Modeling and Simulation of Dynamic Systems for Efficient Control of Production Processes Using AnyLogic Software

Core models, coordinators, and connectors in the dynamic modeling and simulation of multiphase systems

We live in an era characterized by Society 4.0 and Industry 4.0 where successive innovations that are more or less disruptive are occurring. Within this context, the modeling and simulation of dynamic supervisory and control systems require dealing with more sophistication and complexity, with effects in terms of development errors and higher costs. One of the most difficult aspects of simulating these systems is the handling of vision sensors. The current tools provide these sensors but in a specific and limited way. This paper describes a six-step approach to sensor virtualization. For testing the approach, a simulation platform based on game engines was developed. As contributions, the platform can simulate dynamic systems, including industrial processes with vision sensors. Furthermore, the proposed virtualization approach allows for the modeling of sensors in a systematic way, reducing the complexity and effort required to simulate this type of system.

Scilab is a free open-source software package for scientific computation. It includes hundreds of general purpose and specialized functions for numerical computation, organized in libraries called toolboxes, which cover such areas as simulation, optimization, systems and control, and signal processing. One important Scilab toolbox is Scicos. Scicos provides a block diagram graphical editor for the construction and simulation of dynamical systems. The objective of this book is to provide a tutorial for the use of Scilab/Scicos with a special emphasis on modeling and simulation tools. While it will provide useful information to experienced users it is designed to be accessible to beginning users from a variety of disciplines. Students and academic and industrial scientists and engineers should find it useful. The book is divided into two parts. The first part concerns Scilab and includes a tutorial covering the language features, the data structures and specialized functions for doing graphics, importing, exporting data and interfacing external routines. It also covers in detail Scilab numerical solvers for ordinary differential equations and differential-algebraic equations. Even though the emphasis is placed on modeling and simulation applications, this part provides a global view of Scilab. The second part is dedicated to modeling and simulation of dynamical systems in Scicos. This type of modeling tool is widely used in industrybecause it provides a means for constructing modular and reusable models. This part contains a detailed description of the editor and its usage, which is illustrated through numerous examples. All codes used in the book is made available to the reader. This new edition includes expanded chapters, new exercises and major rewrites for examples to work with the new Maple.

This paper discusses the possibility of using a Jordan neural network as a model of dynamic systems and it presents a Model Predictive Control (MPC) algorithm in which such a network is used for prediction. The Jordan network is a simple recurrent neural structure in which only one value of the process input signal (from the previous sampling instant) and only one value of the delayed output signal of the model (from the previous sampling instant) are used as the inputs of the network. In order to obtain a computationally simple MPC algorithm, the nonlinear Jordan neural model is repeatedly linearised on-line around an operating point, which leads to a quadratic optimisation problem. Effectiveness of the described MPC algorithm is compared with that of the truly nonlinear MPC scheme with on-line nonlinear optimisation performed at each sampling instant.

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.

In the simplest cases, P-, PI- and PID-controllers with rigid (unchanged) parameter settings are used to control technological processes. If the mathematical model of the control object contains unknown disturbing influences and parameters that change during the control process, then a digital control system with a learning model should be used. The tasks of control, analysis and modeling of various processes and systems are solved using their mathematical models. The choice of a model is dictated by the conditions of implementation and the requirement of adequacy. The problem of developing control algorithms under uncertainty occupies one of the central places in modern control theory. To solve the arising problems of structural and parametric identification, as a rule, methods, and algorithms of the theory of adaptive control systems are used. The application of the principles of adaptation allows to ensure high accuracy of modeling with a significant change in the dynamic properties of the system under study, to unify individual subsystems and their blocks; reduce the development and debugging time of the system. This article discusses the problem of studying a digital adaptive system for turning the blades of guide apparatus (GA) of an axial compressor of gas turbine engines (GTE). The aim of the study is to increase the efficiency of the control system for turning the blades of the GA using a digital adaptive system. The gas-dynamic stability of the GTE axial compressor under changes in external conditions and engine throttling is provided by air bypass and turning the blades of the first and last compressor stages. The statement of the problem of synthesis of the system of adaptive control of the rotation of the blades of the GA compressor of the gas turbine engine is carried out. A mathematical model of the ACS in the canonical form “model of the system in the state space” and an algorithm for turning the blades of the GA using an adaptive PI-controller have been developed. Simulation modeling of ACS was carried out using the Matlab / Simulink software package. When implementing an adaptive PI-controller, the pressure deviation downstream of the compressor stage from the required value is reduced to 0.4 %.

