An adaptive friction compensation method based on notion of H ∞ optimality is proposed. It is assumed that the static and dynamic characteristics of friction are captured by the dynamic LuGre model. Neural-Network (NN) is used to parameterize the nonlinear characteristic function of the friction model. An adaptive NN based controller is given, and an approximation error in NN is regarded as exogenous disturbance to the system. Consequently, in the resulting control system, the L 2 gains from the disturbance to generalized outputs are made less than prescribed positive constants. For the practical applications, σ-modification method and dead-zone method are applied to the estimation strategies. To illustrate the effectiveness of our proposed method, experimental results are shown.

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