Active noise control (ANC) is achieved by introducing a cancelling "antinoise" wave through an appropriate array of secondary sources. These secondary sources are interconnected through an electronic system using a specific signal processing algorithm for the particular cancellation scheme. ANC has application to a wide variety of problems in manufacturing, industrial operations, and consumer products. The emphasis of this paper is on the practical aspects of ANC systems in terms of adaptive signal processing and digital signal processing (DSP) implementation for real-world applications. In this paper, the basic adaptive algorithm for ANC is developed and analyzed based on single-channel broad-band feedforward control. This algorithm is then modified for narrow-band feedforward and adaptive feedback control. In turn, these single-channel ANC algorithms are expanded to multiple-channel cases. Various online secondary-path modeling techniques and special adaptive algorithms, such as lattice, frequency-domain, subband, and recursive-least-squares, are also introduced. Applications of these techniques to actual problems are highlighted by several examples.

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Active noise control: a tutorial review

Active noise control exploits the long wavelengths associated with low frequency sound. It works on the principle of destructive interference between the sound fields generated by the original primary sound source and that due to other secondary sources, acoustic outputs of which can be controlled. The acoustic objectives of different active noise control systems and the electrical control methodologies that are used to achieve these objectives are examined. The importance of having a clear understanding of the principles behind both the acoustics and the electrical control in order to appreciate the advantages and limitations of active noise control is emphasized. A brief discussion of the physical basis of active sound control that concentrates on three-dimensional sound fields is presented. >

Active noise control

HIS article presents an overview and assessment of the technology leading to the development of intelligent structures. Intelligent structures are those which incorporate actuators and sensors that are highly integrated into the structure and have structural functionality, as well as highly integrated control logic, signal conditioning, and power amplification electronics. Such actuating, sensing, and signal processing elements are incorporated into a structure for the purpose of influencing its states or characteristics, be they mechanical, thermal, optical, chemical, electrical, or magnetic. For example, a mechanically intelligent structure is capable of altering both its mechanical states (its position or velocity) or its mechanical characteristics (its stiffness or damping). An optically intelligent structure could, for example, change color to match its background.17 Definition of Intelligent Structures Intelligent structures are a subset of a much larger field of research, as shown in Fig. I.123 Those structures which have actuators distributed throughout are defined as adaptive or, alternatively, actuated. Classical examples of such mechanically adaptive structures are conventional aircraft wings with articulated leading- and trailing-edge control surfaces and robotic systems with articulated manipulators and end effectors. More advanced examples currently in research include highly articulated adaptive space cranes. Structures which have sensors distributed throughout are a subset referred to as sensory. These structures have sensors which might detect displacements, strains or other mechanical states or properties, electromagnetic states or properties, temperature or heat flow, or the presence or accumulation of damage. Applications of this technology might include damage detection in long life structures, or embedded or conformal RF antennas within a structure. The overlap structures which contain both actuators and sensors (implicitly linked by closed-loop control) are referred to as controlled structures. Any structure whose properties or states can be influenced by the presence of a closed-loop control system is included in this category. A subset of controlled structures are active structures, distinguished from controlled structures by highly distributed actuators which have structural functionality and are part of the load bearing system.

Intelligent structures for aerospace - A technology overview and assessment

Two formulations for calculating the total acoustic power radiated by a structure are compared; in terms of the amplitudes of the structural modes and in terms of the velocities of an array of elemental radiators on the surface of the structure. In both cases, the sound radiation due to the vibration of one structural mode or element is dependent on the vibration of other structural modes or elements. Either of these formulations can be used to describe the sound power radiation in terms of a set of velocity distributions on the structure whose sound power radiation is independent of the amplitudes of the other velocity distributions. These velocity distributions are termed ‘‘radiation modes.’’ Examples of the shapes and radiation efficiencies of these radiation modes are discussed in the cases of a baffled beam and a baffled panel. The implications of this formulation for the active control of sound radiation from structures are discussed. In particular, the radiation mode formulation can be used to provide an estimate of the number of independent parameters of the structural response which need to be measured and controlled to give a required attenuation of the radiated sound power.

Radiation modes and the active control of sound power

Some adaptive signal processing applications, such as wideband active noise control and acoustic echo cancellation, involve adaptive filters with hundreds of taps. The computational burden associated with these long adaptive filters precludes their use for many low-cost applications. In addition, adaptive filters with many taps may also suffer from slow convergence, especially if the reference signal spectrum has a large dynamic range. Subband techniques have been previously developed for adaptive filters to solve these problems. However, the conventional approach is ruled out for many applications because delay is introduced into the signal path. The paper presents a new type of subband adaptive filter architecture in which the adaptive weights are computed in subbands, but collectively transformed into an equivalent set of wideband filter coefficients. In this manner, signal path delay is avoided while retaining the computational and convergence speed advantages of subband processing. An additional benefit accrues through a significant reduction of aliasing effects. An example of the general technique is presented for a 32-subband design using a polyphase FFT implementation. For this example, the number of multiplies required are only about one-third that of a conventional full band design with zero delay, and only slightly greater than that of a conventional subband design with 16 ms delay. >

A delayless subband adaptive filter architecture

An algorithm is presented to adapt the coefficients of an array of FIR filters, whose outputs are linearly coupled to another array of error detection points, so that the sum of all the mean square error signals is minimized. The algorithm uses the instantaneous gradient of the total error, and for a single filter and error reduces to the "filtered x LMS" algorithm. The application of this algorithm to active sound and vibration control is discussed, by which suitably driven secondary sources are used to reduce the levels of acoustic or vibrational fields by minimizing the sum of the squares of a number of error sensor signals. A practical implementation of the algorithm is presented for the active control of sound at a single frequency. The algorithm converges on a timescale comparable to the response time of the system to be controlled, and is found to be very robust. If the pure tone reference signal is synchronously sampled, it is found that the behavior of the adaptive system can be completely described by a matrix of linear, time invariant, transfer functions. This is used to explain the behavior observed in simulations of a simplified single input, single output adaptive system, which retains many of the properties of the multichannel algorithm.

I.M. Stothers

Papers

A multiple error LMS algorithm and its application to the active control of sound and vibration

In-flight experiments on the active control of propeller-induced cabin noise

In-flight experiments on the active control of propeller-induced cabin noise

Preliminary results of in-flight experiments on the active control of propeller-induced cabin noise