Nonlinear active noise control with NARX models

The extension of active noise control (ANC) techniques to deal with nonlinear effects such as distortion and saturation requires the introduction of suitable nonlinear model classes and adaptive algorithms. Large sized models are typically used, resulting in an increased computational load, delayed convergence (and sometimes even algorithm instability), and other unwanted dynamical effects due to overparametrization. This paper discusses the usage of polynomial Nonlinear AutoRegressive models with eXogenous variables (NARX) models and model selection techniques to reduce the model size and increase its robustness, for more efficient and reliable ANC. An offline procedure is devised to identify the controller model structure, and the controller parameters are successively updated with an adaptive algorithm based on the error gradient and on the residual noise. Simulation experiments show the effectiveness of the proposed approach. A brief analysis of the involved computational complexity is also provided.