Due to the competition for limited resources by many concurrent processes in large-scale automated manufacturing systems (AMSs), one has to resolve any deadlock issue in order to reach their production goals without disruption and downtime. Monolithic resolution is a conventional approach for optimal or acceptable solutions, but may suffer from computational difficulty. Some decentralized methods are more efficient in finding approximate solutions, but most are application dependent. By modeling AMSs as Petri nets, we develop an innovative distributed approach, which can create a trajectory leading to a desired goal and is adaptable to different kinds of applications. Control strategies are applied to processes locally such that they can proceed concurrently and efficiently. Global goals are always reachable through the local observation, control, and execution of processes without knowing external and extra information. Polynomially complex are designed to find distributed controllers.
['Hesuan Hu', 'Rong Su', 'MengChu Zhou', 'Yang Liu']
Polynomially Complex Synthesis of Distributed Supervisors for Large-Scale AMSs Using Petri Nets