Active Noise Control (ANC), acoustic and Digital Signal Processing (DSP) experiments. The laboratory facility, recently developed by Blekinge Institute of Technology (BTH) Sweden, supports remote learning through internet covering beginners level such as simple experimental measurements to advanced users and even researchers such as algorithm development and their performance evaluation on DSP. The required software development for ANC algorithms and equipment control are carried out anywhere in the world remotely from an internet-connected client PC using a standard web browser. The paper describes in detail how ANC, acoustic and DSP experiments can be performed remotely The necessary steps involved in an ANC experiment such as validity of ANC, forward path estimation and active control applied to a broad band random noise [0-200Hz] in a ventilation duct will be described in detail. The limitations and challenges such as the forward path and nonlinearities pertinent to the remote laboratory setup will be described for the guidance of the user. Based on the acoustic properties of the ventilation duct some of the possible acoustic experiments such as mode shapes analysis and standing waves analysis etc. will also be discussed in the paper.

Commercial active noise and vibration control systems for vehicles are gradually developing, mainly in Japan, for both active noise control of interior sound and active control engine mounts [1]. There have been four Japanese commercial applications: • Active control for booming noise by Nissan in 1991, • Active vibration mount for idle noise and vibration by Toyota in 1997, • Active vibration mount for diesel engines by Nissan in 1998, • Active control for low-frequency road noise by Honda in 2000. Other manufacturers have, of course, developed similar systems, but these have not progressed to production applications. Additionally, active control engine mufflers using cancelling loudspeakers have been successfully demonstrated. A new type of active muffler, using a controlled oscillating butterfly valve in the exhaust line has been developed recently. The future of active control in vehicles is dependent on customers who are willing to pay the higher price for additional noise and vibration control, so that cost reductions are the essential factor in further commercial products. Trends towards fuel efficiency and lighter vehicles run counter to reducing noise and vibration. This has given new interest in active noise control and active control engine mounts as a solution to these conflicting issues.

Hybrid noise control is the combination of both active noise and passive noise control. For high-frequency noise, we generally use passive noise control technique, and for low-frequency noise of less than 200 Hz, we use active noise control technique. Passive noise control is achieved by introducing passive techniques such as enclosures, physical barriers, and silencers to attenuate the undesired noise, whereas active noise control (ANC) is achieved by introducing a cancelling “anti-noise” signal generated by a processor or electronics circuit. Digital filters can be used for this purpose with an appropriate adaptive algorithms such as FXLMS, FXNLMS, FBLMS, SE-FXLMS, and FULMS. The development of an ANC simulation model can be divided into two parts: the modelling of primary noise and the modelling of secondary or counter noise. The anti-noise generated by the ANC system can be modelled only if we have the information of primary noise. For successful simulation of secondary noise, we have to model the control system and its interaction with the surrounding acoustical space. This article describes the practical application of active noise control as well as passive noise control to a server room, greatly reducing the level of noise in the whole area through appropriate adaptive algorithms. The results obtained using different algorithms are compared and analyzed.

Active control for vehicle interior noise based on DWT-FxLMS algorithm using a piezoelectric feedback system

A number of projects have been carried out in Nanjing University to implement active noise control technologies, which include active control of transformer noise, active sound barrier, active noise control in communication chassis, active noise control in natural ventilation windows, active control of large impulsive noise in headset and active noise control in a train compartment. Unlike our previous research, these projects are all funded by industries and the aim is not for doing academic research, but to make commercial prototypes. Various challenges in the implementation of active noise control technologies in these projects to make commercial products are reported and discussed, and main issues to make successful commercial active noise control products are pointed out.

