Exploiting the experts: Learning to control unknown SISO feedback linearizable systems from expert demonstrations *

Alimzhan Sultangazin, Lucas Fraile, Paulo Tabuada

Abstract: It was shown, in recent work by the authors, that it is possible to learn an asymptotically stabilizing controller from a small number of demonstrations performed by an expert on a feedback linearizable system. These results rely on knowledge of the plant dynamics to assemble the learned controller from the demonstrations. In this paper we show how to leverage recent results on data-driven control to dispense with the need to use the plant model. By bringing these two methodologies — learning from demonstrations and data-driven control — together, this paper provides a technique that enables the control of unknown nonlinear feedback linearizable systems solely based on a small number of expert demonstrations.