Document Detail


An analytical comparison of a neural network and a model-based adaptive controller.
MedLine Citation:
PMID:  18267768     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
A neural network inverse dynamics controller with adjustable weights is compared with a computed-torque type adaptive controller. Lyapunov stability techniques, usually applied to adaptive systems, are used to derive a globally asymptotically stable adaptation law for a single-layer neural network controller that bears similarities to the well-known delta rule for neural networks. This alternative learning rule allows the learning rates of each connection weight to be individually adjusted to give faster convergence. The role of persistently exciting inputs in ensuring parameter convergence, often mentioned in the context of adaptive systems, is emphasized in relation to the convergence of neural network weights. A coupled, compound pendulum system is used to develop inverse dynamics controllers based on adaptive and neural network techniques. Adaptation performance is compared for a model-based adaptive controller and a simple neural network utilizing both delta-rule learning and the alternative adaptation law.
Authors:
R E Nordgren; P H Meckl
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council     Volume:  4     ISSN:  1045-9227     ISO Abbreviation:  IEEE Trans Neural Netw     Publication Date:  1993  
Date Detail:
Created Date:  2008-02-12     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101211035     Medline TA:  IEEE Trans Neural Netw     Country:  United States    
Other Details:
Languages:  eng     Pagination:  685-94     Citation Subset:  -    
Affiliation:
Sch. of Mech. Eng., Purdue Univ., West Lafayette, IN.
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