Two-Microphone Hearing Aids Using Prediction Error Method for Adaptive Feedback Control

This paper focuses on the adaptive control problems for a class of interconnected systems. The nonLinear uncertainties of the systems under study are bounded by polynomial functions with unknown gains. Firstly, the adaptive feedback controller is proposed to guarantee the stabiLity of the closed-loop system. Then the proposed adaptive idea is extended to robust stabiLizing designing method for a class of large-scale interconnected systems. Based on Lyapunov's theory and LMI method, we can deal with the time-delay term in the system and construct the corresponding adaptive control and state feedback controller. Finally, an illustrative example is given to show the vaLidity of the proposed approach.

An improved block adaptive system for effective feedback cancellation in hearing aids

With the increasing complexity of control systems in industrial process control, traditional controllers are greatly limited in many occasions, so the development of new controller is extremely urgent. This paper combines adaptive feedback control with PID control, and designs a new type of adaptive PID feedback controller. The designed controller is verified by establishing a physical model of the 6-DOF collaborative robot. On the basis of improved adaptive PID feedback control, precise tracking control of multi-DOF cooperative robots is achieved, which improves response speed and eliminates steady-state errors. The designed controller is compared with classical PID control and adaptive control. The steady-state performance, dynamic performance, and anti-interference performance of position control and velocity control are analyzed. The improved adaptive PID feedback control has better control performance. The feasibility and reliability of the adaptive PID feedback controller for collaborative robots are verified.

In feedback cancellation in hearing aids, an adaptive filter is used to model the feedback path. The output of the adaptive filter is subtracted from the microphone signal to cancel the acoustic and mechanical feedback picked up by the microphone, thus allowing more gain in the hearing aid. In general, the feedback cancellation filter adapts on the hearing-aid input signal, and signal cancellation and coloration artifacts can occur for a narrowband input. The goal in feedback cancellation is to design an algorithm that is computationally efficient, provides 10 dB or more of additional amplifier headroom, and is free from processing artifacts.This chapter begins with a steady-state analysis of the effects of acoustic feedback on the hearing-aid response. This analysis illustrates the factors that can affect the accuracy of the adaptive filter and the filter convergence for systems adapting with or without a probe signal. The characteristics of the feedback path being modeled are then described. An additional concern in designing signal processing for a hearing aid are the processing constraints imposed by a low-power portable device, and these concerns are discussed next. Two forms of constrained adaptation are then derived, and simulation results are used to give a comparison of the constrained adaptation with the conventional unconstrained approach for a system adapting without a probe signal. Further improvements in feedback cancellation performance, particularly a reduction in audible processing artifacts, can be achieved by using the filtered-X algorithm, and this approach is described next.The performance of feedback cancellation is ultimately limited by the characteristics of the room in which the listener is located since the room reflections form part of the feedback path. The chapter concludes with a study of the feedback path in a room along with the feedback cancellation performance for an 8-, 16-, or 32-tap adaptive FIR filter in series with a five-pole nonadaptive filter, and the performance limitations imposed by the room reverberation are discussed.KeywordsImpulse ResponseFeedback PathAdaptive FeedbackAcoustic FeedbackRoom ReverberationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this paper, we propose an adaptive fuzzy output feedback control method for trajectory tracking control problem for robotic systems. Using Lyapunov method, we first develop a stable adaptive fuzzy state feedback control algorithm by assuming that the systems output and its derivatives are available for feedback control design. The algorithm combines fuzzy systems with robust adaptive controller. The fuzzy system approximates the certainty equivalent (CE)-based optimal controller while robustifying adaptive control term is used to cope with uncertainties that appeared from the effect of external disturbance, fuzzy approximation errors and other modeling errors. Then, an output feedback form of the position-velocity (state feedback) controller is proposed where unknown velocity signal is replaced by the output of model-free linear estimator. We show via asymptotic analysis that the tracking performance of the output feedback design can recover the performance achieved under the state feedback control design. Finally, the proposed method is implemented and evaluated on a 2-DOF robotic system to demonstrate the theoretical development for the real-time applications.

In this paper, we propose a novel solution for continuous acoustic feedback neutralization in the digital hearing aids. The proposed method is based on two adaptive filters working in tandem. The error signal of the first adaptive filter is used as a desired response for the second adaptive filter being excited by a low-level (constant) probe signal. Initially at the start up, the first adaptive filter gives a fast convergence, however, its input and desired response are correlated with each other and it may converge to a biased solution at the steady-state. On the other hand, the second adaptive filter, though converging slowly being excited by a low-level probe signal, would give a good steady-state estimate of the acoustic feedback path. An appropriate delay is inserted at the output of the hearing aid, and based-on this delay-based processing, a novel strategy is proposed to exchange the weights between the two adaptive filters. The proposed strategy is such that the misconvergence of the first adaptive filter is avoided, as well as the initial convergence of the second adaptive filter is improved. Extensive computer simulations are carried out which demonstrate the effectiveness of the proposed method.

