Feedback-Error-Learning for Controlling a Flexible Link

Areolino de Almeida NetoWilson Rios NetoLuiz Carlos S. GóesCairo L. Nascimento Jr.

This paper discusses two approaches for neural control of a flexible link using the Feedback-Error-Learning technique. This technique aims to acquire the inverse dynamics model of the plant and uses a neural network acting as an adaptive controller to improve the performance of a conventional non-adaptive feedback controller. The non-collocated control of a flexible link is characterized as a non-minimum phase system, which is difficult to be controlled by most control techniques. Two different neural approaches are used in this paper to overcome this difficulty. The first approach uses a virtual redefined output as one of the inputs for the neural network and feedback controllers, while the other employs a delayed reference input signal in the feedback path and a tapped-delay line to process the reference input before presenting it to the neural network.

Caso o link acima esteja inválido, faça uma busca pelo texto completo na Web: Buscar na Web

Biblioteca Digital Brasileira de Computação - Contato:
     Mantida por: