Petri net based Methodology for the Development of Collaborative Production Systems Paulo Leitão, Member, IEEE and Armando W. Colombo, Member, IEEE
Abstract— This paper proposes a methodology for the development of collaborative (agent-based) production systems, using High-Level Petri nets. The proposed methodology supports the development life-cycle from specifications analysis through to design-validation and implementation of collaborative and re-configurable production systems and their control systems, in an integrated manner. It covers a wide spectrum of application domains, ranging from intelligent mechatronic devices to multi-agent distributed manufacturing control systems.
I. INTRODUCTION
Currently, to stay in the business, a manufacturing enterprise should be able to change promptly and dynamically its product catalogue and react quickly to unexpected disturbances. In this scenario, the increased need for agility and re-configurability requires high degree of integration and interaction among distributed and intelligent components, leading to a new class of production control systems, called collaborative automated production systems, where: • A complex problem is divided into several small problems, using a distributed approach, with the development of intelligent building blocks, i.e. control units; • Each control unit is autonomous, having its own objectives, knowledge and skills, and encapsulating intelligent functions; however, none of them has a global view of the system; • The global decisions (e.g. the scheduling, monitoring and diagnosis) are determinate by more than one control unit, i.e. the control units need to work together, interacting in a collaborative way to reach a production decision; • Some control units are connected to physical automation devices, such as sensors, robots and CNC Paulo Leitão is with the Polytechnic Institute of Bragança, Quinta Santa Apolónia, Apartado 1134, P-5301-857 Bragança, Portugal (corresponding author, phone: +351.273303003; fax: +351.273313051; e-mail:
[email protected]). Armando Colombo is with Schneider Electric - HUB & Globalization of Technology, Steinheimer Str. 117, 63500 Seligenstadt, Germany (email: armando.colombo@ de.schneider-electric.com).
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machines; Agent-based and holonic manufacturing systems are two examples that addresses this new class of collaborative control. These new approaches are characterized by the interaction of distributed and autonomous entities, supporting the agility and re-configurability requirements, and differ from traditional approaches due to their inherent capabilities to adapt to changes without external intervention. PROSA [1], MetaMorph [2] and ADACOR [3] are examples of well established approaches to intelligent manufacturing control systems derived from agent-based and holonic concepts. The design and implementation of such collaborative and re-configurable automated production systems and their supervisory control systems are complex tasks, requiring a formal modeling methodology that formalizes the structure and the behaviour of these kinds of systems, aiming to simplify their understanding and synthesis. There is currently a lack of a low-cost, detailed and coherent design-implementation process for industrial collaborative automated production systems. This is mainly a consequence of the current manually implementation of the control system, in opposite to the implementation derived from a model-like description of the production system. In this current scenario, the correctness of the design can only be validated after the implementation phase. Thus, the whole design-implementation process is very time-consuming, presenting high rates of misunderstanding and mistakes, and, as a consequence, it is very expensive [4-5]. Integrating the analysis, modeling and validation, it is possible to detect these misunderstanding and mistakes during the design phase, i.e. before to go into implementation, leading to a reduction of costs and time effort. So, the answer to this problem is the establishment of a methodology that covers the development life-cycle from analysis through to validation and implementation in an integrated manner, catching control logic errors before the implementation of the real production system. Such methodology should use a formal modeling formalism that capture characteristics like concurrency or parallelism, asynchronous operations, deadlocks, conflicts and resource sharing, which are inherent to flexible
II. HIGH-LEVEL PETRI NET-BASED METHODOLOGY The proposed H-L Petri net based methodology provides a catalogue of services that simplifies the development of collaborative automation systems, from the design to the operation. It facilitates the conception, definition and
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formal specification of an “encapsulation process” in industrial production systems, when mechanic, control and intelligence are integrated in an automation unit, designated by collaborative smart control unit. The methodology combines the bottom-up approach inherent to distributed systems, where the complete production system emerge from the interaction among individual and autonomous smart control units, and the topdown approach, taking advantage of the stepwise refinement associated to H-L Petri nets, as illustrated in the Fig. 1.
