Reconfiguring Hybrid Systems Using SAT

TL;DR

This paper introduces a SAT-based algorithm for reconfiguring hybrid systems by leveraging models of non-faulty systems, discretizing the search space, and efficiently finding valid reconfigurations without modeling faults.

Contribution

It presents a novel SAT-based method that requires only non-faulty system models, reduces search complexity, and quickly identifies reconfiguration solutions for hybrid systems.

Findings

Successfully reconfigures faults in simulated process systems.

Reduces search space for hybrid system reconfiguration.

Uses SAT solver for fast, non-optimal reconfiguration solutions.

Abstract

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.