Yuan J
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong.
J Acoust Soc Am. 2008 Oct;124(4):2078-84. doi: 10.1121/1.2968700.
An important step for active noise control (ANC) systems to be practical is to develop model independent ANC (MIANC) systems that tolerate parameter variations in sound fields. Reliabilities and stabilities of many MIANC systems depend on results of online system identifications. Parameter errors due to system identifications may threaten closed-loop stabilities of MIANC systems. A self-learning active noise control (SLANC) system is proposed in this study to stabilize and optimize an ANC system in case identified parameters are unreliable. The proposed system uses an objective function to check closed-loop stability. If partial or full value of the objective function exceeds a conservatively preset threshold, a stability threat is detected and the SLANC system will stabilize and optimize the controller without using parameters of sound fields. If the reference signal is available, the SLANC system can be combined with a feedforward controller to generate both destructive interference and active damping in sound fields. The self-learning method is simple and stable for many feedback ANC systems to deal with a worst case discussed in this study.
有源噪声控制（ANC）系统要想实用化，一个重要步骤是开发能容忍声场参数变化的与模型无关的有源噪声控制系统（MIANC）。许多MIANC系统的可靠性和稳定性取决于在线系统识别的结果。系统识别产生的参数误差可能会威胁到MIANC系统的闭环稳定性。本研究提出了一种自学习有源噪声控制（SLANC）系统，以便在识别出的参数不可靠的情况下稳定和优化ANC系统。所提出的系统使用一个目标函数来检查闭环稳定性。如果目标函数的部分或全部值超过保守预设的阈值，则检测到稳定性威胁，并且SLANC系统将在不使用声场参数的情况下稳定和优化控制器。如果参考信号可用，SLANC系统可以与前馈控制器相结合，以在声场中产生相消干涉和主动阻尼。对于许多反馈ANC系统而言，自学习方法简单且稳定，能够应对本研究中讨论的最坏情况。