Application of active structural acoustic control (ASAC) for helicopter cabin noise reduction is investigated using both analytical modeling and experimental tests. With ASAC, forced structural inputs from piezoelectric actuators bonded directly to the structure are used to cancel noise. These actuators are small, lightweight, inexpensive, and easily installed on aircraft surfaces, and were found to provide the necessary control authority to cancel cabin tonal noise when installed on thin sound-radiating surfaces. An analytical model of helicopter fuselage was developed and validated using experimental transfer function data. Estimates of noise reductions with different actuator configurations were determined numerically. An optimum actuator configuration was used for flight tests. . An ASAC system was developed and tested on an MD900 Explorer helicopter in flight. Cabin noise reductions were demonstrated with this system. INTRODUCTION With rapidly growing applications for rotorcraft worldwide, both passengers and crewmembers need quiet, comfortable transportation similar to that of commercial fixed wing aircraft. Helicopter interiors are significantly noisier (20 to 30 dB) than those of commercial fixed wing aircraft. Traditionally, aircraft interior noise reduction has been achieved with passive materials and devices, which are usually effective in the high frequency region, and in cases where the cabin noise is generated by aero / thermodynamic sources (e.g., rotors, engines, turbulence, etc.). However, significant weight penalties generally prevent them from being useful in the lowto mid-frequency region, and where the noise sources are mechanically generated (e.g., drive system, gearbox, etc.). In helicopters, for example, noise radiated into the cabin is typically transmitted initially as gearbox vibrations through the airframe structure before it radiates into the acoustic cavity. * Boeing Technical Fellow, Boeing-Long Beach, Member AIAA a Senior Engineer, Boeing-Mesa t Boeing Technical Fellow, Boeing-Mesa, t Roanoke Electric Steel Professor, AIAA Assoc. Fellow Presented at the 40th AIAA Aerospace Sciences Meeting at Reno, NV, January 14-17, 2002. Copyright ©The American Institute of Aeronautics and Astronautics, Inc. All rights reserved Controlling these vibrations before they become airborne sound could prove to be a more practical solution with potentially lower weight penalties than those normally incurred by purely passive techniques. Alternative means of aircraft cabin noise reduction using active control within the airframe structure were investigated for potential application to the uniquely complex helicopter cabin noise problem. Because there has been a lack of an integrated approach in applying advanced noise control approaches to new and existing rotorcraft, an analytical methodology was required for evaluation and design optimization of active (and passive) noise control concepts for helicopter interiors. Advanced noise control technologies, and the analytical means to optimize their integration into new and existing rotorcraft, were necessary to achieve acceptable levels of comfort for passengers and crew. The need for low cost, low weight rotorcraft interior noise control methods exist for both civilian and military helicopters. Military helicopters demand low noise crew stations to meet speech intelligibility requirements for reliable communication which can directly impact mission effectiveness. Specific crew station noise control requirements are specified in the U. S. Army MIL-STD-1294A. Lower interior noise levels on commercial rotorcraft will improve speech intelligibility for crewmembers, 1 American Institute of Aeronautics & Aeronautics (c)2002 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization. thereby improving flight safety. Reduced passenger cabin noise levels will improve passenger acceptance and provide a competitive advantage in the worldwide commuter aircraft infrastructure. HELICOPTER CABIN NOISE A large part of helicopter cabin noise is transmitted as vibratory responses through structural components before it is radiated into the enclosed space. The transmission gearbox is generally considered the major source of interior noise in helicopters, The main objective of this 3-year effort, which started in 1997, was to develop and demonstrate in flight an Active Structural Acoustic Control (ASAC) system, which reduces midfrequency transmission noise on helicopters [1]. The MD900 Explorer helicopter, shown in Figure 1, was selected as the candidate flight test vehicle for evaluating Figure 1. MD900 Explorer helicopter. and demonstrating the potential for active structural acoustic control. It is a 6250 lb., twin engine helicopter with a NOTAR®-equipped anti-torque system. Its main 0

This paper is concerned with the rejection of sinusoidal disturbances of unknown frequency acting at the output of unknown plants. Disturbance rejection is based on an adaptive harmonic steady‐state (ADHSS) algorithm combined with a magnitude/phase locked‐loop (MPLL) frequency estimator. The harmonic steady‐state method assumes that the plant can be approximated by its steady‐state frequency response. For high‐order plants such as those encountered in active noise and vibration control (ANVC), this assumption greatly reduces the number of parameters and enables online estimation of the plant response using simple algorithms. The paper shows that when the MPLL is integrated with the ADHSS algorithm, the two components work together in such a way that the control input does not prevent frequency tracking by the MPLL, and so that the order of the ADHSS can be reduced. Thus, the addition of the MPLL allows disturbances of unknown frequency to be considered without significantly increasing the complexity of the original ADHSS. After analyzing the reduced‐order ADHSS in the ideal case, the equations describing the complete system are considered. The theory of averaging is used to gain insight into the steady‐state behavior of the algorithm. It is found that the system has a two‐dimensional equilibrium surface such that the disturbance is cancelled exactly. A subset of the surface is proved to be locally stable. Extensive active noise control experiments demonstrate the performance of the algorithm, even when disturbance and plant parameters are changing.