Magnetic levitation system is a typical nonlinear and instable system. Based on the complexity and inaccuracy of modelling, in this paper identified magnetic levitation system using the speciality that neural network(NN) can approach any nonlinear function. A Radial Basis Function neural network (RBFNN) controller is designed based on the neural network adaptive control principle. This paper proposes a control method which combine neural network adaptive control method and state feedback control method based on RBFNN. A simulation of the system is proposed, and the result shows that RBFNN could approach magnetic levitation system very well, neural network adaptive state feedback controller has a good effect on this nonlinear system; this control system has a preferable stability and control property.

Adaptive feedback cancellation in hearing aids

Publication in the conference proceedings of EUSIPCO, Antalya, Turkey, 2005

While several proactive acoustic feedback (Larsen-effect) cancellation schemes have been presented for speech applications with short acoustic feedback paths as encountered in hearing aids, these schemes fail with the long impulse responses inherent to, for instance, public address systems. We derive a new prediction error method (PEM)-based scheme (referred to as PEM-AFROW) which identifies both the acoustic feedback path and the nonstationary speech source model. A cascade of a short- and a long-term predictor removes the coloring and periodicity in voiced speech segments, which account for the unwanted correlation between the loudspeaker signal and the speech source signal. The predictors calculate row operations which are applied to prewhiten the speech source signal, resulting in a least squares system that is solved recursively by means of normalized least mean square or recursive least squares algorithms. Simulations show that this approach is indeed superior to earlier approaches whenever long acoustic channels are dealt with

Acoustic feedback in hearing aids occurs due to the coupling between the hearing aid loudspeaker and microphones. In order to reduce acoustic feedback, adaptive filters are often used to estimate the feedback path. To increase the convergence speed and decrease the computational complexity of the adaptive algorithms, it has been proposed to split the acoustic feedback path into a time-invariant fixed part and a time-varying variable part. A key question of this approach is how to determine the fixed part. In this paper, two approaches are investigated: (1) a digital filter design approach that makes use of the signals of at least two hearing aid microphones and (2) a defined physical location approach using an electro-acoustic model and the signals of one hearing aid microphone and an additional ear canal microphone. An experimental comparison using measured acoustic feedback paths showed that both approaches enable one to reduce the number of variable part coefficients. It is shown that individualization of the fixed part increases the performance. Furthermore, the two approaches offer solutions for different requirements on the effort to a specific hearing aid design on the one hand and the effort during the hearing aid fitting on the other hand.

Robust Adaptive Control

In this paper, a non-linear adaptive control method based on SO(3) for the quadrotor attitude tracking is proposed. Distinct from other control methods on Euclidean space, the controller proposed is developed on SO(3), which can avoid singularities and ambiguities. Besides, the geodesic on SO(3) is used to provide the shortest curve between the current attitude configuration and the desire attitude configuration. Then, a feedback controller based on geodesic is derived. To solve the problem that the inertial tensor of the quadrotor is unknown, an adaptive term is designed with the Lyapunov theory to estimate the inertial tensor. Thus, the almost global asymptotic stability of the quadrotor attitude tracking is achieved without the prior knowledge of the quadrotor inertia tensor. The numerical simulations are utilised to show better control performance of the adaptive geodesic feedback control method.

In this paper, distributed state and output feedback adaptive consensus control protocols are developed for addressing networked multiagent systems subject to exogenous stochastic disturbances as well as sensor and actuator attacks. Specifically, for a class of linear leader–follower multiagent systems with an undirected communication graph topology, state and output feedback adaptive control design protocols are developed for each follower agent to address malicious attacks on the actuator signals of the follower agents as well as sensor attacks on the state and output neighborhood synchronization error measurements. The proposed adaptive controllers involve an indirect adaptive architecture that estimates and compensates for the malicious attacks while guaranteeing uniform ultimate boundedness of the state tracking error for each agent in a mean-square sense.

Spatial filtering techniques typically rely on estimates of the target relative transfer function (RTF). However, the target speech signal is typically corrupted by late reverberation and ambient noise, which complicates RTF estimation. Existing methods subtract the noise covariance matrix to obtain the target plus late reverberation covariance matrix, from where the RTF is estimated. However, the noise covariance matrix is typically unknown. More specifically, the noise power spectral density (PSD) is typically unknown, while the spatial coherence matrix can be assumed known as it might remain time-invariant for a longer time. Using the spatial coherence matrices we simplify the signal model such that the off-diagonal elements are not affected by the PSDs of the late reverberation and the ambient noise. Then we use these elements to estimate the target covariance matrix, from where the RTF can be obtained. Hence, the resulting estimate of the RTF is insensitive to the noise PSD. Experiments demonstrate the estimation performance of our proposed method.