Active disturbance cancellation in nonlinear dynamical systems using neural networks

<p>A proposal for the use of a time delay CMAC neural network for disturbance cancellation in nonlinear dynamical systems is presented. Appropriate modifications to the CMAC training algorithm are derived which allow convergent adaptation for a variety of secondary signal paths. Analytical bounds on the maximum learning gain are presented which guarantee convergence of the algorithm and provide insight into the necessary reduction in learning gain as a function of the system parameters. Effectiveness of the algorithm is evaluated through mathematical analysis, simulation studies, and experimental application of the technique on an acoustic duct laboratory model.</p>

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Dissertation

Year

2003

Author dc:creator

• Canfield, John C

Contributors dc:contributor

• L Gordon Kraft

• Engineering
• Electronics and Electrical
• Physics
• Acoustics

Repository record dc:identifier

https://scholars.unh.edu/dissertation/188

OAI identifier oai:identifier

oai:scholars.unh.edu:dissertation-1187