Reconfiguring Hybrid Systems Using SAT
This addresses the need for AI-based reconfiguration in modern hybrid systems with frequent changes, though it appears incremental as it builds on existing SAT-based methods for search and validity definition.
The paper tackles the problem of automatically recovering hybrid systems from faults without pre-defined recovery actions, presenting a novel algorithm that uses SAT solvers to find reconfiguration operations, and demonstrates its ability to reconfigure faults on simulated process engineering systems.
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.