A neural network controller for flexible-link robots

The object of this paper is to achieve tracking control of a partially known flexible-link robot arm. We show how to stabilize the internal dynamics by selecting a physically meaningful modified performance output for tracking. The controller is composed of a singular-perturbation-based fast control and an outer-loop slow control. The slow subsystem is controlled by a neural network (NN) for feedback linearization, plus a PD outer-loop for tracking, and a robustifying term to assure the closed-loop stability. No off-line training or learning is needed for the NN. Tracking and stability are proven using Lyapunov techniques that yield a novel modified NN weight tuning algorithm.<
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