Robust supervision using shared-buffers in automated manufacturing systems with unreliable resources

Hao, Yue; Xing, Keyi; Hu, Hesuan; Wu, W.; Su, Hongye

doi:10.1016/j.cie.2015.01.028

Cited by 33 publications

(18 citation statements)

References 49 publications

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“…In the literature, there have been studies dealing with the development of deadlock control strategies. Some of these studies have assumed that resources in AMSs are reliable [8,[13][14][15][16][17][18][19], while some others have assumed that they are unreliable [20][21][22][23][24][25][26][27][28][29][30]. As an exploratory study on deadlock control for reliable and unreliable resources in AMSs, Wang et al [22] suggested two strategies for one-unit resource allocation systems with unreliable resources.…”

Section: Introductionmentioning

confidence: 99%

Petri Net Model Based on Neural Network for Deadlock Control and Fault Detection and Treatment in Automated Manufacturing Systems

et al. 2020

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Previously, different deadlock control strategies for automated manufacturing systems (AMSs) based on Petri Nets with reliable resources have been proposed. However, in real-world applications, resources may be unreliable. Therefore, deadlock control strategies presented in previous research studies are not suitable for such applications. To address this issue, this paper proposes a novel three-step deadlock control strategy for fault detection and treatment of unreliable resource systems. In the first step, a controlled system (deadlock-free) is obtained using the "Maximum Number of Forbidding First met Bad Markings Problem 1" (MFFBMP1), which does not consider resource failures. Subsequently, all obtained monitors are merged into a single monitor based on a colored Petri net. The second step addresses deadlocks caused by resource failures in the Petri net model using a common recovery subnet based on colored Petri nets. The recovery subnet is applied to the system obtained in the first step to ensure that the system is reliable. The third step proposes a hybrid approach that combines neural networks with colored Petri nets obtained from the second step, for the detection and treatment of faults. The proposed approach possesses the advantages of modular integration of Petri nets and can also learn neurons and reduce knowledge, similar to neural networks. Therefore, this approach solves the deadlock problem in AMSs and also detects and treats failures. The proposed approach was tested using an example from literature.

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Section: Introductionmentioning

confidence: 99%

Petri Net Model Based on Neural Network for Deadlock Control and Fault Detection and Treatment in Automated Manufacturing Systems

et al. 2020

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“…The second one is called as ‘distributing’ type, which distributes parts requiring failed resources in their remaining routes, respectively, among the buffer slots of shared resources. Through analysis, most robust supervisory control policies belong to ‘absorbing’ type [22–25, 27, 39, 40, 42], while little policy belongs to ‘distributing’ type [36, 41]. For example, in [36], Wang et al proposed a robust control strategy for AMSs with unreliable resources based on shared‐resource capacity.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, two robust control policies are developed. In [41], Yue et al propose a robust control method consisting of two variants of Banker's algorithms (BAs). The first one is to predict whether all non‐failure‐dependent resources have available buffer slots when the system arrives at a new state from the current one.…”

Section: Introductionmentioning

confidence: 99%

Robust deadlock avoidance control for AMSs with assembly operations embedded in flexible routes using Petri nets

2019

IET Control Theory & Applications

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“…As a consequence, we develop a supervisory control policy to ensure robust deadlock-free operations in the event of resource failure. AMSs are said to be robust if all processes not requiring the failed resources can still continue their production smoothly when the unreliable resources fail [3], [4], [14], [15], [17].…”

Section: Introductionmentioning

confidence: 99%

Robust control of automated manufacturing systems with assembly operations using petri nets

Liu

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Automated manufacturing systems (AMSs) with complex topologies have become increasingly active in the area of research. However, a prerequisite is indispensable for numerous researchers to investigate AMSs. That is resources are presumptively and arbitrarily assumed never to fail; nevertheless, this does not completely match the reality. In this paper, we consider deadlock and blocking problems in AMSs with unreliable resources and assembly operations. Our objective is to develop a robust supervisory control policy that allocates resources so that parts requiring the failed resource do not block the production of parts not necessarily requiring the failed resource in case any unreliable resource fails unexpectedly. Conventional methods are based on structure-oriented and monolithic specifications. On the contrary, our supervisory policy looks ahead to anticipate whether currently-available resources are enough in their quantity to assist a concerned token to reach its desired destination without knowing the global information. Our strategy is achieved in a local and dynamic manner. It can effectively respond to resource failures.

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Robust supervision using shared-buffers in automated manufacturing systems with unreliable resources

Cited by 33 publications

References 49 publications

Petri Net Model Based on Neural Network for Deadlock Control and Fault Detection and Treatment in Automated Manufacturing Systems

Petri Net Model Based on Neural Network for Deadlock Control and Fault Detection and Treatment in Automated Manufacturing Systems

Robust deadlock avoidance control for AMSs with assembly operations embedded in flexible routes using Petri nets

Robust control of automated manufacturing systems with assembly operations using petri nets

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