Polynomial-Complexity Robust Deadlock Controllers for a class of Automated Manufacturing Systems with Unreliable Resources using Petri nets

Abstract In the context of automated manufacturing systems (AMSs) with unreliable resources, most existing robust deadlock controllers have high computational complexity or relatively low permissiveness. This work focuses on the deadlock control problem of AMSs with a kind of unreliable resources. Petri nets are used to model the dynamic behaviors of such failure-prone AMSs. First a robust deadlock prevention controller is developed for a large class of AMSs under consideration. Such a robust controller guarantees that the system can process all types of parts continuously through any one of their routes, even if one of unreliable resources fails. Also, this robust controller is proved to be optimal, i.e., maximally permissive, during one resource failure period. Then by using the one-step look-ahead method, we establish a polynomial-complexity robust deadlock avoidance policy (DAP) with the same permissiveness as the obtained robust deadlock prevention controller. That is, such a robust DAP not only has low computational complexity, but also is maximally permissive during one resource failure period.