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manufacturing systems. Additionally, it is crucial that the formal modeling formalism has the capability to validate the behavioural specifications of these event-driven systems, and also the analysis of other important aspects, such as those related to the systems performance. State Diagrams, Function Blocks and Grafcet modeling approaches present several drawbacks to model and validate efficiently collaborative automation systems [6]. UML (Unified Modeling Language) [7] is a modeling tool adequate to model object-oriented systems, but it doesn't support efficiently the modeling of their dynamic behaviour and the formal validation of these specifications. Petri nets have a well-founded mathematical theory and a very good capacity to formally and graphically model, analyze and validate certain typical relationships and to visualize certain concepts, such as concurrency and parallelism, synchronization, resource sharing, mutual exclusion, memorizing, monitoring and supervising [8], which are typical specifications of collaborative automation systems. In fact, the Petri net formalism allows designing the control system behaviour, but also to validate and to verify the system specifications. Additionally, using exponential distributions associated to timed transitions it is possible to incorporate in the Petri nets all advantages associated to Markov chains. The modeling of simultaneous execution of several instances of the same component (e.g. a type of product) is easily modeled by ordinary Petri nets. However, the modeling of simultaneous execution of several instances of different components (e.g. different types of products) is complex using ordinary Petri nets, since it requires the usage of as many Petri net models as the number of available components (e.g. types of products) in the system. The use of High-Level Petri nets (H-L Petri nets) allows reducing this complexity by compressing the representation of states, actions and events. Also the structural conflicts can be easily solved using H-L Petri nets through the association of guards functions to transitions. Motivated by these facts, this paper introduces a structured methodology for the development of collaborative (agent-based) automated production systems, using H-L Petri nets. The proposed methodology supports all the phases of the control system life-cycle, from specification to re-use and reconfiguration. An important assumption is the integration of specifications analysis, modeling and validation processes in an unique design phase.
Fig. 1. Sinergy between the Bottom-up and Top-down Approaches
The proposed methodology comprises a set of steps grouped in the design, implementation and operation phases. In the design phase, the catalogue includes services to identify the automation components, model and validate the smart (agent-based) control units, and formal specify the complete collaborative automation scenarios. The dynamic behaviour of collaborative control units is formally modeled and validated using H-L Petri nets, which ensure a rigorous specification due to its powerful mathematical foundation and facilitates the integration of mechatronics, control and intelligence. Automation control units, as parts of a collaborative and distributed real-time system, need to interact in order to achieve global objectives. Thus, the specification of the complete collaborative control system requires the specification of coordination models, among individual HL Petri net models. The simulation of this virtual discrete system allows refining strategies or specifications of the system, detecting errors and mistakes before to implement the real production system. The implementation phase is related to the implementation of individual control units, supported by a set of proper mechanisms, such as the automatic code generation, both for low-level control, according an IEC 61131-3 language, or for high-level control, according an agent development framework such as JADE (Java Agent Development Framework). This task is supported by the use of re-usable libraries for the implementation of generic functions and behaviours, and for the development of
specific components dependent from the application requirements. The development of specific wrappers for each physical automation device is an example of specific components. Once the production system is designed and implemented, it is required to set the system into operation. The real time supervision of the control system using H-L Petri nets allows easily to re-formulate the specifications, in order to address the changes faced to the automation system along its life-cycle. The methodology presents several innovation aspects, such as the integration of analysis, modeling and validation processes, which are articulated with the implementation and real time supervision of the whole system, leading to a reduction of the development costs and time effort, supporting also the adaptation and re-configurability needs. Additionally, the methodology provides the capability to model the system behaviour from the high-level control abstraction to the hardware control. The detailed description of each methodology phase will be discussed in the following sections. III. DESIGN PHASE The analysis of the collaborative (agent-based) automated production control system is achieved through a description, namely, a model of the system. The behaviour of each collaborative smart control unit, presented in the production system, is formally specified using H-L Petri nets. It comprises, in an integrated manner, the specification analysis, modeling and validation, as illustrated in the Fig. 2. A. Modeling The proposed approach allows identifying the functional manufacturing components, with different degrees of granularity (i.e. manufacturing cell, machine, sensor actuator, etc.), breaking them into reusable units, i.e. hardware components. These hardware components, and specifically their dynamic behaviour, will be represented by H-L Petri net models, which are a skeleton of the corresponding smart (agent-based) control unit, which implements the main control functions, such as scheduling, dispatching, execution, monitoring diagnosis and reaction to disturbances. The H-L Petri net model of each smart (agent-based) unit needs to include all significant information on both production system and its control operations: processingplans, resources, layout, commands, control laws, etc. and their inter-relationships. The model will result in a computational model practical for analytical validation and in a simulation model, which allows experiments to be performed onto the system model.
