  In a Hilbert space $H$, in order to develop fast optimization methods, we
analyze the asymptotic behavior, as time $t$ tends to infinity, of inertial
continuous dynamics where the damping acts as a closed-loop control. The
function $f: H \to R$ to be minimized (not necessarily convex) enters the
dynamic through it gradient, which is assumed to be Lipschitz continuous on the
bounded subsets of $H$. This gives autonomous dynamical systems with nonlinear
damping and nonlinear driving force. We first consider the case where the
damping term $\partial \phi (\dot{x}(t))$ acts as a closed-loop control of the
velocity. The damping potential $\phi : H \to [0,+\infty)$ is a convex
continuous function which achieves its minimum at the origin. We show the
existence and uniqueness of a global solution to the associated Cauchy problem.
Then, we analyze the asymptotic convergence properties of the generated
trajectories generated. We use techniques from optimization, control theory,
and PDE's: Lyapunov analysis based on the decreasing property of an energy-like
function, quasi-gradient and Kurdyka-Lojasiewicz theory, monotone operator
theory for wave-like equations. Convergence rates are obtained based on the
geometric properties of the data $f$ and $\phi$. When $f$ is strongly convex,
we give general conditions which provide exponential convergence rates. Then,
we extend the results to the case where an additional Hessian-driven damping
enters the dynamic, which reduces the oscillations. Finally, we consider an
inertial system involving jointly the velocity $\dot{x}(t)$ and the gradient
$\nabla f(x(t))$. In addition to its original results, this work surveys the
numerous works devoted in recent years to the interaction between continuous
damped inertial dynamics and numerical algorithms for optimization, with the
emphasis on autonomous systems, closed-loop adaptive procedures, and
convergence rates.
