Control of uncertain systems by feedback linearization with neural networks augmentation. Part I. Controller design
Journal Title: INCAS BULLETIN - Year 2009, Vol 1, Issue 1
Abstract
The paper highlights the main steps of adaptive output feedback control for non-affine uncertain systems – both in parameters and dynamics – having a known relative degree. Given a reference model, the objective is to design a controller that forces the measured system output to track the reference model output with bounded errors. A single hidden layer neural network is used to counteract feedback linearization error. A dynamic observer of tracking error is added. The treatment of control saturation is also sketched. The mathematical model for the longitudinal dynamics of an experimental helicopter is used as framework
Authors and Affiliations
Ioan URSU, Adrian TOADER, George TECUCEANU
Keywords
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- EP ID EP134580
- DOI 10.13111/2066-8201.2009.1.1.16
- Views 88
- Downloads 0
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