Modelling chronicle recognition for distributed simulation processing with coloured Petri nets

The paper is a partial synthesis of the work that a team within the Faculty of Managerial and Technological Engineering has in the field of modeling and simulation of systems with Petri nets. The paper deals with three issues. The first part is Modeling and simulation of manufacturing systems with timed Petri nets. The first part presents the way in which the performances of some manufacturing systems are evaluated using timed Petri nets. The first example is a manufacturing system specific to the textile industry. The system has three production lines. In this case, the modeling with timed Petri nets allows the analysis of the operation of the flow lines and highlights the bottleneck. Another modeled manufacturing system is specific to a series production, more precisely it refers to the manufacture of gas cookers within a company from Oradea. The model with Petri nets also describes the supply sequences of the stocks of parts and subassemblies. The next modeled system is an automated manufacturing system for the packaging of detergents. The complexity of the Detergent Packing System and the high level of automation, require the use of efficient modeling and simulation methods to verify the validity of the solutions adopted. In this chapter is present the model of a robotic welding cell. The second part of the paper, the transition from timed Petri nets to hierarchical colored Petri nets, shows how Petri nets were developed for a robotic cell for metallographic analysis. The third part is dedicated to modeling with colored Petri nets timed with complex colors of the flexible manufacturing cell A007 within the Department of Industrial Engineering.

Multi-agent system provides the parallel logic for modeling and simulation of complex systems, while high level architecture (HLA) provides a standardized framework for distributed simulation that promotes reuse and interoperability of simulation components (federates). Integrating multi-agent system with HLA will improve the design for simulation and modeling significantly. A multi-agent simulation environment based on HLA is provided. A middleware called Wrapper Federate is used, which enables agent federates interact with RTI indirectly, whereas non-agents interact with RTI directly. Details on how Wrapper Federate interfaces with RTI, the mechanism of simulation cycle of integration system, and the communication mechanism are discussed

Petri Nets provide a graphical notation for modeling systems and performing analysis. Colored Petri Nets (CPNs) combine the strengths of ordinary Petri Nets with a high level programming language, making them more suitable for modeling large systems. A CPN model is an executable representation of a system that can be analyzed through simulation. CPN models are built using CPN Tools, a graphical software tool and interface used to create, edit, simulate, and analyze models. This tutorial is meant to introduce the reader to the vocabulary and constructs of CPNs and illustrate the use of CPN Tools in creating and simulating models by means of a familiar, simple example. In particular, we show how to create a CPN model of the call center example presented by White and Ingalls in their tutorial Introduction to Simulation.

Modern control systems are hybrid – continuous or mixed objects are controlled by discrete controllers. Colored Petri nets are theexcellent mean of simulation of control algorithms. Continuous objects can be described by differential equations. These equations canbe represented as a structural scheme. An interface between those to parts of a model is very important as well as evaluation of the time,because it can be one of the factors of control. The simulation system realized in the program package CENTAURUS-C is presented.Interface between both parts of a model is realized via specialized transition, in Petri net, named – process. Three default real variables– $Out, $In and $Prev are used in the interface. A process can carry the value of $Out into a structure of an object as a step signal andreceive signal from any output of blocks in structure as a value of the variable $In. Value of variable $Prev is a value of $Inremembered on previous step of simulation. Variable $Out can be used in an expression of input chord of a process and init the process.Variables $In and $Prev can be used in guard expression of a process to set the end of a process – firing of the specialized transition.Time in the presented system is an attribute of processes and transitions – delay. The delays of transitions are represented by specialexpressions and can be changed dynamically. Ill. 7, bibl. 11 (in Lithuanian; summaries in Lithuanian, English, Russian).Modern control systems are hybrid – continuous or mixed objects are controlled by discrete controllers. Colored Petri nets are theexcellent mean of simulation of control algorithms. Continuous objects can be described by differential equations. These equations canbe represented as a structural scheme. An interface between those to parts of a model is very important as well as evaluation of the time,because it can be one of the factors of control. The simulation system realized in the program package CENTAURUS-C is presented.Interface between both parts of a model is realized via specialized transition, in Petri net, named – process. Three default real variables– $Out, $In and $Prev are used in the interface. A process can carry the value of $Out into a structure of an object as a step signal andreceive signal from any output of blocks in structure as a value of the variable $In. Value of variable $Prev is a value of $Inremembered on previous step of simulation. Variable $Out can be used in an expression of input chord of a process and init the process.Variables $In and $Prev can be used in guard expression of a process to set the end of a process – firing of the specialized transition.Time in the presented system is an attribute of processes and transitions – delay. The delays of transitions are represented by specialexpressions and can be changed dynamically. Ill. 7, bibl. 11 (in Lithuanian; summaries in Lithuanian, English, Russian).

