Reduced-Order Inverse Model Optimization

The performance of traditional model-based control relies upon accurate modeling. In motion control of flexible systems, it is common to use the reduced-order model for ease of trajectory planning and pole placement, but its performance is limited by modeling inaccuracies due to the existence of friction and multiple flexible modes. To improve the tracking performance, a data-based method is developed for iterative tuning of the parameters in the reduced-order inverse model within a 3-DOF composite control structure. The proposed method solely makes use of the input-output data obtained during closed-loop experiments to optimize the inverse system model, and accurate system modeling is not required. Unbiasedness of the cost function gradient estimation is proved under reasonable assumptions of stochastic properties of the perturbations. Simulations and experiments are conducted to further illustrate the proposed method and show its practical appeal in industrial applications.