FxLMS Algorithm for Active Vibration Control of Structure By Using Inertial Damper with Displacement Constraint

Engine is the main source of vibration that generates unwanted noise and vibration of vehicle chassis. Especially, in submarine applications, radiation of noise signatures can be detected at some distance away from the submarine using a sonar array. Thus quiet operation is crucial for submarine’s survivability. This study addresses reduction of the force transmissibility originating from engines and transmitted to hull through engine mounts. An inertial damper, as an actuator of hybrid mount system, is addressed to reduce even further the level of vibration. Narrow band FxLMS algorithms are broadly used to cancel the vibration of engine mount because of its excellent performance of canceling narrow band noise. However, in real active dampers, the maximum displacement of damper mass is kinematically restricted. When the control input signal from the FxLMS algorithm exceeds this limitation, the damper mass will collide with the mechanical stops and results in many problems. Originated from these, a modified narrow band FxLMS algorithm based on the equalizer technique with the maximum allowable displacement of active damper mass is proposed in this study. Some simulation results showed that the propose algorithm is effective to suppress vibration of engine mount while ensuring given displacement constraint.

Simplified fast transversal filter algorithms for multichannel active noise control

In recent years, more attention has been paid to the performance of algorithm in active noise control (ANC). Compared with filtered-x LMS (FxLMS) algorithm based on stochastic gradient descent, filtered-x RLS (FXRLS) algorithm has faster convergence speed and better tracking performance at the cost of high computational complexity. In order to reduce the computation, fast transversal filter (FTF) algorithm can be used in ANC system. In this paper, simplified multi-channel FXFTF algorithms are presented, and the convergence speed and noise reduction performance of different multichannel algorithms are simulated and compared, and the numerical stability of the algorithms are analyzed.

Indoor Acoustic Signals Enhanced Algorithm and Visualization Analysis

A pure acoustic signal can be easy to realize signal analysis and feature extraction. However, the surrounding noises will affect the content of acoustic signals as well as auditory fatigue to the audience. Therefore, it is vital to overcome the problem of noises that affect the acoustic signal. An indoor acoustic signal enhanced method based on image source (IS) method, filtered-x least mean square (FxLMS) algorithm, and the combination of Delaunay triangulation and fuzzy c-means (FCM) clustering algorithm is proposed. In the first stage of the proposed system, the IS method was used to simulate indoor impulse response. Next, the FxLMS algorithm was used to reduce the acoustic signals with noise. Lastly, the quiet areas are optimized and visualized by combining the Delaunay triangulation and FCM clustering algorithm. The experimental analysis results on the proposed system show that better noise reduction can be achieved than the most widely used least mean square algorithm. Visualization was validated with an intuitive understanding of the indoor sound field distribution and the quiet areas.

Nonlinear Neural System for Active Noise Controller to Reduce Narrowband Noise

Noise in a dynamic system is practically unavoidable. Today, such noise is commonly reduced using an active noise control (ANC) system with the filtered-x least mean square (FXLMS) algorithm. However, the performance of the ANC system with FXLMS algorithm is significantly impaired in nonlinear systems. Therefore, this paper develops an efficient nonlinear adaptive feedback neural controller (NAFNC) to eliminate narrowband noise for both linear and nonlinear ANC systems. The proposed controller is implemented to update its coefficients without prior offline training by neural network. Hence, the proposed method has rapid convergence rate as confirmed by simulation results. The proposed work also analyzes the stability and convergence of the proposed algorithm. Simulation results verify the effectiveness of the proposed method.