Active control has been gaining an increasing role in protecting humans against excessive exposure to lowfrequency acoustic noise. They are designed as supplements to passive isolation techniques or as autonomous solutions. Rapid development of technology including smart materials, electronics, control theory and signal processing algorithms makes active noise control an attractive field for research, development and applications. The plenary lecture presents the problem of machinery and device noise control and author's contribution to this technology. Ideas behind classical and modern control structures and techniques along with guidelines for their choice are demonstrated. Emerging modeling, identification and control problems are also addressed. They are related to complex coupled physical and control phenomena. Concepts and solutions without taking unrealistic assumptions are presented. Perspectives for active control of machinery and device noise are outlined.

Fuzzy active noise modeling and control

The method of reducing automotive intake noise can be classified by passive and active control techniques. However, passive control has a limited effect of noise reduction at low frequency range (below 500 Hz) and is limited by the space of the engine room. However, active control can overcome these passive control limitations. The active control technique mostly uses the Least-Mean-Square (LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time, particularly when the Filtered-X LMS (FXLMS) algorithm is applied to an active noise control (ANC) system. However, the convergence performance of the LMS algorithm decreases significantly when the FXLMS algorithm is applied to the active control of intake noise under rapidly accelerating driving conditions. Therefore, in this study, the Co-FXLMS algorithm was proposed to improve the control performance of the FXLMS algorithm during rapid acceleration. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. The performance of the Co-FXLMS algorithm is presented in comparison with that of the FXLMS algorithm. Experimental results show that active noise control using Co-FXLMS is effective in reducing automotive intake noise during rapid acceleration.

The purpose of this chapter is to apply robust identification and control techniques to perform active noise control (ANC). The identification phase is based on a control-oriented robust identification approach that considers both parametric and nonparametric descriptions of the system, and quantifies the uncertainty. The controller design compares the feedback (FB), feedforward (FF) and hybrid (FB/FF) control structures. The feedback control is synthesized and evaluated in the robust control framework. It is designed using \( \mathcal{H}_\infty \) optimal control as a mixed-sensitivity problem, and the robust performance of the loop is analyzed via the structured singular value (μ). The FF controller is an adaptive identifier, based on the robustly normalized σ-algorithm. Two approaches are developed to decide what control structure is more efficient on a 4-m duct example with broadband noise. In addition, the compromises between identification and control, the inherent limitations of feedback and implementation issues in ANC are explicitly pointed out. Relations between performance, controller order, parametric/nonparametric models and digital signal processor (DSP) implementation are discussed. Theoretical and experimental results on the duct are compared. Finally the gaps that still remain between theory and practice in this type of applications, are outlined.

Active interior noise control for rail vehicle using a variable step-size median-LMS algorithm

For Gaussian noise with random or periodic impulsive interference, the conventional active noise control (ANC) methods with finite second-order moments may fail to converge. Furthermore, the intensity of impulsive noise typically varies over time in the actual application, which also decreases the performance of conventional active impulsive noise control methods. To address these problems, a novel adaptive state detector based post-filtering active control algorithm is proposed. In this work, information entropy with adaptive kernel size is first introduced into the cost function of a post-filtering algorithm to improve its tracking. To enhance the robust performance of adaptive filters when impulsive interference happens, a recursive optimal threshold selecting method is also developed and analyzed by statistical theories. Simulations show that the new method has fast tracking ability in non-impulsive noise environment and keeps robust when impulsive interference happens. It also works well for the impulsive noise of different degrees. Experiment results confirm the effectiveness of the proposed algorithm.

Passive and active interior noise control of box structures using the structural intensity method

Active control of sound through full-sized open windows