Iterative learning neural network control for nonlinear system trajectory tracking

Abstract This paper presents a neural network control scheme for tracking a non-periodic trajectory with a finite time interval from the view point of the iterative learning control. A system with time-varying but iteration-invariant uncertainties can be controlled by this scheme. The controller consists of a series of local networks. Every point along the desired trajectory has its own one for approximating the nonlinearity in a neighborhood only. An iterative training law described by a positive definite discrete kernel is presented. The control scheme is applied to robot movement imitation by visual servo. Stability of the controller is ensured.

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