Quantum-behaved particle swarm optimization-based active noise control system with timing varying path

Abstract

Active noise control systems can effectively suppress the impact of low-frequency noise and they have been applied in many fields. Recently, the evolutionary computation algorithm-based active noise control system has attracted considerable attention. To improve the noise reduction performance of the evolutionary computation algorithm-based active noise control system and solve the problem that the system cannot converge again when the path abruptly changes in steady state, we propose the path abruptly change-quantum-behaved particle swarm optimization algorithm. We apply quantum-behaved particle swarm optimization, a global optimization algorithm, to the active noise control system to improve noise reduction performance. In addition, the scheme of detecting the abrupt path change in steady state and performing re-convergence processing is designed to effectively address the problem that the system cannot regain convergence after a path change in steady state. The simulation study demonstrates that the proposed algorithm can efficiently improve noise reduction performance, accurately detect the path change, and re-converge to new global optimization.Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https:// creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).versions 7,8 have been used in many ANC systems, but FxLMS has some limitations. First, the estimated model of the secondary pathŜ z ð Þ is required; the model estimation error will reduce noise reduction performance and even cause instability. [9][10][11][12] In addition, FxLMS is a gradient-based optimization method, which can easily fall into local minima. 13 The adaptability of the most commonly used off-line estimation S z ð Þ method is poor. One solution is the online estimation of the secondary path. [14][15][16][17] However, most of the on-line estimation methods require additional noise, which increases the residual noise level and computational complexity. 18 Another method is evolutionary computation algorithm-based ANC system, which does not require secondary path estimation and is able to avoid the problem that the traditional gradient-based methods easily fall into local minima. Next, we introduce the evolutionary computation algorithm-based ANC systems in detail.Genetic algorithm (GA) has been applied to ANC systems. Yim et al. 13 proposed an ANC system, which uses the IIR filter as the controller and the GA to adjust the weights of the adaptive IIR filter. Russo and Sicuranza 19,20 used the GA to adjust the weights of a nonlinear Volterra filter in an ANC system and demonstrates better noise reduction performance than an FxLMS-based ANC system. Chang and Chen 21 proposed an adaptive GA-based linear ANC system using FIR filter and nonlinear ANC system using Volt...

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