Particle swarm optimization based nonlinear active noise control under saturation nonlinearity

Thermal failure analysis and control algorithm improvement for the active controlled mount with consideration of thermal-magnetic coupling

This paper proposes an improved narrowband filtered-x least mean square(FxLMS) algorithm to overcome the thermal failure problems of the active controlled mount(ACM). Firstly, the temperature-rising model and the thermal demagnetization model of the ACM are respectively developed. Combining these two models with the powertrain mounting system model, an analytical method is established for the thermal-magnetic coupling analysis of the ACM. Then a numerical simulation is carried out to obtain the permanent magnet(PM) temperature and the coil current. The ACM failure problem is discussed according to the working point trajectory. Finally, an improved algorithm is proposed. This algorithm is able to overcome the thermal failure problems by sacrificing part of the vibration isolation performance. The effectiveness of this algorithm is validated by numerical simulations and a comparison with conventional algorithms.

A Novel PSO-Based Adaptive Filter Structure with Switching Selection Criteria for Active Noise Control

In recent years, active noise control (ANC) systems have been widely used in advanced electronic appliances. Nowadays, several authors use gradient-optimization algorithms since they can be easily implemented in these devices. However, these algorithms need to estimate the secondary path in advance. As consequence, this factor can limit its use in real-ANC applications since the secondary path can undergo significant variations over time. To solve this problem, we propose an ANC system with switching filter selection based on particle swarm optimization (PSO) algorithms. Specifically, we use two sets of populations of particles with different acceleration coefficients and inertia weights to create an advanced structure in which the first PSO algorithm guarantees a high convergence speed while the use of the second PSO algorithm allows to achieve a high-level noise reduction. The results demonstrate that the proposed algorithm exhibits better convergence properties compared with previously reported solutions.

Theoretical transient analysis of a hearing aid feedback canceller with a saturation type nonlinearity in the direct path

Feedback cancellation in a hearing aid is essential for achieving high maximum stable gain to compensate for the losses in severe to profound hearing impaired people. The performance of adaptive feedback cancellers has been studied by assuming that the feedback path can be modeled as a linear system. However, limited dynamic range, low-cost loudspeakers, and nonlinear power amplifiers may distort the hearing aid output signal. In this way, linear-based predictions of the canceller performance may lead to significant deviations from its actual behavior. This work presents a theoretical performance analysis of a Least Mean Square based shadow filter that is applied to set up the coefficients of a feedback canceller, which is subject to a static saturation type nonlinearity at the output of the direct path. Deterministic recursive equations are derived to predict the mean square feedback error and the mean coefficient vector evolution between updates of the feedback canceller. These models are defined as functions of the canceller parameters and input signal statistics. Comparisons with Monte Carlo simulations show the provided models are highly accurate under the considered assumptions. The developed models allow inferences about the potential impact of an overdriven loudspeaker over the transient performance of the direct method feedback canceller, serving as insightful tools for understanding the involved mechanisms.