Flexible Link Control with Three Different Structures Using Feedback-Error-Learning

Abstract

This paper discusses three structures for neural control of a flexible link using the Feedback-Error-Learning technique. This technique aims to acquire the inverse dynamic 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. Three different neural approaches are used in this paper to overcome this difficulty. The first and second structures use a virtual redefined output as one of the inputs for the neural network and feedback controllers, while the third 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.