  This paper proposes a general framework for constructing feedback controllers
that drive complex dynamical systems to "efficient" steady-state (or slowly
varying) operating points. Efficiency is encoded using generalized equations
which can model a broad spectrum of useful objectives, such as optimality or
equilibria (e.g. Nash, Wardrop, etc.) in noncooperative games. The core idea of
the proposed approach is to directly implement iterative solution (or
equilibrium seeking) algorithms in closed loop with physical systems.
Sufficient conditions for closed-loop stability and robustness are derived;
these also serve as the first closed-loop stability results for sampled-data
feedback-based optimization. Numerical simulations of smart building automation
and game-theoretic robotic swarm coordination support the theoretical results.
