期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 51, 期 1, 页码 188-198出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.2964301
关键词
Manufacturing systems; Collaboration; Energy consumption; Real-time systems; Vehicle dynamics; Analytical target cascading (ATC); cyber-physical systems (CPSs); hybrid automata; industrial Internet of Things (IIoT); self-adaptive collaborative control (SCC)
类别
资金
- National Science Foundation of China [51675441]
- 111 Project Grant of NPU [B13044]
- Fundamental Research Funds for the Central Universities [31020190505001]
- State Scholarship Fund [201806290042]
The article introduces a self-adaptive collaborative control mode to enhance the capability of intelligent manufacturing systems by leveraging IoT and CPS for real-time data collection and processing. It addresses different exceptions by introducing three levels of collaborative control granularity and using hybrid automata to model physical manufacturing resources, solving collaborative optimization problems.
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.
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