  Sustained research efforts have been devoted to learning optimal controllers
for linear stochastic dynamical systems with unknown parameters, but due to the
corruption of noise, learned controllers are usually uncertified in the sense
that they may destabilize the system. To address this potential instability, we
propose a "plug-and-play" modification to the uncertified controller which
falls back to a known stabilizing controller when the norm of the state exceeds
a certain threshold. We show that the switching strategy enhances the safety of
the uncertified controller by making the linear-quadratic cost bounded even if
the underlying linear feedback gain is destabilizing. We also prove the
near-optimality of the proposed strategy by quantifying the maximum performance
loss caused by switching as asymptotically negligible. Finally, we demonstrate
the effectiveness of the proposed switching strategy using simulation on an
industrial process example.
