  Differentiation is an important task in control, observation and fault
detection. Levant's differentiator is unique, since it is able to estimate
exactly and robustly the derivatives of a signal with a bounded high-order
derivative. However, the convergence time, although finite, grows unboundedly
with the norm of the initial differentiation error, making it uncertain when
the estimated derivative is exact. In this paper we propose an extension of
Levant's differentiator so that the worst case convergence time can be
arbitrarily assigned independently of the initial condition, i.e. the
estimation converges in \emph{Fixed-Time}. We propose also a family of
continuous differentiators and provide a unified Lyapunov framework for
analysis and design.
