CPS-Based Self-Adaptive Collaborative Control for Smart Production-Logistics Systems

Zhengang Guo, Yingfeng Zhang, Xibin Zhao, Xiaoyu Song

Research output: Contribution to journalArticlepeer-review

112 Scopus citations

Abstract

Discrete manufacturing systems are characterized by dynamics and uncertainty of operations and behavior due to exceptions in production-logistics synchronization. To deal with this problem, a self-adaptive collaborative control (SCC) mode is proposed for smart production-logistics systems to enhance the capability of intelligence, flexibility, and resilience. By leveraging cyber-physical systems (CPSs) and industrial Internet of Things (IIoT), real-time status data are collected and processed to perform decision making and optimization. Hybrid automata is used to model the dynamic behavior of physical manufacturing resources, such as machines and vehicles in shop floors. Three levels of collaborative control granularity, including nodal SCC, local SCC, and global SCC, are introduced to address different degrees of exceptions. Collaborative optimization problems are solved using analytical target cascading (ATC). A proof of concept simulation based on a Chinese aero-engine manufacturer validates the applicability and efficiency of the proposed method, showing reductions in waiting time, makespan, and energy consumption with reasonable computational time. This article potentially enables manufacturers to implement CPS and IIoT in manufacturing environments and build up smart, flexible, and resilient production-logistics systems.

Original languageEnglish
Article number9000952
Pages (from-to)188-198
Number of pages11
JournalIEEE Transactions on Cybernetics
Volume51
Issue number1
DOIs
StatePublished - Jan 2021
Externally publishedYes

Keywords

  • Analytical target cascading (ATC)
  • cyber-physical systems (CPSs)
  • hybrid automata
  • industrial Internet of Things (IIoT)
  • self-adaptive collaborative control (SCC)

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