  Understanding and manipulating work fluctuations in microscale and nanoscale
systems are of both fundamental and practical interest. For example, aspects of
work fluctuations will be an important factor in designing nanoscale heat
engines. In this work, an optimal control approach directly exploiting
Jarzynski's equality is proposed to effectively suppress the fluctuations in
the work statistics, for systems (initially at thermal equilibrium) subject to
a work protocol but isolated from a bath during the protocol. The control
strategy is to minimize the deviations of individual values of exp(-\beta W)
from their ensemble average given by exp(-\beta \Delta F}, where W is the work,
\beta\ is the inverse temperature, and \Delta F is the free energy difference
between two equilibrium states. It is further shown that even when the system
Hamiltonian is not fully known, it is still possible to suppress work
fluctuations through a feedback loop, by refining the control target function
on the fly through Jarzynski's equality itself. Numerical experiments are based
on linear and nonlinear parametric oscillators. Optimal control results for
linear parametric oscillators are also benchmarked with early results based on
accelerated adiabatic processes.
