Abstract
In this paper we present a low dimensional adaptive neural network controller for robot manipulators with fast convergence of tracking error. Its novelty lies in the low dimensional network, smooth control input and very fast convergence that reduce the computational cost that face the problem of over parameterization. The control strategy is based on a second order sliding surface which drives the controller and the online computation of weights with a chattering-free control output. Furthermore, a time base generator induces well-posed finite time convergence of tracking errors for any initial condition. We validate our approach including experimental results obtained in a planar 2 dgf manipulator.
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