Supervisory Control And Schedule Of A Class Of Automated Guided Vehicle Systems Via Petri Nets

With the development of industry 4.0,more and more attention is drawn to intelligent manufacturing that should be capable to quickly and flexibly respond to the changes of orders,devices,and raw materials.Further,the orders tend to be of small quantity,customization and multi-batch.This requires that an automated guided vehicle(AGV)system be employed to intelligently and efficiently perform transportation tasks,which vary quickly.Automated Guided Vehicle System are widely used in the transportation and distribution of materials in manufacturing systems because of their efficiency and intelligence.AGVs in AGVS uaually work at the same time and work along the designated route to complete the transport task with the signal given by the guidance system.AGV collision may occur in the situation of path crossing and space limitation,and improper allocation of path resources may also cause deadlock,so path planning strategy is essential for transportation cost and efficiency.Meanwhile,due to the limited sensors and actuators,there may be indistinguishable and uncontrollable events in the AGV system,which exacerbates the complexity of AGV scheduling and control problems.This paper uses advanced Petri nets as a tool to model the AGV system.Based on this,the methods of collision prevention,deadlock prevention and path planning for the AGV system are proposed.The main content are as follows:1.For the collision problem of the AGV system,two methods are proposed to solve the collision problem during the movement of the AGV.First,the labeled Petri net is used to model the AGV system,for uncontrollable events in the system,linear constraints are given to solve this problem,for indistinguishable events,two solutions are proposed.A maximum allowable controller is designed by Consistent marking set and constraints on uncontrollable changes,the maximum allowable controller is used to calculate the maximum allowable transitions that can be fired without collision in AGV systems.The second method is Extended danger area,when uncontrollable event is sloved,constraints are given to resolve indistinguishable events,and an anti-collision controller based on the extended danger zone method is designed to avoid collisions.2.Aiming at the deadlock problem of the AGV system,after analyzing the experimental results of the two methods to solve the collision problem,when the has occurred,analyzing the relationship between the extended dangerous domain where the consistent marking set method is used to solve collision problem with no deadlock during the movement of AGVs,so there no search on it;when the extended danger zone method is used to solve the collision problem with deadlock occur during the movement of AGVs,so corresponding analysis is given,analyzing the relationship between extended danger zones where the token is located when a deadlock occurs,and the impact of tokens in extended danger area on deadlock problem,designing corresponding constraints to prohibit the occurrence of deadlock situations,thereby avoiding deadlocks.3.For the dispatch problem of AGV system,the colored timed Petri net is used to model the system.According to the principle of the model predictive control algorithm and the AGV system scheduling target,the optimization goal of the model predictive control algorithm is given;in the limited prediction step,rolling optimization is performed on the local optimization target,the extreme value of the optimization objective function is used as the judgment condition,the control variable sequence is determined,and finally the optimal scheduling path is obtained.