CONIX Publication

Current learning-based techniques for the control of physical systems, such as reinforcement learning, require the crunching of large amounts of data for extended periods of time. In this talk we show how to obviate this hunger for data by judicious modeling. In particular, we will show how to control unknown nonlinear systems without prior data or training. Key to our approach is the re-interpretation of several results in control theory, such as Fliess and co-workers intelligent-PIDs, feedback linearization, and adaptive control, as different examples of data-driven control. We illustrate the usefulness and applicability of the results via experimental results and conclude by speculating about the right mix of model-based and data-driven design in the context of autonomous cyber-physical systems.