Markov models have a long tradition in modeling and simulation of dynamic systems. In this article, we look at certain properties of a discrete-time Markov chain, including entropy, trace, and second-largest eigenvalue to better understand their role for time-series analysis. We simulate a number of possible input signals, fit a discrete-time Markov chain, and explore properties with the help of Sobol indices, partial correlation coefficients, and the Morris elementary effect screening method. Our analysis suggests that the presence of a trend, periodicity, and autocorrelation impact entropy, trace, and second-largest eigenvalue to varying degrees but not independently of each other and with Markov chain parameter settings as other influencing factors. The properties of interest are promising to distinguish time-series data, as evidenced for the entropy measure by recent results in the analysis of cell development for Xenopus laevis in cell biology.

Markov models have a long tradition in modeling and simulation of dynamic systems. In this paper, we look at certain properties of a discrete time Markov chain including entropy, trace and 2nd largest eigenvalue to better understand their role for time series analysis. We simulate a number of possible input signals, fit a discrete time Markov chain and explore properties with the help of Sobol indices. This research is motivated by recent results in the analysis of cell development for Xenopus laevis in cell biology that relied on the considered entropy measure to distinguish development stages from time series data of Calcium levels in cells.

In this article, a new class of quasi-orthogonal filters, based on the Legendre and Malmquist-type quasi-orthogonal polynomials, is presented. These filters are generators of quasi-orthogonal functions for which we derive and present all important properties and relations. Our article is based on the classical theory of orthogonality and orthogonal functions, and also on new results in this field of mathematics. Based on theoretical results, we design schemes for the realisation of these filters. Finally, a trail quasi-orthogonal filter is practically realised and its quasi-orthogonality is proven by performing experiments. Quasi-orthogonal filters can be successfully used for signal approximation as well as for modelling, identification, analysis, synthesis and simulation of dynamical systems.

MISSE : A Method of Modeling and Simulation of Dynamic Systems : Technological Forecasting and Social Change

Identification and modeling of calcium dynamics in cardiac myocytes

Discrete Event Simulation (DEVS) is a widely used approach for modelling and simulation of dynamic discrete systems. The modern object-oriented DEVS world view regards active objects (entities) passing passive objects (stations) along given path. An event mechanism updates movement of entities, being capable also of collisions and other state-dependent phenomena. Consequently; DEVS can be used for simulation of pedestrian and evacuation dynamics. Classical straightforward modelling approaches, based on abstract classical queuing systems, do not take into account the spatial distributions primarily — only for animation a spatial component is used. But modern object-oriented tools allow modelling of a spatial distribution primarily by means of topological attributes as well in active and passive objects at the modelling level. This contribution discusses the “spatial” features of DEVS simulation systems The chosen simulation tool, Enterprise Dynamics, offers a high flexibility due to its programming language 4d script that allows adding further functionality to the provided elements as well as creating completely new ones.

In this paper, we propose a new statistical model order reduction technique called SSMOR method, that is suitable for considering both intra-die and inter-die process variations. The SSMOR generates order reduced variational models from the original variational circuits. The reduced model can be used for fast statistical performance analysis of interconnect circuits with variational power sources. The SSMOR uses statistical spectrum method to compute the variational moments and Monte Carlo sampling method via modified Krylov subspace reduction method to generate the variational reduced models. Experimental results show that explicit moment matching is not suitable for variational analysis and Krylov subspace projection method is more reliable. The proposed method can deliver about 100times speedup over the pure Monte Carlo based projection-based reduction method with less than 1 % of errors for both means and variances in statistical transient analysis

We consider a problem of mathematical modeling and computer simulation of nonlinear controlled dynamical systems represented by differential-algebraic equations of index 2. The solution of the problem is proposed within the framework of a neural network based semi-empirical approach that combines theoretical knowledge of the modeling object with training tools applied to artificial neural networks. We propose particular form semi-empirical models implementing implicit Runge-Kutta integration formulas inside the activation function. The training of the semi-empirical model makes it possible to elaborate on the models of aerodynamic coefficients implemented as a part of it. We present a semi-empirical model that uses as theoretical knowledge the equations of a full model of hypersonic vehicle motion in the specific phase of descent in the atmosphere. The simulation results for the problem of identifying the aerodynamic coefficient, implemented as an ANN-module of a semi-empirical model of the movement of a hypersonic vehicle, are presented.

Preface to the special issue on soft computing for modeling, simulation, and control of nonlinear dynamical systems