Data-driven tuning of state feedback gains with stability constraint using experimental data

摘要

A mathematical model is required to design the state feedback gains. Since, the mathematical model generally has uncertainty, the designed state feedback gains might not achieve the desired property. In such a case, the tuning method of the state feedback gains using experimental data has been proposed in order to obtain the desired property. However, this method cannot achieve the desired output property enough. Furthermore, stability of the closed-loop system is also not enough considered. Therefore, this paper proposes a new off-line tuning method of the state feedback gains with the stability constraint based on the Nyquist criterion using experimental data.