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In recent years, active noise control (ANC) systems have been widely used in advanced electronic appliances. Nowadays, several authors use gradient-optimization algorithms since they can be easily implemented in these devices. However, these algorithms need to estimate the secondary path in advance. As consequence, this factor can limit its use in real-ANC applications since the secondary path can undergo significant variations over time. To solve this problem, we propose an ANC system with switching filter selection based on particle swarm optimization (PSO) algorithms. Specifically, we use two sets of populations of particles with different acceleration coefficients and inertia weights to create an advanced structure in which the first PSO algorithm guarantees a high convergence speed while the use of the second PSO algorithm allows to achieve a high-level noise reduction. The results demonstrate that the proposed algorithm exhibits better convergence properties compared with previously reported solutions.

A Novel PSO-Based Adaptive Filter Structure with Switching Selection Criteria for Active Noise Control