Object-oriented high level modeling of complex systems

Modelling and predictive control: Enabling technologies for reconfiguration

Object-oriented modelling and control of flexible conveyor systems for automated assembly

This paper describes a didactical Computer Aided Software Engineering (CASE)-tool that was developed for use within the context of a course in object-oriented domain modelling. In particular, the tool was designed to address several inconveniences that challenge the realisation of the course objectives: the number of students enrolled does not allow for individual feedback (a); students have little opportunity to build a concrete information system, therefore they fail to predict the consequences of the different choices when building a conceptual model (b); students lack examples and practice on how to convert a conceptual model into a concrete information system (c); at the beginning of the course students have very different levels of prior knowledge leading to major differences in motivation and learning outcomes (d). The tool was evaluated positively by the students and was shown to have a positive impact on the student's capabilities to construct object-oriented models. It is argued that even better learning results can be realised by capitalizing on the opportunities for social interaction in an educational context.

Currently popular object-oriented modelling methods, when applied to computer-based control systems, mostly produce incomplete and fragmented models consisting mainly of state transition diagrams and message passing diagrams. Such shortcomings can limit the model's usefulness in the development process. We present a formally-based object-oriented method to construct a precise and complete analysis model of the system, which can easily be converted into a simulation of the system or a prototype operating in the real environment.

The efficacy of the multimodel framework (MMF) in modeling and identification of complex, nonlinear, and uncertain systems has been widely recognized in the literature owing to its simplicity, transparency, and mathematical tractability, allowing the use of well-known modeling analysis and control design techniques. The approach proved to be effective in addressing some of the shortcomings of other modeling techniques such as those based on a single nonlinear autoregressive network with exogenous inputs model or neural networks. A great number of researchers have contributed to this active field. Due to the significant amount of contributions and the lack of a recent survey, the review of recent developments in this field is vital. In this two-part paper, we attempt to provide a comprehensive coverage of the multimodel approach for modeling and identification of complex systems. The study contains a classification of different methods, the challenges encountered, as well as recent applications of MMF in various fields. Part 1 of this paper presents an overview of MMF for modeling and identification of nonlinear systems as well as the review of recent developments in the partitioning strategies employed.

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 %.

Many modern models contain changes that affect the structure of their underlying equation system, e.g. the breaking of mechanical devices or the switching of ideal diodes. The modeling and simulation of such systems in current equation-based languages frequently poses serious difficulties. In order to improve the handling of variable-structure systems, a new modeling language has been designed for research purposes. It is called Sol and it caters to the special demands of variable-structure systems while still representing a general modeling language. This language is processed by a new translation scheme that handles the differential-algebraic equations in a highly dynamic fashion. In this way, almost arbitrary structural changes can be processed. In order to minimize the computational effort, each change is processed as locally as possible, preserving the existing computational structure as much as possible. Given this methodology, truly object-oriented modeling and simulation of variable-structure systems is made possible. The corresponding process of modeling and simulation is illustrated by two examples from different domains.

Object-oriented modeling and simulation of the closed loop cardiovascular system by using SIMSCAPE

