Data-driven Feedforward Input Design for Optimal Servosystem with Input Constraint

Abstract

The goal of this paper is to design a feedforward input which achieves a fast convergence to the reference under input constraint. In particular, this paper focuses on data-driven design which does not require a model of the system. We employ the data-driven prediction technique which has been developed for two-degrees-of-freedom systems. With this technique, the problem reduces to a quadratic programming whose optimization variable is impulse response of a feedforward controller. We also propose to employ regularization when the observation is contaminated to avoid overfitting. The effectiveness of the proposed method is demonstrated through a numerical example and a practical experiment.