Active control of repetitive impulsive noise in a non-minimum phase system using an optimal iterative learning control algorithm

In this paper, active control of repetitive impulsive noise is studied. An optimal iterative learning control (ILC) algorithm is developed for an active noise control (ANC) system with a non-minimum phase secondary path. A non-causal transversal finite impulse response (FIR) filter is used as the ILC learning filter, and the impulse response coefficients of the FIR filter are designed according to the asymptotically stable and monotonically convergent criterion in time domain. Computer simulations have been carried out to suggest that the proposed algorithm is effective for attenuating repetitive impulsive noise, and then the proposed algorithm has been implemented in an experimental ANC system. Experimental results show that the proposed scheme has good performance for repetitive impulsive noise attenuation in a non-minimum phase ANC system.