Data-driven Control for State Feedback Servo System without Reference Model and Experimental Verification

In this paper, we propose a new approach to data-driven parameter tuning method of feedback gain and integral gain for servo system. Differently from other methods, the proposed method does not require reference model to be tracked by the output, which is considered as the key point in the servo system. To realize it, we utilize data-driven prediction, which predicts the response of the closed loop with some controller parameter before experiment. Then, by using such a data-driven prediction, we perform nonlinear optimization under constraint conditions on the predicted input and output. Finally, we show the usefulness of the proposed method with the experimental example.