车内声品质主动控制算法研究综述

A survey of research on active control algorithms for vehicle interior sound quality

随着经济水平的提高和汽车技术的发展,车内噪声控制要求在降低声压级的基础上,通过调整声音特性改善声品质.为满足此目标,对声品质控制目标、控制系统建模、算法改进、算法仿真分析以及试验验证等方面进行研究,其中声品质主动控制算法是控制系统的核心,也是研究重点之一.为进一步提高控制算法有效性、实时性和自适应性,重点综述主动噪声均衡(ANE)、滤波误差最小均方(FELMS)、神经网络3类声品质主动控制算法,详细阐述各类算法的实现原理、发展情况以及优劣势对比分析,同时对声品质主动控制算法存在的问题以及未来发展前景进行展望,为研究者提供理论基础.

With the improvement of the economic level and the development of automobile technology,the vehicle interior noise control is required to reduce the sound pressure level.In addition,it is necessary to improve the sound quality by adjusting the sound characteristics.In order to meet this goal,many studies have focused on sound quality control objectives,control system modeling,algorithm improvement,algorithm simulation analysis and experimental verification.Among them,the sound quality active control algorithm is the core of the control system,and it is also the key of research.To further improve the effectiveness,real-time and adaptability of control algorithms,three types sound quality active control algorithms of active noise equalizer(ANE),filtered error least mean square(FELMS),and neural networks were reviewed in detail.The implementation principle,development,advantages and disadvantages of each algorithm were described in detail.At the same time,the existing problems and future development prospects of sound quality active control algorithms were prospected,which could provide a theoretical basis for researchers.