Inverse model-based iterative learning control for active control of repetitive impulsive noise with a non-minimum phase secondary path

In this paper, active control of impulsive noise is studied. A novel approximate inverse model combined with optimal criterion-based iterative learning control (ILC) algorithm is used for an active noise control (ANC) system with a non-minimum phase secondary path. Computer simulations have been carried out to validate the effectiveness of the proposed algorithm. The plant model used in the computer simulations is obtained from a practical ANC system in our laboratory. Simulation results show that the proposed scheme can significantly reduce the impulsive noise and the convergence rate is fast for a non-minimum phase plant.