Deadlock Control for a Class of Generalized Petri Nets Based on Proper Resource Allocation

Abstract

This article develops a deadlock prevention policy for a class of generalized Petri nets, namely G‐systems, which can model flexible manufacturing systems with machining, assembly, and disassembly operations. In this research, a polynomial complexity control policy for non‐blocking supervisors is established by properly adjusting resource allocation. First, a set of linear inequality constraints is derived with respect to different resource requirements such that the allocation of the system resources to various requesting processes can be appropriately restricted, that is, the resulting system has no deadlock state by imposing monitors on operation places. Moreover, an algorithm is proposed to identify the redundant constraints such that structurally simple liveness‐enforcing net supervisors can be obtained. Compared with existing polices, the proposed method can usually lead to a suboptimal supervisor with high computational efficiency. Finally, a flexible manufacturing system example is utilized to demonstrate the proposed method.

Masked observation for majority‐based control of a democratic progress model in the framework of discrete event systems

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Fault Diagnosis for Discrete Event Systems Modeled By Bounded Petri Nets

“…Solving the fault diagnosis problem of discrete event systems (DESs) consists in associating a diagnosis state, such as "normal" or "faulty" or "uncertain", to each observation. The problem has received much attention in the literature in the past few decades [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17].…”

On structural reduction of liveness-enforcing Petri net supervisors for flexible manufacturing systems: an algebraic approach

“…Many advantages can be derived from an FMS. However, due to the high utilization of resources in such a system, deadlocks can occur, which may degrade the performance of an FMS (Han & Lee, 2005;Ji & Wang, 2014;Chen et al, 2015Chen et al, , 2017aHou et al, 2016), and even lead to serious economy loss in highly automated production processes.…”