![]() ĭue to the presence of a secondary (electro-acoustic) path between the cancelling loudspeaker and the error microphone, the classical least mean square (LMS) algorithm cannot be directly implemented. An excellent survey on recent research trends and interesting applications for ANC systems can be found in. Many successful applications of ANC technology have been reported in (among others) however, the most popular is ANC headsets. Active noise and vibration control is a well-researched area that has spread from academic research in a tight laboratory settings to successful commercial products, thanks to great advancements in the modern semiconductor technology. These two signals, being anti-phase to each other, tend to cancel each other out. Therefore, any ANC system would essentially generate an anti-phase (acoustic) noise-cancelling signal, which is combined acoustically with the primary (disturbance or noise) signal generated from some noise source (for example, exhaust fan, vacuum cleaner, power transformer, etc.). Lueg in his US patent, is very simple: two out of phase acoustic waves would result in a destructive interference and hence cancel each other out. The basic idea of active noise control (ANC), first conceptualized by P. The simulation results demonstrate that the proposed CCSS-MFxRRLS algorithm is very effective in many practical scenarios involving ANC of impulsive sources. Extensive simulations have been designed to mimic many scenarios for practical applications of ANC for impulsive sources. As the ANC system converges at the steady-state, the CCSS is automatically tuned to a small value which improves the steady-state performance of the proposed CCSS-MFxRRLS algorithm. When the ANC is started, the CCSS strategy (automatically) selects a large-valued step size to achieve a fast initial convergence. In order to address this issue of a trade-off situation, the idea of a convex combined step size (CCSS) is introduced into the adaptive procedure to develop the CCSS-MFxRRLS algorithm. As expected, a fixed value step size results in a trade-off situation for convergence speed and steady-state misalignment. This results in the fixed step-size modified filtered-x (MFx) robust RLS (FSS-MFxRRLS) algorithm. Furthermore, it is proposed to introduce a step size in the update equation of the adaptive algorithm. In order to improve upon the numerical stability issue of RLS-based adaptation, it is suggested to employ smoothing while updating the inverse correlation matrix. The derivation of the algorithm is quite straightforward however, a few modifications have been incorporated to address the application at hand. The proposed RLS-based adaptive algorithm employs an objective function designed to achieve robustness against the impulse type sources. The objective of this paper is to propose a new recursive least squares (RLS) algorithm (and its variant) for being implemented in the framework of ANC systems. It is well-known that performance of the classical algorithms for active noise control (ANC) systems severely degrades when implemented for controlling the impulsive sources.
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