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Fig. 2. Design of H-L Petri net based Collaborative Automation Systems
The H-L Petri nets used in this work are Coloured Petri Nets (CPN), which are mathematical-graphical oriented formalisms for design, specification, validation and verification of concurrent systems [9,10]. CPN have got their name because they allow the use of tokens that carry data values and can be distinguished from each other, in contrast to the tokens of low-level Petri nets, which by convention are drawn as black-dots. Fig. 3 depicts the graphical and data structure of a sample CPN tailored for collaborative production automated system specifications [11]. Briefly, the circles of the net are called places and represent the states of the modelled production resources, i.e., manufacturing units plus mechatronics components, the tasks being performed or the occurrence of an error. The marking of each place belong to a specific set of coloured tokens. In this case, each place can contain a set of markers called tokens carrying a data value, which belongs to a given type or set of types, such as types of hardware components, e.g. robots or CNC machines, tasks of a work-plan, types of products and parts to be processed, and types of errors related with an operation. In the
example of Fig. 3, the marking of the place p1 belongs to the set of robots represented by different robots types. p1 resource is free ta
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Fig. 3. An Example of a CPN to Model an Automated System
The rectangles are called transitions and describe the manufacturing activities according different firing-modes: instantaneous firing to model logic conditions or the occurrence of events, and 3-phases-timed firing to model physical operations, i.e. actions related with changes in the real world. In the example of Fig. 3, the transition tb models an assembly task, i.e. the robot is placing a part in the pallet. Guards, also called transition firing-modes, are associated to the transitions. They represent restrictions to the type of data value, i.e., coloured marks, which a transition can move during its firing. Typically, different functions can be modelled: • [G&(t)]: Boolean function of parameters. • [IG(t)]: Boolean function of external events. • [CG(t)]: Boolean function that issues external events. The arcs connect places with transitions and transitions with places. They have an attached arc function (expression), which describes how the state of the CPN changes when the transitions are fired, i.e. defining the relations between the firing-modes of transitions and the marking of places. The net evolution, i.e. dynamic behaviour, is supported by the firing of transitions, which is characterized by the movement of tokens between places. To be fired with
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respect to a firing-mode, a transition must have sufficient tokens on its input places. In this case, the tokens must take values that match the arc expressions, and they must belong to a type that match also the guards associated with the transition. At least two aspects are represented by the evolutions of the net: • One expresses serial and concurrence events that can be observed, i.e., the interaction states achieved by the collaborative control units. • Other describes the causally precedence that exists among production components, i.e., physical agents, and interaction acts occurred during production control and management. In order to achieve a formal specification of the logic control structure, the methodology considers a top-down approach, refining step by step some transitions to include enough details about system operation for purpose of hardware implementation. The stepwise refinement concept associated to H-L Petri nets, consists in exploding sequentially and hierarchally some timed transitions, until reach control over a physical automation device, i.e. replacing a timed transition by a more detailed and refined sub-net so that a large Petri net can be obtained. Fig. 3 illustrates the explosion of transition tb that represents an activity at hardware level, in order to include more details about the execution of the operation by the robot. This includes the interaction with the physical mechatronic device, both for sending commands to actuators or receiving information from sensors, for example modeling the following sequence of operations: a) a robot program is called, b) a robot program is started, c) a robot program is running and d) a signal finished is generated by the robot. All these tasks are contained in the transition tb, which means that the transition can be splited into more fine tasks and each of them has some relation with hardware, such as electronics and pneumatics. Using the stepwise refinement of H-L Petri net models, the control specifications of a collaborative unit are automatically generated (i.e. one catalogue that aggregates mechatronics and control specifications). The sub-models, according to the degree of refinement, are the different software control modules of the hardware. The model of the collaborative smart control unit is at the end or contains at the end, the control software of the collaborative unit. B. Formal Validation The formal validation aims to verify the correctness of the system design and to verify the initial specifications when the hardware or software components (control and intelligence) are completely integrated in each model. Two kinds of analysis are proposed: qualitative and quantitative analysis. The qualitative analysis verifies the compliance of certain desirable specifications of the system components
