Abstract
This paper considers a tracking control of a servo mechanism with friction. It is assumed that the static and dynamic characteristics of friction are captured by the dynamic LuGre model. Based on notion of H∞ optimality, an adaptive friction compensation strategy is proposed. Neural-Network (NN) is used to parameterize the nonlinear characteristic function of the friction model. In this proposed method, unknown friction parameters and NN weight vector are updated by estimating strategy. Moreover, an approximation error in NN is regarded as an exogenous disturbance to the system, the L2 gains from the disturbance to generalized outputs are made less than prescribed positive constants. To cope with the practical applications, dead-zone modification method is also applied to the unknown parameter estimating strategies. Experimental results are shown to illustrate the effectiveness of our proposed method.
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