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Diagnostics of a Nonlinear Pendulum Using Computational Intelligence

[+] Author Affiliations
M. Samadani, C. A. Kitio Kwuimy, C. Nataraj

Villanova University, Villanova, PA

Paper No. DSCC2013-4054, pp. V002T24A006; 8 pages
  • ASME 2013 Dynamic Systems and Control Conference
  • Volume 2: Control, Monitoring, and Energy Harvesting of Vibratory Systems; Cooperative and Networked Control; Delay Systems; Dynamical Modeling and Diagnostics in Biomedical Systems; Estimation and Id of Energy Systems; Fault Detection; Flow and Thermal Systems; Haptics and Hand Motion; Human Assistive Systems and Wearable Robots; Instrumentation and Characterization in Bio-Systems; Intelligent Transportation Systems; Linear Systems and Robust Control; Marine Vehicles; Nonholonomic Systems
  • Palo Alto, California, USA, October 21–23, 2013
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5613-0
  • Copyright © 2013 by ASME


A novel method has been presented in this paper for the diagnostics of nonlinear systems using the features of the nonlinear response and capabilities of computational intelligence. Four features of the phase plane portrait have been extracted and used to characterize the nonlinear response of a nonlinear pendulum. An artificial neural network has been created and trained using the numerical data for the estimation of parameters of a defective nonlinear pendulum setup. The results show that, with appropriately selected features of the nonlinear response, the parameters of the nonlinear system can be estimated with an acceptable accuracy.

Copyright © 2013 by ASME
Topics: Pendulums



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