and system behaviour such as absence of deadlocks, cyclic behaviour, finite number of system states, finite capacity and boundedness of resources and possible control sequences. Some important set of qualitative analysis can be elaborated: structural and behavioural properties, Pinvariants and T-invariants. The analysis of behavioural properties allows verifying some properties that depend of the initial state, or marking, of the net, such as the liveness, boundedness and conservativeness. On the other hand, the analysis of structural properties allows verifying other kind of properties, which depend on the topology, or net structure, of a Petri net, such as conflicts. The analysis of P-invariants allows confirming mutual exclusion relationships among places and resources, and the analysis of the T-invariants shows the several sequences of transitions fires that conducted to the path of operations. The quantitative analysis, also called performance analysis, takes into account system specifications, therewith checking the system compliance with specified performance indexes, such as throughput of the system, percentual use of a resource and manufactured parts per time units. Normally, this information is presented in terms of a Gantt diagram. This type of analysis allows to perform different kind of simulation/analysis and also to perform simulation of the system operation under different scenarios, as illustrated in the Fig. 4. This feature is crucial to allow the re-design and re-engineering of the control system during the design phase and before to start the implementation phase. H-L Petri net models
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analysis of: - work cycles - ocurrence of errors - statistical information ... extraction of conclusions about system operation
Fig. 4. Simulation of H-L Petri net Models under different Scenarios
Based in the structural analysis and in the conclusions of the performance analysis, optimized strategies, re-tuning of some parameters and also re-design of the control system can be performed. C. Specification of Complete Collaborative Systems Collaborative control units placed in a distributed automation system are real-time systems, not completely autonomous, that need to interact in order to achieve global objectives. The relationships between collaborative units,
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constraints and rules for the composition of H-L Petri net based models of collaborative units are recognized as coordination model, allowing having the whole view of the system. Other researchers, such as [12] and [13], use the concepts of orchestration and choreography in a similar way. According to [12] orchestration can be defined as the practice of sequencing and synchronized execution of services, which encapsulate business or manufacturing processes. A complementary concept is choreography that considers rules that define the messages and interaction sequences that must occur in order to execute a given process. At system level, the coordination model represent, in a formal manner, the structure and behaviour of the system, taking in consideration the competition, cooperation, synchronization, parallelism/concurrency and relationships among resources and their tasks: • Competition is solved after solving conflicts (structural and behavioural), which are represented by transitions simultaneously enabled but in a mutual exclusion firing condition relationship; • Cooperation will be represented by merging preconditions for a given transition; • Synchronization is represented by the firing of transitions, which are related to the above defined cooperation relationship; • Parallelism/concurrency is represented by transitions that are enabled and can be fired independently from each other, allowing the representation with only one model of a high distributed system. After solving all relationships described above, a coordination model is reached, being a mathematicalgraphical representation of collaborative production system, designated in this work by virtual discrete system. The coordination model is a skeleton of the coordination control system of the real system, especially because each 3-phases-timed transition of the coordination model or of each individual control unit model is representing real activities (mainly time consuming), which are closed related to hardware components, such as robot programs, pallet movements and loading/unloading operations. The amount of time a 3-phases-timed transition is running in the model is representing the time that a modeled resource is performing some action. In this paper, the coordination model is illustrated by solving the synchronization relationship among collaborative control units, which is performed by synchronizing the individual H-L Petri net models. The synchronization of Petri net models is extracted from the UML sequence diagrams developed to model the conversations between collaborative control units. Several methods have been applied for the interactions
