PSO based adaptive narrowband ANC algorithm without the use of synchronization signal and secondary path estimate. (1st January 2019)

• Record Type: Journal Article
• Authors: Rout, Nirmal Kumar; Das, Debi Prasad; Panda, Ganapati
• Is Part Of: Mechanical systems and signal processing. Volume 114(2019)
• Page Start: 378
• Page End: 398
• Publication Date: 2019-01-01
• Subjects: Active noise control -- Narrowband ANC -- Waveform synthesis -- Particle swarm optimization (PSO) -- Conditional freezing particle swarm optimization (CFPSO); Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2018.05.018
• Languages: English
• ISSNs: 0888-3270

Abstract

Graphical abstract: Highlights: A new PSO based narrowband active noise control algorithm is proposed. It does not require a synchronization signal and a secondary path estimate. The controller adapts to change in frequencies, amplitude, phase of discrete tones in the noise, the primary and secondary paths. Algorithm is derivative free algorithm and immune to fall in local minima. A novel conditional freezing PSO is proposed for achieving early convergence. Abstract: Narrowband active noise control (ANC) conventionally uses the waveform synthesis method in which a synchronization signal is essential. In addition to this, to update the parameters of the controller, prior estimation of the secondary path is required. Any mismatch in the frequency of the synchronization signal and the secondary path estimate compared to actual leads to a degraded performance in noise control. In this paper, a new type of narrowband ANC algorithm is proposed which requires neither the synchronization signal nor the secondary path estimate for controller tuning. Therefore, the algorithm is free from the effect of the change in the source frequency and the secondary path transfer function. The proposed adaptive algorithm is based on a non-gradient type optimization algorithm named as particle swarm optimization (PSO) algorithm. Through exhaustive computer based simulation, it has been demonstrated that the algorithm is not only capable to control a monotone noise but also efficiently control a multi-tone noise. The algorithm, due to its adaptive nature, is also capable of tracking the change in amplitude and frequency of the noise as well as, the change in the characteristics of primary and secondary paths.