End-to-end guarantees for indirect data-driven control of bilinear systems with finite stochastic data

In this paper we propose an end-to-end algorithm for indirect data-driven control for bilinear systems with stability guarantees. We consider the case where the collected i.i.d. data is affected by probabilistic noise with possibly unbounded support and leverage tools from statistical learning theory to derive finite sample identification error bounds. To this end, we solve the bilinear identification problem by solving a set of linear and affine identification problems, by a particular choice of a control input during the data collection phase. We provide a priori as well as data-dependent finite sample identification error bounds on the individual matrices as well as ellipsoidal bounds, both of which are…

• DF
  Deutsche Forschungsgemeinschaft

  Awards: AL 316/15-1 \u2013 468094890, EXC 2075 \u2013 390740016
• SC
  Stuttgart Center for Simulation Science, Universität Stuttgart