2021/03/30 by Claudio De Persis, Pietro Tesi, De Persis, Claudio +1 · 5 citations
Computer Science · Engineering · #FOS: Electrical engineering #Systems and Control (eess.SY) #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2103.16509
Submitted to the "24th International Symposium on Mathematical Theory of Networks and Systems" on January 2020
arxiv created 2021/03/30 · arxiv updated 2021/03/31
In a recent paper we have shown that data collected from linear systems excited by persistently exciting inputs during low-complexity experiments, can be used to design state- and output-feedback controllers, including optimal Linear Quadratic Regulators (LQR), by solving linear matrix inequalities (LMI) and semidefinite programs. We have also shown how to stabilize in the first approximation unknown nonlinear systems using data. In contrast to the case of linear systems, however, in the case of nonlinear systems the conditions for learning a controller directly from data may not be fulfilled even when the data are collected in experiments performed using persistently exciting inputs. In this paper we show how to design experiments that lead to the fulfilment of these conditions.