  Reconfiguration aims at recovering a system from a fault by automatically
adapting the system configuration, such that the system goal can be reached
again. Classical approaches typically use a set of pre-defined faults for which
corresponding recovery actions are defined manually. This is not possible for
modern hybrid systems which are characterized by frequent changes. Instead,
AI-based approaches are needed which leverage on a model of the non-faulty
system and which search for a set of reconfiguration operations which will
establish a valid behavior again.
  This work presents a novel algorithm which solves three main challenges: (i)
Only a model of the non-faulty system is needed, i.e. the faulty behavior does
not need to be modeled. (ii) It discretizes and reduces the search space which
originally is too large -- mainly due to the high number of continuous system
variables and control signals. (iii) It uses a SAT solver for propositional
logic for two purposes: First, it defines the binary concept of validity.
Second, it implements the search itself -- sacrificing the optimal solution for
a quick identification of an arbitrary solution. It is shown that the approach
is able to reconfigure faults on simulated process engineering systems.
