This paper presents two new algorithms for rejection of known-frequency sinusoidal disturbances that act on a completely unknown, multi-input multi-output, asymptotically stable, linear time-invariant system. The first algorithm is centralized adaptive harmonic control (AHC), which does not impose any structural constraints on the input–output channels of the controller. The second algorithm is decentralized AHC, which imposes the constraint that each local control is computed using only local sensor measurements. These new control methods are data-driven, adaptive algorithms that do not rely on any information regarding the system. It is also worth noting that all control computations are performed using discrete Fourier transform data. The stability and closed-loop performance with each algorithm are analyzed. The main analytic results show that each algorithm asymptotically rejects sinusoidal disturbances. Illustrative numerical simulations and active noise control experiments demonstrate the effectiveness of centralized and decentralized AHC.

本文提出了两种用于抑制已知频率正弦扰动的新算法，这些算法作用于一个完全未知的，多输入多输出，渐近稳定的线性时不变系统。第一种算法是集中式自适应谐波控制（AHC），它不对控制器的输入-输出通道施加任何结构性约束。第二种算法是分散式AHC，它施加了以下约束：每个局部控制仅使用局部传感器测量值进行计算。这些新的控制方法是数据驱动的自适应算法，不依赖于有关系统的任何信息。还值得注意的是，所有控制计算都是使用离散傅立叶变换数据执行的。分析了每种算法的稳定性和闭环性能。主要分析结果表明，每种算法都渐近地拒绝了正弦干扰。说明性的数值模拟和主动噪声控制实验证明了集中式和分散式AHC的有效性。