Feedforward-Control-Oriented Identification: A Robust Iterative Learning Approach

Feedforward controller plays an important role in the achievement of high servo performance in the field of industrial electromechanical systems. Although existing instrumental-variables-based iterative feedforward tuning (IFT) methods show a promising prospect on improving estimation accuracy, the robust approaches have not developed. In this paper, a robust IFT approach is proposed through a combination of instrumental-variables-based system identification and learning control. It transforms the feedforward controller design to a system identification problem, and then to a typical H
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state feedback problem. A simulation example is provided to confirm its superiority.