of real-time systems, such as MUTEX, Queue Theory and Mailbox [14]. In this work the synchronization between individual H-L Petri net models uses mailbox structures with additional places playing the role of Mailboxes that synchronize the evolution of each individual model. As illustrated in the Fig. 5, two distinct Petri net models are synchronized using additional places to manage the evolution of the net, which are extracted from the developed UML sequence diagram for their conversation. UML sequence diagrams
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production control system specifications requires that the properties of the model are proved under all possible conditions. Due to time and budgetary constraints, it is impossible to validate the accuracy of all logical paths. Therefore, the purpose is to increase the confidence in the model credibility, as much as by its formal properties, i.e. properties of its structure, rather than trying to test the model after its conception. The proposed approach relies on the fact that the collaborative production system structure and behaviour specifications are formally represented by means of elements of the H-L Petri nets, like place- and transition-flows. As main result, the production engineer has a H-L Petri net based virtual collaborative production environment, which is a faithful representation of the real system and behaves according to the desired production and production control specifications.
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The set of rules that are in the catalogue allows improving the structure and behaviour of the co-ordination model for being used as formal specification of a complete collaborative automation scenario. This leads to the automatic system adaptation and reconfiguration, since the coordination model supports one of the main functionality of a collaborative automation system, that is the collaboration/co-operation (for example the introduction of new hardware, i.e. new models of the catalogue, breakdown of components, new production specifications for new products, etc.). D. Synthesis The objective of synthesis is to build a supervisor that must guarantee the compliance of the system specifications, imposed on the production system. Combining modeling and validation methods and using the theoretical foundations of H-L Petri nets, a practical procedure to formally specify and design collaborative production control systems is introduced. The model reflects the set of specifications of the production environment by means of its structural and behavioural properties. Moreover, the synthesis of such formal model can be done by taking into account that it fulfils a set of properties, since the modeling requirements were considered during its development. An analysis of results shows that an exhaustive (complete) validation of the production components and
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The second phase of the proposed methodology is related to the implementation of the collaborative production system through the implementation of its individual control units and associated coordination models, when the system specifications were verified and validated in the previous design phase. The implementation phase comprises the codification, parameterization and development of wrapper interfaces. The codification process is supported by the automatic code generation, translating each H-L Petri net model that represents an individual and autonomous control unit, to a running software package using an appropriate language, as illustrated in the Fig. 6. Having hierarchy decomposition with certain degree of refinement, two distinct control levels are identified: a) low-level control, driven by an industrial programmable controller, with a strong connection with hardware, such as electronics, instrumentation and pneumatics, and b) high-level control, typically driven by industrial computers, and related to management and supervisory control functions. In this way, for low-level control applications that use programmable controllers, the code generation will follow one IEC 61131-3 standard language (e.g. Ladder diagrams), and for high-level applications, supporting agent-based or holonic manufacturing control systems running in industrial computers, the code generation follows a high-level language, such as Java or C++, and if possible compliant with a platform for the development of multi-agent systems, such as JADE. The coordination models need to be translated into service interfaces to implement the synchronization mechanisms between those individual control units. These service interfaces should be defined according each control unit, and may use communication mechanisms to
send/receive messages over an industrial network. The development of the service interface is constrained by the type and level of control application and the type of industrial network, which can range from an Ethernet network to a fieldbus network. p1
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need by encapsulating a control unit with a server (commonly a web server) that acts like a bridge between the internal structure and the exposed interface. More complex systems, such as machine centers, manufacturing stations or lines, can be easily build through the assembling of implemented individual control units and designing of proper coordination models.
