A short historical review of active noise control is followed by brief discussions of the physical principles and basis concepts of technical realisations, grouped by the geometrical system topology (one-dimensional, local and three-dimensional control). The Section on active vibration control starts, again, with historical remarks, followed by examples of technical applications to active mounts, buildings, active and adaptive optics and sound radiation control by structural inputs. The Chapter ends with a discussion of active flow control and some statistical data on publications in the field of active controls.

A coherent method for the analysis and design of active noise control systems, in a three-dimensional nondispersive propagation medium, is presented in this chapter. An analysis of single-input single-output and single-input multi-output control structures is provided. Conditions for the robust operation of such systems on the basis of optimum cancellation are determined. These include the physical extent of cancellation in the medium, relative stability of the inherent feedback loop and controller design. These conditions are interpreted as constraints on the geometric compositions of the system.

In this chapter adaptive feedforward and fixed feedback controllers are reviewed and their robust stability conditions contrasted for active control applications.

This chapter presents stable adaptive schemes in two cases: in the first case reference microphones are available to detect unwanted primary noise, and in the second case no reference microphones are available. The latter case requires no causality condition but its availability is limited to periodic primary noises. In both cases, we propose stability-assured adaptive algorithms to update the adaptive feedforward controllers.

This chapter discusses adaptive control based on control at individual harmonics for cancellation of periodic disturbances. It is shown that the original idea, which has been around for a long time, can be developed much further to produce robust and highly adaptable controllers. First, a review is given of the most accessible literature, after which frequency-selective RLS and LMS methods are presented. Simulations illustrate the effectiveness of the method.

In this chapter a model-free time-domain iterative controller tuning method is introduced and extended for periodic noise cancellation using a two degree of freedom (tdf) controller. An assumption is made that a stabilising initial con troller is known. In each iteration of the design a controller is to be computed with a better performance than in the previous iteration. The first section will introduce the single-input single-output (SISO) problem, give a description of the iterative design method and explain its advantages compared with model-based approaches. The method is extended to a self-tuning method. Con vergence to the optimal solution is improved by introducing frequency selective filters (FSFs). The methodological section is followed by laboratory hardware results to show the usefulness of the different approaches. Finally, conclusions are drawn and future research directions are pointed out.

A commonly used modern approach to the design of controllers is the H_∞ design method; the purpose of this chapter is to present a tutorial on the method and how it can be used for the design of active vibration controllers. The essence of H_∞ design is that it accounts for the dynamical uncertainty of the model used for control. As both physical and empirical models tend to be approximate descriptions of the dynamics of the plant, control design tends to be based on inaccurate models. To achieve good performance this implies that control design should account for possible uncertainties. Design for uncertainty can be done by suitable implementations of H_∞ controllers. This chapter gives a tutorial introduction into H_∞ control for active vibration control. The presentation is much simplified so that the reader can appreciate the approach without extensive reading of the literature.

Neuro-adaptive active control schemes for noise cancellation and vibration suppression are developed and presented in this chapter. Multilayered perceptron and radial basis function neural networks are considered in both the modelling and control contexts. A feedforward ANC structure is considered for optimum cancellation of broadband noise in a three-dimensional propagation medium. Online adaptation and training mechanisms allowing the neural network architecture to characterise the optimal controller within the ANC system are developed. The neuro-adaptive ANC algorithms thus developed are implemented within a free-field noise environment and simulation results verifying their performances in the cancellation of broadband noise are presented and discussed.

In feedforward active noise control problems, it is always necessary to optimise both the physical system and the electronic controller. Optimisation of the physical system is principally concerned with determining the optimum location of the control sources and error sensors. Here, genetic algorithms are investigated for this purpose and modifications to the standard algorithm that are necessary for this application are discussed. Optimising the electronic controller is principally concerned with finding the optimal control filter weights that will produce the most noise reduction when the reference signal is filtered and then input to the control sources. Normally, gradient descent algorithms are used for this purpose. However, for nonlinear systems, such as control sources (loudspeakers or shakers) with significant harmonic distortion, the gradient descent algorithm is unsatisfactory. Here a genetic algorithm is developed specifically for control filter weight optimisation. It is able to handle nonlinear filters and requires no cancellation path identification. The disadvantage is that it is relatively slow to converge.

This chapter introduces a new active noise control system for optimal control of the sound field around a human's head. Geometrical arrangements of error microphones are proposed around the head in a diffused noise field. In order to evaluate the controlled sound field, a rigid sphere theoretical model is used as a human's head in the computer simulations. In previous studies it was shown that the theoretical sphere model maintains a good approximation of a head-related transfer function (HRTF) below about 5 kHz. In this chapter the relationship between the noise reduction and the corresponding configuration of the error microphone and secondary source arrangement is evaluated. As a result, we found that the closely located secondary sources to the sphere head give significant noise reduction in a large control area. A possible arrangement of the error microphones is found to provide an optimal result according to a given control strategy, such as flatness of reduced sound pressure of the quiet zone.

Microvibrations, generally defined as low-amplitude vibrations at frequencies up to 1 kHz, are now of critical importance in a number of areas. One such area is onboard spacecraft carrying sensitive payloads, such as accurately targeted optical instruments or microgravity experiments, where the microvibrations are caused by the operation of other equipment, e.g. reaction wheels, necessary for its correct functioning. It is now well known that the suppression of such microvibrations to acceptable levels requires the use of active control techniques which, in turn, require sufficiently accurate and tractable models of the underlying dynamics on which to base controller design and initial performance evaluation. This chapter describes the development of a modelling technique for either mass or equipment loaded panels and the subsequent use of such models in controller design and basic performance prediction of the resulting feedback control schemes.

Control of vibration inflexible robot manipulators is considered in this chapter. A constrained planar single-link flexible manipulator is considered. Open-loop control strategies based on filtered and Gaussian-shaped command inputs and closed-loop control techniques based on partitioning of the rigid and flexible motion dynamics of the system are developed. These incorporate lowpass and bandstop filtered inputs, Gaussian-shaped inputs, switching surface and adaptive variable structure control, adaptive joint-based collocated and adaptive hybrid collocated and noncollocated control. The control strategies thus developed are tested within simulations and using a laboratory-scale flexible manipulator test rig.

This chapter presents the application of active noise control techniques inside the driver cabins of electric locomotives. A full analysis of the problem is provided. Algorithmic solutions and DSP hardware architectures are included and experimental results are explained.

This chapter presents an application of active noise control techniques for passenger comfort in cars. At the analysis stage nonstationary vibrations of the front and rear wheels are generated by nonuniform road profiles and change of vehicle speed. These propagate through the tyre and the complicated suspension system and finally generate structure-borne noise, impulsive noise, and other low-frequency noise in the interior of the passenger vehicle. These noises produce acoustical resonance in the interior of the passenger car and such resonant noise is called “road booming noise”. Cancellation of this noise is the topic of this chapter.

Sequential and parallel processing techniques for real-time adaptive active control are considered in this chapter. Three different algorithms, namely simulation, control and identification, are involved in the adaptive control algorithm. These are implemented on a number of uniprocessor and multiprocessor computing platforms. The interprocessor communication speed and the impact of compiler efficiency on processor performance are investigated. A comparative assessment is provided, on the basis of the real-time communications performance, computation performance and compiler performance, to lead to merits of design of parallel systems incorporating fast processing techniques for real time active control applications.