Designing Experiments for Data-Driven Control of Nonlinear Systems

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), solving matrix inequalities (LMI) semidefinite programs. We also how stabilize in the first approximation unknown nonlinear using data. contrast case of systems, however, conditions for learning controller directly may not fulfilled even when are experiments performed inputs. this show lead fulfilment these conditions.