Data-driven Update of State Feedback Gains

Abstract

In this paper, we propose a data-driven method to update state feedback gains for linear time invariant systems. Particularly, by employing the concept of fictitious reference iterative tuning, we give an effective update method for a controller to be improved with more desired property. We also show that the proposed method can be performed by the least squares method. We illustrate an experimental example on control of the two-wheeled inverted pendulum to show the validity of the proposed method.