IntroductionThe current trend in industrial production processes is to have agile and flexible systems that respond quickly to the permanent changes and disturbances in the production environment.This trend has created an important volume of research and papers aimed at having production control, supervision and programming systems that respond to these demands.Most of the proposals are grouped inside what has been labeled as Intelligent Manufacturing Systems (IMS).Among them are virtual, fractal, bionic, holonic manufacturing.These proposals initially appeared for discrete manufacturing processes.However, continuous production processes such as oil and gas, chemical plants, and power generation, also face demands for flexibility and rapid response.Therefore the IMS proposals can be applied to these types of processes.Holonic Production Systems (HPS) is one of the proposals that has advanced the most.It already shows evidence of its application in industrial systems.In general terms, a HPS is formed by autonomous entities that cooperate proactively to reach a common goal.These entities are labeled holons and, through aggregation relationships, they can form groups to form the so called holarchies.The grouping of various holons or holarchies with the objective of carrying out a productive process is called a Holonic Production Unit (HPU).The principal attributes of holons are: autonomy, cooperation, proactiveness, and reactivity.Other key characteristics are the distribution of intelligence and self-similarity to build complex structures from simpler systems.In order to achieve agility and flexibility, HPSs need distributed coordination and supervision functions.These functions enable them to dynamically reconfigure the production structure and the control laws to accommodate to the new operative conditions.Reconfiguration can only be faced if the production system has flexibility with regards to the allocation of manufacturing operations and of control architectures, which enable using different control policies for different types of services, or adapting control strategies to achieve new requirements. www.intechopen.comAdvances in Petri Net Theory and Applications 76The selection of a new configuration is basically a problem of state reachability in discrete dynamics systems.Therefore, Petri nets and the supervisory control theory have been deemed appropriate to find solutions that perform well in real time.Due to the demands for temporary performance, reconfiguration has been considered a function of real time in control architecture.The work that is presented is inspired in the holonic paradigm, and supported by Petri nets and the supervisory control theory, to define, in real time, a new configuration for the production structure that adjusts to new requirements or disturbances.For the holonic paradigm perspective, each production resource is seen as a HPU, with skills, availability and capacities.The HPU makes offers and negotiates its objectives or missions.Each holon, for a determined operation condition, offers its services based on its current capacities and state and, in this manner, a highly reconfigurable system is formed.Petri Nets (PN) are a mathematical and graphic tool with the ability to capture precedence relationships and structural relationships, to model blocking, sequences, concurrent processes, conflicts and restrictions.Products manufactured by an HPU can also be modeled through PN, because of the ease to represent precedence relationships.A global HPU model is established through PN composition operations, composing the resources with the products.The initial marking of this global PN is generated from the state of the resources in real time which, in order to conduct a production mission, presents offers based on current operative conditions.Once the initial marking for the resources state is established, the PN is executed and the complete state space of the possible HPU behavior is generated.The states space, or reachability tree, is an finite automata and the trajectories between states define the possible configurations to reach the objective.The configuration selected must be feasible and controllable and must also guarantee that there neither blocking nor forbidden states in the system.Besides, the selected trajectory must lead to a satisfactory termination of the product.The supervisory control theory (SCT) is suitable to solve this type of problems which are present in DES -Discrete Event Systems.By guaranteeing properties as boundedness, a finite states space and the obtaining of a solution in finite time are assured.Once a configuration to reach an objective, based on the capacities and the state of resources is selected, the holarchies are formed.Generated for each holarchy, in a recursive manner, is a PN model of its behavior, following the same construction structure explained for the HPU.When a disturbance occurs, the holarchy tries to solve it internally by adjusting its production structure and following the proposed analysis technique.If the holarchy is incapable of achieving its mission for the new operative condition, it requests cooperation from the rest of the HPU.The new mission redistribution is carried out by following the same analysis technique, generating a global PN model formed by holarchies, and a marking for the current condition.In this manner, a proposal for determining a new configuration, which shows the advantages of using the recursivity of the holonic paradigm and the description and analysis power of Petri nets, has been developed.Real time performance is noticeably improved as the reachability tree is considerably reduced.This chapter is structured as follows.The first part presents related works highlighting research made from the holonic paradigm or making use of PN and SCT.Theoretical concepts are presented in the second part.The third part presents the construction of the HPU's global model and the obtaining of the reachability tree from the marking determined www.intechopen.com