  This paper presents a linear-programming based algorithm to perform
data-driven stabilizing control of linear positive systems. A set of
state-input-transition observations is collected up to magnitude-bounded noise.
A state feedback controller and dual linear copositive Lyapunov function are
created such that the set of all data-consistent plants is contained within the
set of all stabilized systems. This containment is certified through the use of
the Extended Farkas Lemma and solved via Linear Programming. Sign patterns and
sparsity structure for the controller may be imposed using linear constraints.
The complexity of this algorithm scales in a polynomial manner with the number
of states and inputs. Effectiveness is demonstrated on example systems.
