This study describes an iterative learning approach to the active control of machinery noise with high autocorrelation properties. In contrast to typical active noise control solutions, which work by adapting the transfer function of the controller, in the iterative learning control one adapts the control signal itself. Special care was taken to develop a generic solution that can handle different sorts of secondary path models including very long and non-minimum phase finite impulse response filters. To achieve that, the authors used spectral factorisation and exploit the fact that, for non-minimum phase systems, a stable inverse can be constructed if the causality constraint is relaxed and later restored by taking advantage of the periodicity of the attenuated signal. The resulting controller can be efficiently implemented on a sample-to-sample calculation basis. The behaviour and the performance of the proposed scheme are studied using computer simulations and real-world experiments on noises from an electric transformer and functional magnetic resonance imaging device. The proposed solution was also compared to normalised feedforward filtered-X least mean squares algorithm and performed much better in terms of attenuation, convergence, and robustness.

中文翻译：

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高度自相关信号的主动噪声控制的迭代学习方法及其在机械噪声中的应用

这项研究描述了一种迭代学习方法，以主动控制具有高自相关特性的机械噪声。与通过调整控制器的传递函数来工作的典型主动噪声控制解决方案相反，在迭代学习控制中，一种是自适应控制信号本身。我们特别注意开发一种通用解决方案，该解决方案可以处理不同类型的次级路径模型，包括非常长且非最小的相位有限冲激响应滤波器。为此，作者使用频谱分解并利用以下事实：对于非最小相位系统，如果放宽了因果关系约束并随后利用衰减信号的周期性来恢复，则可以构造一个稳定的逆。可以在样本间计算的基础上有效地实现所得的控制器。使用计算机模拟和实际实验研究了所提出方案的行为和性能，该实验来自变压器和功能性磁共振成像设备的噪声。所提出的解决方案还与归一化前馈滤波的X最小均方算法进行了比较，并且在衰减，收敛性和鲁棒性方面表现更好。