2023 |
Hyuntae Kim / Hamin Chang / Hyungbo Shim Model Reference Gaussian Process Regression: Data-Driven Output Feedback Controller Proceedings Article In: 2023 American Control Conference (ACC), pp. 955-960, IEEE, San Diego, CA, USA, 2023, ISBN: 979-8-3503-2806-6. Abstract | Links | BibTeX | Tags: Data-driven control, Gaussian process, MR-GPR @inproceedings{nokey,Data-driven controls using Gaussian process regression have recently gained much attention. In such approaches, system identification by Gaussian process regression is mostly followed by model-based controller designs. However, the outcomes of Gaussian process regression are often too complicated to apply conventional control designs, which makes the numerical design such as model predictive control employed in many cases. To overcome the restriction, our idea is to perform Gaussian process regression to the inverse of the plant with the same input/output data for the conventional regression. With the inverse, one can design a model reference controller without resorting to numerical control methods. This paper considers single-input single-output (SISO) discrete-time nonlinear systems of minimum phase with relative degree one. It is highlighted that the model reference Gaussian process regression controller is designed directly from pre-collected input/output data without system identification. |
Yongsoon Eun / Jaeho Lee / Hyungbo Shim Data-Driven Inverse of Linear Systems and Application to Disturbance Observer Proceedings Article In: 2023 American Control Conference (ACC), pp. 2806-2811, IEEE, San Diego, CA, USA, 2023, ISBN: 979-8-3503-2806-6. Abstract | Links | BibTeX | Tags: Data-driven control, Disturbance observer, Disturbance rejection, Linear Systems @inproceedings{nokeyt,This work develops a data-based construction of inverse dynamics for LTI systems. Specifically, the problem addressed here is to find an input sequence from the corresponding output sequence based on pre-collected input and output data. The problem can be considered as a reverse of the recent use of the behavioral approach, in which the output sequence is obtained for a given input sequence by solving an equation formed by pre-collected data. The condition under which the problem gives a solution is investigated and turns out to be L-delay invertibility of the plant and a certain degree of persistent excitation of the data input. The result is applied to form a data-driven disturbance observer. The plant dynamics augmented by the data-driven disturbance observer exhibits disturbance rejection without the model knowledge of the plant. |
List of English Publication
2023 |
Model Reference Gaussian Process Regression: Data-Driven Output Feedback Controller Proceedings Article In: 2023 American Control Conference (ACC), pp. 955-960, IEEE, San Diego, CA, USA, 2023, ISBN: 979-8-3503-2806-6. |
Data-Driven Inverse of Linear Systems and Application to Disturbance Observer Proceedings Article In: 2023 American Control Conference (ACC), pp. 2806-2811, IEEE, San Diego, CA, USA, 2023, ISBN: 979-8-3503-2806-6. |