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dc.creatorBaldini, Patricia Noemí
dc.creatorBambill, Héctor Ricardo
dc.creatorFriedrich, Guillermo
dc.date.accessioned2018-03-27T20:26:08Z
dc.date.available2018-03-27T20:26:08Z
dc.date.issued2017-11
dc.identifier.citationBaldini, P. N.; Bambill, H. R. y Friedrich, G. (2017). Filtrado adaptativo de Lyapunov aplicado al control activo de procesos de ruido de banda ancha. En " XV AdAA", Bahía Blanca.es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12272/2732
dc.description.abstractEl objetivo del control activo de ruido es reducir el nivel de presión sonora en una dada región del espacio. En este trabajo se presentan los resultados de aplicar a ductos un método de control activo orientado a procesos de ruido de banda ancha basado en la teoría de Lyapunov. Mediante un algoritmo de filtrado adaptativo se estima la señal secundaria para lograr la cancelación. En estos sistemas la interacción de partes mecánicas rotativas de componentes tales como motores, sopladores, compresores, con el eje y con el flujo de aire genera ruido no lineal de banda ancha, el cual puede modelarse como caótico determinístico. A diferencia de métodos basados en LMS, el diseño resulta independiente de las propiedades estocásticas del ruido, generalmente desconocidas, y la estabilidad se garantiza en el sentido de Lyapunov. La efectividad del control propuesto, comparado con métodos tradicionales, se verifica por simulación.es_ES
dc.description.abstractThe objective of active noise control is to reduce the sound pressure level in a given region of space. In this paper we present the results of applying to ducts an active control method oriented to broadband noise processes based on the Lyapunov theory. Using an adaptive filtering algorithm, the secondary signal is estimated to achieve cancellation. In these systems the interaction of rotating mechanical parts of components such as motors, blowers, compressors, with the shaft and with the air flow generates wide-band non-linear noise, which can be modeled as chaotic deterministic. Unlike LMS-based methods, the design is independent of the stochastic properties of noise, generally unknown, and stability is guaranteed in the Lyapunov sense. The effectiveness of the proposed control, compared with traditional methods, is verified by simulation.es_ES
dc.formatapplication/pdf
dc.language.isospaes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectCARes_ES
dc.subjectControl adaptativoes_ES
dc.subjectruido de banda anchaes_ES
dc.subjectFiltrado de Lyapunnoves_ES
dc.titleFiltrado adaptativo de Lyapunov aplicado al control activo de procesos de ruido de banda anchaes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.description.affiliationFil: Baldini, Patricia Noemí. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; Argentina.es_ES
dc.description.affiliationFil: Bambill, Héctor Ricardo. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; Argentina.es_ES
dc.description.affiliationFil: Friedrich, Guillermo. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; Argentina.es_ES
dc.description.peerreviewedPeer Reviewedes_ES
dc.relation.projectidPID UTN 3972TCes_ES
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_ES
dc.type.snrddocunento de conferenciaes_ES
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dc.relation.referencesSeng. Phooi K; Man, Zhihong; Wu Hong R. (2002). Lyapunov Theory Based Radial Basis Function Networks for Adaptative Filtering. IEEE Transaction on Circuits and Systems-I: Fundamental Theory ans Applications (49) 8, 1215-1220.es_ES
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dc.relation.referencesSeng, Phooi K.; Man, Zhihong; Wu, Hong R. (2000) Nonlinear Active Noise Control Using Lyapunov Theory and RBF Network. Proceeding of X IEEE Neural Networks for Signal Processing X, Sydney, Australia. 916-925.es_ES
dc.relation.referencesZhao, Haiquan; Zhang, Jiashu. (2008) Filtered-s Lyapunov Algorithm for Active Noise Control of Nonlinear Noise Procecses. Proceeding of International Conference on Signal Processing (ICSP), Beijing, China. 311-314.es_ES
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dc.relation.referencesLuo, Lei; Sun, Jinwei; Bai, Zonglong (2017). An Adaptative Recursive Feedback Active Noise Control for Chaotic Noise. Proceeding of 3rd International Conference on Control, Automation and Robotic. Nagoya, Japan. 423-427.es_ES
dc.relation.referencesTan, Li; Jiang, Jean.(2001) Adaptive Volterra Filters for Active Control of Nonlinear Noise Processes. IEEE Transaction on Signal Processing (49) 8, 1667-1676.es_ES
dc.relation.referencesBehera, Suman Bala; Das, Debi Prasad; Rout, Nirmal Kumar.(2014) Nonlineal feedback Active Noise Control for broadband chaotic noise. Applied Soft Computing. Vol 15. 80-87.es_ES
dc.relation.referencesBehera, Suman Bala; Das, Debi Prasad; Subudhi, Bidyadhar.(2014) Funtional Link Artifitial Neural Network Applied to Active Noise Control of a Mixture of tonal and Chaotic Noise. Applied Soft Computing (23), 51-60.es_ES
dc.relation.referencesSun, Jia; Sun, Chang-Yin; Yu, Yao (2016). Active Noise Control Using STF for Time-vary Delay Estimation in Secondary Path Based on AFxLMS. Proceeding of World Congreses on Intelligent Control and Automation (WCICA), Guilin, China, 3346-3352.es_ES
dc.relation.referencesJones, R. W.; Olsen, B. L.; Mace, B.R. (2007). Comparison of Convergence Characteristics of Adadtative IIR and FIR filters for ANC in a Duct. Applaid Acustics (68) 729-738.es_ES
dc.relation.referencesVidyasagar, Mathukumalli (1993). Nonlinear Systems Analysis. Cap. 5. 2nd Ed. Prentice Hall, New Jersey, USA.es_ES
dc.rights.useAtribución-NoComercial 4.0 Internacional (CC BY-NC)es_ES
dc.rights.useAtribución-NoComercial 4.0 Internacional*


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