Abstract: |
Data-driven control (DDC), that is the design of controllers directly from observed data, has attracted substantial attention in recent years due to its advantages over model-based control. DDC avoids a computationally expensive, potentially conservative model identification step and bypasses practically difficult questions such as model order/class selection. This tutorial paper seeks to offer a sampling of the different approaches that have been recently used to synthesize data driven controllers and filters, covering both analytic approaches and learning enabled ones, indicating the relative strengths of each. A second objective is to provide a key to the rapidly expanding literature in the subject, to help researchers newly interested in this field to quickly come up to speed. |