Adaptive control of nonlinear Hammerstein model using NLMS filter

A novel technique, using the normalized least mean squares (NLMS) adaptive filter is proposed for adaptive control of the plant dynamics of a nonlinear plant. The nonlinear plant considered belongs to the Hammerstein type of nonlinear system. Stable adaptive controller for a stable discrete time nonlinear plant is designed. The nonlinear plant considered may be non-minimum phase. The controller is composed of an adaptive finite impulse response (FIR) filter in the feedback loop. This adaptive FIR filter is designed online as an L-delay approximate inverse system of the given stable nonlinear plant

Adaptive control of nonlinear Hammerstein model using NLMS filter

A novel technique, using the normalized least mean squares (NLMS) adaptive filter is proposed for adaptive control of the plant dynamics of a nonlinear plant. The nonlinear plant considered belongs to the Hammerstein type of nonlinear system. Stable adaptive controller for a stable discrete time nonlinear plant is designed. The nonlinear plant considered may be non-minimum phase. The controller is composed of an adaptive finite impulse response (FIR) filter in the feedback loop. This adaptive FIR filter is designed online as an L-delay approximate inverse system of the given stable nonlinear plant

Adaptive Control Of Nonlinear Hammerstein Model Using NLMS Filter

A novel technique, using the normalized least mean squares (NLMS) adaptive filter is proposed for adaptive control of the plant dynamics of a nonlinear plant. The nonlinear plant considered belongs to the Hammerstein type of nonlinear system. Stable adaptive controller for a stable discrete time nonlinear plant is designed. The nonlinear plant considered may be non-minimum phase. The controller is composed of an adaptive finite impulse response (FIR) filter in the feedback loop. This adaptive FIR filter is designed online as an L-delay approximate inverse system of the given stable nonlinear plant

Adaptive Algorithms for Intelligent Acoustic Interfaces

Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the “last-mile” problem of telecommunications. One of the main feature of immersive communications is the distant-talking, i.e. the hands-free (in the broad sense) speech communications without bodyworn or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms. The acoustic path modelling entails a set of problems which have to be taken into account in designing an adaptive filtering algorithm. Such problems may be basically generated by a linear or a nonlinear process and can be tackled respectively by linear or nonlinear adaptive algorithms. In this work we consider such modelling problems and we propose novel effective adaptive algorithms that allow acoustic interfaces to be robust against any interfering signals, thus preserving the perceived quality of desired speech signals. As regards linear adaptive algorithms, a class of adaptive filters based on the sparse nature of the acoustic impulse response has been recently proposed. We adopt such class of adaptive filters, named proportionate adaptive filters, and derive a general framework from which it is possible to derive any linear adaptive algorithm. Using such framework we also propose some efficient proportionate adaptive algorithms, expressly designed to tackle problems of a linear nature. On the other side, in order to address problems deriving from a nonlinear process, we propose a novel filtering model which performs a nonlinear transformations by means of functional links. Using such nonlinear model, we propose functional link adaptive filters which provide an efficient solution to the modelling of a nonlinear acoustic channel. Finally, we introduce robust filtering architectures based on adaptive combinations of filters that allow acoustic interfaces to more effectively adapt to environment conditions, thus providing a powerful mean to immersive speech communications

Adaptive Algorithms for Intelligent Acoustic Interfaces

Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the “last-mile” problem of telecommunications. One of the main feature of immersive communications is the distant-talking, i.e. the hands-free (in the broad sense) speech communications without bodyworn or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms. The acoustic path modelling entails a set of problems which have to be taken into account in designing an adaptive filtering algorithm. Such problems may be basically generated by a linear or a nonlinear process and can be tackled respectively by linear or nonlinear adaptive algorithms. In this work we consider such modelling problems and we propose novel effective adaptive algorithms that allow acoustic interfaces to be robust against any interfering signals, thus preserving the perceived quality of desired speech signals. As regards linear adaptive algorithms, a class of adaptive filters based on the sparse nature of the acoustic impulse response has been recently proposed. We adopt such class of adaptive filters, named proportionate adaptive filters, and derive a general framework from which it is possible to derive any linear adaptive algorithm. Using such framework we also propose some efficient proportionate adaptive algorithms, expressly designed to tackle problems of a linear nature. On the other side, in order to address problems deriving from a nonlinear process, we propose a novel filtering model which performs a nonlinear transformations by means of functional links. Using such nonlinear model, we propose functional link adaptive filters which provide an efficient solution to the modelling of a nonlinear acoustic channel. Finally, we introduce robust filtering architectures based on adaptive combinations of filters that allow acoustic interfaces to more effectively adapt to environment conditions, thus providing a powerful mean to immersive speech communications

Basis Function Selection of Frequency-Domain Hammerstein Self-Interference Canceller for In-Band Full-Duplex Wireless Communications

This paper presents a basis function selection technique of a frequency-domain Hammerstein digital selfinterference canceller for in-band full-duplex communications. The power spectral density (PSD) of the nonlinear selfinterference signal is theoretically analyzed in detail, and a nonlinear self-interference PSD estimation method is developed. The proposed selection technique decides on the basis functions necessary for cancellation and relaxes the computational cost of the frequency-domain Hammerstein canceller based on the estimated PSD of the self-interference of each basis function. Furthermore, the convergence performance of the canceller is improved by the proposed selection technique. Simulation results are then presented, showing that the proposed technique can achieve similar cancellation performance compared with the original frequency-domain Hammerstein canceller and a time-domain nonlinear canceller. Additionally, it is shown that the proposed technique improves the computational cost and the convergence performance of the original frequency-domain Hammerstein canceller