This Ph.D. thesis includes fundamental considerations about topologies, algorithms, implementations, methods etc., that can enter in the next generation of active control (AC) systems. Specifically, a new variant of feedforward control referred to as confined feedforward active control (CFFAC) is proposed. This topology is constituted from a set of reference sensors that are positioned on a surface that completely confines the desired zones of quite. A set of performance sensors monitors the achieved noise reduction. This CFFAC topology in turn is embedded in a multiple-input and multiple-output (MIMO) system that facilitates both feedforward and feedback control. The general system is then referred to as hybrid MIMO confined-feedforward feedback (HMIMOCFFFB) active noise reduction (ANR) system. The investigation of a multi-channel ANR system with hybrid feedforward and feedback topologies is motivated by requirements of high ANR attenuation in extreme noise environments as typically experienced onboard airborne military platforms. Noise recordings acquired on such platforms reveal very high sound pressure levels often exceeding 140 dB re. 20 μPa. Moreover, these noise signals exhibit large temporal as well as spatial variations. Inherent limitations are related to the use of stand-alone feedback AC implementation commonly applied in modern ANR headset. In such systems the anti-noise signal is notoriously behind the primary disturbance in time. Accordingly, in demanding military applications requirements on more advanced and effective ANR system designs prevail. The achievable ANR performance in a feedforward system (FFS) is to a large extent determined by the degree of coherence between the set of reference sensors and the set of error sensors (or performance sensors). Accordingly, this thesis includes a number of coherence analysis that are based on diffuse sound field measurements in a reverberant chamber and measurements conducted onboard a CH-47D Chinook helicopter. From these coherence analysis it can be concluded that the CFFAC system with 10 reference sensors applied to pilot helmets potentially provides approximately 25 dB noise reduction at 100 Hz decreasing to approximately 10 dB attenuation at 900 Hz. Moreover, there is no apparent sign of saturation of the noise reduction with an increasing number of reference sensors. Accordingly, by using more reference sensors the spatial sampling rate is increased which in turn most likely also will lead to an increased ANR bandwidth. The hybrid system is also constituted from a continuous-time feedback system (FBS) and a discrete-time FBS. The continuous-time FBS is primarily responsible for additional broadband noise reduction, whereas the discrete-time FBS primarily is responsible for the attenuation of periodic signals. Owing to the requirement on causal operation of a physical AC system time delays will also to a large extent determine the achievable performance in FFS design and in particular in FBS design. A quantity referred to as the spatially-weighted-averaged acquisition lead time is introduced to represent the averaged time-advance obtained by each reference sensor relative to each performance sensor involved in the proposed CFFAC system. A problem exist when one attempts to model a physical spatially distributed system with no obvious input and output channel definition by a finite lumped-elements multi-channel system. Usually, no unique transfer function x exist as the system is not point-wise excited, but excited over an area as in the case of diffuse sound field illumination. A new method for acoustical signal processing that is referred to as joint-channel residual spectral analysis (JCRSA) is developed. The JCRSA method is used for the extraction of joint signal information from different observation positions in space. The idea is to separate each spectrum in a coherent spectrum and a residual spectrum. The contents of the coherent spectrum can be obtained from a linear superposition of the other signals, whereas the residual spectrum bears information that is unique to each specific channel. In a specific example a system consisting of 10 reference sensors flush-mounted on a Gentex HGU-55/P helmet that in turn is mounted on a head and torso simulator (HATS), is exposed to diffuse sound field illumination. By applying the JCRSA method the spatially-weighted-averaged acquisition lead times provided by the reference sensors relative to the performance sensors are estimated to be as much as 800-900μs. The thesis also includes a detailed description of a new idea for a computational efficient implementation of a multi-channel system in which the adaptive filters for adaptive control as well as the adaptive filters used for plant modeling are allowing to take different lengths. A new and more general variant of the affine projection algorithm has been developed. This adaptive filter algorithm that is denoted by multiple-channel-αγΠ-affine projection algorithm includes parameters for both weight-driven and control-effort-driven leakage, adaptive tap-weight regularization as well as numerical regularization. A simplification of this algorithm leads to the MC-αγΠ-NLMS algorithm that is an extended variant of the NLMS algorithm. Off-line simultaneous system identification capabilities of a complex system involving a total 4 secondary paths, 20 feedback paths and 4 control-performance paths is demonstrated. Different adaptive filters and parameterizations hereof are examined. A novel and general multi-rate adaptive filter for adaptive AC has been developed. Specifically, a system involving 3 different sampling rates has been implemented and the results hereof are presented. In this multi-rate system conversion take place at highly oversampled rates in order to reduce the delays in the secondary paths. The non-adaptive control is performed at a somewhat lower rate. Hereby, a compromise between delays related to the generation of the anti-noise signal and the computational load involved is ensured. Finally, the adaptive control that might be computational intensive takes place at an even slower sampling rate hereby relaxing the requirements on a high bandwidth. It is demonstrated that computational savings as high as 40% can be achieved in a 192, 24, 3 kHz triple-rate system as compared with a 24 kHz single-rate system without sacrificing the ANR performance. It is common engineering practice to apply an assumption of Gaussian distributed signals. However, many phenomena encountered in daily life fall into a generalization of the normal distribution that is referred to as α-stable distributions. Noise sources encountered in the domain of AC are sometimes best fitted to the family of α-stable distributions. This thesis includes a brief technical introduction to the stable distributions and description of the adaptive filter that can be used for AC. Large parts of the HMIMOCFFFB system including the developed methods and algorithms have been implemented in a real-time environment (RTE) that includes a signal processor. Test on the helmet system will continue and a dedicated reference test unit (RTU) for AC is currently being designed.