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Direct adaptive control using feedforward neural networks

Author(s): Cajueiro Daniel Oliveira | Hemerly Elder Moreira

Journal: Sba: Controle & Automação Sociedade Brasileira de Automatica
ISSN 0103-1759

Volume: 14;
Issue: 4;
Start page: 348;
Date: 2003;
Original page

Keywords: Adaptive control | backpropagation | convergence | extended Kalman filter | neural networks | stability

This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the convergence of the identification error is investigated by Lyapunov's second method. The performance of the proposed scheme is evaluated via simulations and a real time application.
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