  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.

Systems and Control;Systems and Control