Title:
Neural-Network Augmentation of Existing Linear Controllers
Neural-Network Augmentation of Existing Linear Controllers
Author(s)
Sharma, Manu
Calise, Anthony J.
Calise, Anthony J.
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Abstract
A method to augment existing linear controllers with a multilayer neural network is presented. The neural
network is adapted online to ensure desired closed-loop response in the face of parametric plant uncertainty; no
off-line training is required. The benefit of this scheme is that the neural-network output is simply added to the
nominal control signal, thereby preserving the existing control architecture. Furthermore, the nominal control
signal is only modified if the desired closed-loop response is not met. This method applies to a large class of modern
and classical linear controllers. Stability guarantees are provided via Lyapunov-like analysis, and the efficacy of
this scheme is illustrated through two numerical examples.
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Date Issued
2005-01
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Text
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Article