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

Summary. The relevance of the research topic is proven. The work has achieved the goal of the research on the formation of theoretical and practical aspects regarding the modeling and study of complex cadastral systems of real estate to ensure territorial development. The following tasks have been solved: substantiation of theoretical provisions on the determination of territorial development of land use; identification of factors influencing the provision of territorial development; determination of the features of the formation of cadastral information in the territorial development system; modeling and research of complex cadastral systems to ensure territorial development, taking into account international experience. It has been established that for modeling the parameters of complex cadastral systems, cadastral information is formed and used, which is based on the information support of 3-D and 4-D cadastres. Modeling and research of complex cadastral systems is aimed at the formation of multi-purpose information support with the possibility of developing a multi-purpose cadastre. The features of ensuring territorial development, which are characterized by the directions of formation and application of land and cadastral information, have been determined. At the same time, a decrease in the efficiency of using this information has been established due to the significant number of cadastres and registers and the regulation of the relevant process by various state institutions. The development and application of directions for ensuring territorial development is based on relevant regulatory and legal documents.Along with this, they require improvement and application of a systemic approach, which will take into account leading practices of land use. It is determined that in the territorial development system, geoinformation systems and geoinformation data are used to model relevant processes and make relevant decisions. The features of the implementation of land relations in international practices are highlighted, which are characterized by balanced land use, market aspects, the use of 3-D and 4-D cadastres, functional areas of land use, stakeholder relations, and the use of a land administration system. To ensure territorial development, it is necessary to form a quantitative basis for making informed decisions and developing appropriate recommendations. It is based on a multi-level system of indicators and the results of the development and implementation of a method for assessing the level of provision of territorial development. At the same time, information and analytical support is formed, which is formed by applying the results of modeling and research of complex cadastral systems of real estate. Keywords: modeling and research of complex systems, modern cadastral systems, territorial development, geospatial support, geographic information systems, multi-purpose cadastre, 3-D and 4-D cadastres.

Modelling and simulation are key tools for analysis and design of systems and processes from almost any scientific or engineering discipline. Models of complex systems are typically built on acausal Differential-Algebraic Equations (DAE) and discrete events using Object-Oriented Modelling (OOM) languages, and some of their key concepts can be explained as symmetries. To obtain a computer executable version from the original model, several algorithms, based on bipartite symmetric graphs, must be applied for automatic equation generation, removing alias equations, computational causality assignment, equation sorting, discrete-event processing or index reduction. In this paper, an open source tool according to OOM paradigm and developed in MATLAB is introduced. It implements such algorithms adding an educational perspective about how they work, since the step by step results obtained after processing the model equations can be shown. The tool also allows to create models using its own OOM language and to simulate the final executable equation set. It was used by students in a modelling and simulation course of the Automatic Control and Industrial Electronics Engineering degree, showing a significant improvement in their understanding and learning of the abovementioned topics after their assessment.

PurposeThe purpose of this paper is to propose a new method for computing validities in the multimodel approach.Design/methodology/approachThe multimodel approach offers an interesting alternative and a powerful tool to bypass the difficulties to model, control and diagnose a nonlinear and complex system. Its idea is defined as the apprehension of a nonlinear behaviour of a system by a set of local models characterizing the system operation in different operating zones. In spite of the success of its application in different fields, many problems related to the synthesis of multimodel approach remain open. These include, in particular, the method of obtaining the contribution degrees, also called validities, of the base-models for the deduction of the multimodel output.FindingsThe presented method may lead to superior results in comparison with the residue approach commonly used in the calculation of validities. Numerical simulation results and an experimental validation on a semi-batch reactor clearly illustrated the effectiveness of the proposed method and proved its impact on the improvement of the performances of the multimodel approach. Moreover, the multimodel approach using the new validities’ computation method can lead to perfect modelling of the process.Practical implicationsThe proposed method discussed in the paper has the potential to make the multimodel approach more efficient in the modelling of complex real systems.Originality/valueA significant contribution of the paper is the formulation of a new constrained optimization problem that can be solved by using a powerful mathematical tool such as the active set method, allowing to estimate the validity indexes in the multimodel approach. The obtained optimal solution can lead to perfect modelling of nonlinear and complex real systems.

Overview: New directions for simulation and modeling of complex systems

The modelling of mechatronic systems is a complex field in system design. The derivation of the mathematical representation is necessary for the calculation of the behaviour of the system. The derivation process can be divided into different levels with an encapsulated functionality. On the different levels, textual descriptions are introduced for system representation; they are logically interrelated by graph transformations. To manage the complexity resulting from many different part descriptions, object-orientation is employed.

A Modelica-based Modeling and Simulation Framework for Large-scale Cyber-physical Systems of Systems