  This paper considers the problem of learning control laws for nonlinear
polynomial systems directly from the data, which are input-output measurements
collected in an experiment over a finite time period. Without explicitly
identifying the system dynamics, stabilizing laws are directly designed for
nonlinear polynomial systems using experimental data alone. By using data-based
sum of square programming, the stabilizing state-dependent control gains can be
constructed.
