首页|A general Boolean semantic modelling approach for complex and intelligent industrial systems in the framework of DES
A general Boolean semantic modelling approach for complex and intelligent industrial systems in the framework of DES
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
维普
Discrete event system(DES)models promote system engineering,including system design,verification,and assess-ment.The advancement in manufacturing technology has endowed us to fabricate complex industrial systems.Conse-quently,the adoption of advanced modeling methodologies adept at handling complexity and scalability is imperative.More-over,industrial systems are no longer quiescent,thus the intelli-gent operations of the systems should be dynamically specified in the model.In this paper,the composition of the subsystem behaviors is studied to generate the complexity and scalability of the global system model,and a Boolean semantic specifying algorithm is proposed for generating dynamic intelligent opera-tions in the model.In traditional modeling approaches,the change or addition of specifications always necessitates the complete resubmission of the system model,a resource-con-suming and error-prone process.Compared with traditional approaches,our approach has three remarkable advantages:(i)an established Boolean semantic can be fitful for all kinds of systems;(ii)there is no need to resubmit the system model whenever there is a change or addition of the operations;(iii)multiple specifying tasks can be easily achieved by continu-ously adding a new semantic.Thus,this general modeling approach has wide potential for future complex and intelligent industrial systems.
Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education,School of Control Science and Engineering,Dalian University of Technology,Dalian 116024,China
State Key Laboratory of Fluid Power and Mechatronic Systems,School of Mechanical Engineering,Zhejiang University,Hangzhou 310027,China
National Natural Science Foundation of ChinaNational Natural Science Foundation of China
万方数据
维普