With the aim of solving the problems of the Petri nets to the place/transition nets automatic conversion and realization of Petri nets parallel control and running, the algorithm is proposed to transform Color Petri net systems transform into P/T nets. The algebraic model of colored Petri nets and P/T nets, and its intrinsic mechanism is analyzed, the process and theory verification of the colored Petri nets into P/T nets are given. The colored Petri nets into P/T network algorithm is proposed, through the network model of formal, colored Petri nets correlation matrix pretreatment. Experimental results show that it is an effective method to solve Color Petri Net systems transform into P/T nets, lay the foundation for various senior Petri nets system transformation into P/T nets and achieve the parallelism and simulation of running.

The travelling salesman problem, which can be solved with various methods, is a traditional and typical problem in artificial intelligence (AI). As an evolution of Petri nets, colored Petri nets (CPN) can be dedicated to modeling and analysis of different systems as a new attempt. The paper shows the brief description of the given problem and CPN, we detail the implementation of the CPN model of the problem and analyze the simulation result. This CPN example shows the potentiality of CPN application in AI.

Data mining with a multitude of methodologies is a good basis for the integration of intelligent systems. Small, specialised systems have a large number of feasible solutions, but developing truly adaptive, and still understandable, systems for highly complex systems require domain expertise and more compact approaches at the basic level. This paper focuses on the integration of methodologies in the smart adaptive applications. Statistical methods and arti cial neural networks form a good basis for the data-driven analysis of interactions and fuzzy logic introduces solutions for knowledge-based understanding the system behaviour and the meaning of variable levels. Efficient normalisation, scaling and decomposition approaches are the key methodologies in developing large-scale applications. Linguistic equation (LE) approach originating from fuzzy logic is an efficient technique for these problems. The nonlinear scaling methodology based on advanced statistical analysis is the corner stone in representing the variable meanings in a compact way to introduce intelligent indices for control and diagnostics. The new constraint handling together with generalised norms and moments facilitates recursive parameter estimation approaches for the adaptive scaling. Well-known linear methodologies are used for the steady state, dynamic and case-based modelling in connection with the cascade and interactive structures in building complex large scale applications. To achieve insight and robustness the parameters are de ned separately for the scaling and the interactions. Introduction EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 2 SNE 24(1) – 4/2014 ON 1 Steady-State Modelling multiple input, multiple output MIMO response surface methodology RSM multiple input, single output MISO Fuzzy set theory Extension principle Type-2 fuzzy Figure 1: Methodologies for modelling of complex system. EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 3 O N Linguistic fuzzy models Takagi-Sugeno (TS) fuzzy models Singleton models Fuzzy relational models Figure 2: Combined fuzzy modelling. Artificial neural networks ANN Linear networks multilayer perceptron MLP backpropagation learning Neurofuzzy systems Figure 3: A fuzzy neuron. function expansion Approximate reasoning 2 Decomposition Methodologies EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 4 SNE 24(1) – 4/2014 ON Decomposition Figure 4: Decomposition for modelling. Hierarchical clustering Partitioning-based clustering algorithms fuzzy clustering Fuzzy c-means (FCM) Subtractive clustering Neural clustering Robust clustering number of clusters Composite local model Linear parameter varying (LPV) models Piecewise affine (PWA) systems Fuzzy models Multiple neural network systems mixed approach EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 5 O N 3 Adaptive Nonlinear Scaling Membership 3.1 Working point and feasible ranges Figure 5: Nonlinear scaling [28] EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 6 SNE 24(1) – 4/2014 ON 3.2 Membership definitions linguistic range linguistic values • the corner points (Figure 5) are good for visualisation; • the parameters suit for tuning; • the coefficients are used in the calculations. 3.3 Adaptation of nonlinear scaling EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 7 O N 4 Intelligent Systems 4.1 LE models 4.2 Hybrid LE systems EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 8 SNE 24(1) – 4/2014 ON extension principle linguistification