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Fig. 6. Code Generation for Logic and Coordination Control Levels
Since each H-L Petri net model represents a smart control unit class (e.g. products or resources), it is necessary to proceed with an instantiation of each implemented control unit, customizing each one with the proper attributes, skills and knowledge. A particular customization required for each control unit, is related with the development of wrapper interfaces, allowing integrating physical mechatronic devices (sensors, actuators, PLCs, robots, CNC machines, etc.), if it represents one. This integration should be independent from the collaborative control approach and assumes a crucial role, since these kinds of systems involve the interaction with hardware components, either to sense or to change its state. The codification process is also supported by the re-use of pre-defined logic control units, both to implement generic functions and specific components dependent from the application requirements. Dealing with a collaborative automation system, some interoperability problems arise, due to the presence of heterogeneous hardware and software components. This problem can be overfilled by using standard ontologies that provide common understanding for all distributed entities belonging to the collaborative automated production control system. Service-Oriented Architectures (SOA) are an emergent approach that address this interoperability
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OPERATION PHASE
After the implementation of the collaborative automation control application, the system is set into operation, triggering the operation phase, which is the third main phase of the proposed methodology. In this phase, one aims to achieve the continuous improvement of the control system, re-tuning the control parameters and learning from the execution, leading to a re-specification and reconfiguration of agent-based control applications. The real-time supervision of the automation control system is performed by synchronizing the operation of the collaborative production control system with the whole HL Petri net model (which is considered as the virtual production system), in order to allow controlling and monitoring the system. For this purpose, the signals from the sensors and the status of mechatronics devices are acquired, using appropriated data acquisition boards, and connected with the transitions guards of the whole H-L Petri net model, as illustrated in the Fig. 7. Additionally, acting in some transition guards of the model, it will be possible to control the real operation of the collaborative production control system, by forcing some actuators.
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Fig. 7. Real Time Supervision of the Automated Control System
The virtual production system model evolves in a synchronized manner with the real production system, by
the dynamic evolution of the H-L Petri net model. A production system analysis and reporting can then be generated providing real-time performance statistics, such as average utilization of resources, total busy time of each resource, manufacturing lead time, number of parts produced, work in process (WIP) and production rate. The on-line monitoring of these performances indexes allows extracting conclusions that support the re-formulation of control strategies and system specifications, addressing the changes of the manufacturing system along its life-cycle. The adaptation and re-configuration of the manufacturing system is currently a key issue, being a drawback in the available methodologies. This characteristic is easily supported using this H-L Petri net methodology: • The introduction of new components belonging to a control unit class already available in the catalogue only requires the instantiation of one more object and its consequent customization (i.e. the addition of a new token in the corresponding H-L Petri net model). • The introduction of a new component, not available in the catalogue, requires the development of a new H-L Petri net model that represents its dynamic behaviour, and the consequent definition of associated coordination models. • The remotion of a component only requires the remotion of the token associated to the component in the correspondent H-L Petri net model. • The modification of the component characteristics or behaviour requires the modification of the H-L Petri net model and its parameters, and probably the modification of associated coordination models.
computational tool to support the development life cycle of collaborative automation systems using H-L Petri nets, based in the proposed methodology. ACKNOWLEDGMENT The authors would like to thank the European Commission and the partners of the Innovative Production Machines and Systems (I*PROMS) Network of Excellence for their support. REFERENCES [1]
[2] [3] [4]
[5]
[6]
[7] [8]
VI. CONCLUSION
[9]
The Petri net based methodology proposed in this paper supports the path from the definition of specifications of the collaborative control units, to their operation, passing through the analysis, modeling, validation, and even the reengineering of the production control system (if necessary). The technique for modeling and implementing production control systems is based in the H-L Petri net formalism and considers both structural and functional/performance specifications, and properties of the production system to be controlled, and the associated control system. The establishment of coordination models between H-L Petri nets models, representing smart agent control units, assumes a crucial role to build a virtual production system that is a faithful representation of the real system and behaves according to the desired production and production control specifications. The implementation of the collaborative control production system can be then easily derived from the designed and simulated virtual model. This work is being sustained with the development of a
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[10]
[11]
[12] [13]
[14]
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