Adaptive H∞ control for linear slider with friction compensation using σ-modification strategy

This paper concerns with a tracking control of a servo mechanism with friction. An adaptive friction compensation strategy based on notion of H^∞ optimality is proposed. It is assumed that the friction dynamics is described by the LuGre model. Neural-network (NN) is used to parameterize the nonlinear characteristic function of the LuGre model. Unknown friction parameters and NN weight are updated by estimating strategy in this proposed method. 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, σ-modification method is also applied to the unknown parameter estimation methods to cope with the practical applications. Experimental results are shown to illustrate the effectiveness of our proposed method.