In this work, the analysis of behavioural and structural features of automated systems has been investigated by using coloured Petri Nets (CPNs). CPN is one of the best modelling methods of the automated system. Many features of Petri Nets can be searched by reachability tree and invariants. They also show the features of CPN. Reachability tree shows the markings which can be reached by the net. With P invariants we can determine the correctness of the net. In this work, an algorithm to calculate reachability tree of automated systems is developed. P invariants of CPN is analysed and Martinez-Silva algorithm is applied to CPNs. Time performances of these methods are also determined.

Analysis of controlled technological systems using high level petri nets

The specification and distributed control of discrete event robotic manufacturing systems using Petri nets are considered, and a methodology of decomposition and coordination is presented for hierarchical and distributed control. First, a task specification is defined as a Petri net model at the conceptual level, and then transformed to the detailed Petri net representation of the subtasks. Finally, the overall Petri net is decomposed and the constituent subnets are assigned to local Petri net based controllers. The controllers are coordinated by the coordinator so that the decomposed transitions fire at the same time. System coordination algorithm through communication between the coordinator and the controllers, is presented. By the proposed method, modeling, simulation and control of large and complex manufacturing systems can be performed consistently using Petri nets.

The research on modeling and simulation of complex biological systems is getting more important in Systems Biology. In this respect, we have developed Hybrid Function Petri net (HFPN) that was newly developed from existing Petri net because of their intuitive graphical representation and their capabilities for mathematical analyses. However, in the process of modeling metabolic, gene regulatory or signal transduction pathways with the architecture, we have realized three extensions of HFPN, (i) an entity should be extended to contain more than one value, (ii) an entity should be extended to handle other primitive types, e.g. boolean, string, (iii) an entity should be extended to handle more advanced type called object that consists of variables and methods, are necessary for modeling biological systems with Petri net based architecture. To deal with it, we define a new enhanced Petri net called hybrid functional Petri net with extension (HFPNe). To demonstrate the effectiveness of the enhancements, we model and simulate with HFPNe four biological processes that are diffcult to represent with the previous architecture HFPN.

Petri net (PN) is a classical tool for modeling, simulation, and analysis. With the emergence of distributed manufacturing system (DMS), PN has been evolved into different forms such as colored PN (CPN) and timed PN (TPN). To fulfill the practical requirements, CPN has been extended to a larger and more complex model. Meanwhile, DMS becomes an important issue for industry, and how to model a complex manufacturing network for better throughput needs further investigation. A simple single type of PN only cannot fulfill all contemporary requirements at the same time. This paper proposes a new concept for a new PN-hierarchical timed CPN (HTCPN), which can be a powerful tool for modeling, simulation, and analysis for current large complex distributed manufacturing system. This paper presents a general structure of DMS and uses HTCPN to model the manufacturing system as discrete event with dynamic behavior. The proposed approach shows superiority on modeling large complex manufacturing system as it represents the material flow and transitions clearly and provides overview and detailed description for DMS.

An Approach of the Grey System Modelling and Simulation for Complex Systems was published in Systems Analysis and Simulation 1988, I: Theory and Foundations. Proceedings of the International Symposium held in Berlin (GDR), September 12–16, 1988 on page 117.

Usually, the maximally permissive control of Petri nets with an OR-logic constraint is difficult to implement. In this paper, OR-logic is easily enforced using a colored Petri net. A colored Petri net place, named colored monitor place, is designed to monitor the states of constraints. A so-called guard function of colored Petri nets that maps certain transitions into a Boolean expression is suitably defined on the colored sets. The maximally permissive control is then obtained via the guard function. In addition, a constraint transformation method for marked graphs with uncontrollable transitions is proposed.

Coloured Petri Nets (CPNs) is a language for the modelling and validation of systems in which concurrency, communication, and synchronisation play a major role. Coloured Petri Nets is a discrete-ev...