Areolino de Almeida Neto,  Wilson Rios Neto,  Luiz Carlos S. Góes,  Cairo 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.

http://csdl.computer.org/comp/proceedings/sbrn/2000/0856/00/08560273abs.htm

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