Modelling and analyzing adaptive self-assembling strategies with Maude

Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA

Modelling and analyzing adaptive self-assembling strategies with Maude

Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA

Past, present and future mathematical models for buildings (i)

This is the first of two articles presenting a detailed review of the historical evolution of mathematical models applied in the development of building technology, including conventional buildings and intelligent buildings. After presenting the technical differences between conventional and intelligent buildings, this article reviews the existing mathematical models, the abstract levels of these models, and their links to the literature for intelligent buildings. The advantages and limitations of the applied mathematical models are identified and the models are classified in terms of their application range and goal. We then describe how the early mathematical models, mainly physical models applied to conventional buildings, have faced new challenges for the design and management of intelligent buildings and led to the use of models which offer more flexibility to better cope with various uncertainties. In contrast with the early modelling techniques, model approaches adopted in neural networks, expert systems, fuzzy logic and genetic models provide a promising method to accommodate these complications as intelligent buildings now need integrated technologies which involve solving complex, multi-objective and integrated decision problems

Novel analysis and modelling methodologies applied to pultrusion and other processes

Often a manufacturing process may be a bottleneck or critical to a business. This thesis focuses on the analysis and modelling of such processest, to both better understand them, and to support the enhancement of quality or output capability of the process. The main thrusts of this thesis therefore are: To model inter-process physics, inter-relationships, and complex processes in a manner that enables re-exploitation, re-interpretation and reuse of this knowledge and generic elements e.g. using Object Oriented (00) & Qualitative Modelling (QM) techniques. This involves the development of superior process models to capture process complexity and reuse any generic elements; To demonstrate advanced modelling and simulation techniques (e.g. Artificial Neural Networks(ANN), Rule-Based-Systems (RBS), and statistical modelling) on a number of complex manufacturing case studies; To gain a better understanding of the physics and process inter-relationships exhibited in a number of complex manufacturing processes (e.g. pultrusion, bioprocess, and logistics) using analysis and modelling. To these ends, both a novel Object Oriented Qualitative (Problem) Analysis (OOQA) methodology, and a novel Artificial Neural Network Process Modelling (ANNPM) methodology were developed and applied to a number of complex manufacturing case studies- thermoset and thermoplastic pultrusion, bioprocess reactor, and a logistics supply chain. It has been shown that these methodologies and the models developed support capture of complex process inter-relationships, enable reuse of generic elements, support effective variable selection for ANN models, and perform well as a predictor of process properties. In particular the ANN pultrusion models, using laboratory data from IKV, Aachen and Pera, Melton Mowbray, predicted product properties very well

Dynamic state reconciliation and model-based fault detection for chemical processes

In this paper, we present a method for the fault detection based on the residual generation. The main idea is to reconstruct the outputs of the system from the measurements using the extended Kalman filter. The estimations are compared to the values of the reference model and so, deviations are interpreted as possible faults. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. The use of this method is illustrated through an application in the field of chemical processe

Inductive Pattern Formation

With the extended computational limits of algorithmic recursion, scientific investigation is transitioning away from computationally decidable problems and beginning to address computationally undecidable complexity. The analysis of deductive inference in structure-property models are yielding to the synthesis of inductive inference in process-structure simulations. Process-structure modeling has examined external order parameters of inductive pattern formation, but investigation of the internal order parameters of self-organization have been hampered by the lack of a mathematical formalism with the ability to quantitatively define a specific configuration of points. This investigation addressed this issue of quantitative synthesis. Local space was developed by the Poincare inflation of a set of points to construct neighborhood intersections, defining topological distance and introducing situated Boolean topology as a local replacement for point-set topology. Parallel development of the local semi-metric topological space, the local semi-metric probability space, and the local metric space of a set of points provides a triangulation of connectivity measures to define the quantitative architectural identity of a configuration and structure independent axes of a structural configuration space. The recursive sequence of intersections constructs a probabilistic discrete spacetime model of interacting fields to define the internal order parameters of self-organization, with order parameters external to the configuration modeled by adjusting the morphological parameters of individual neighborhoods and the interplay of excitatory and inhibitory point sets. The evolutionary trajectory of a configuration maps the development of specific hierarchical structure that is emergent from a specific set of initial conditions, with nested boundaries signaling the nonlinear properties of local causative configurations. This exploration of architectural configuration space concluded with initial process-structure-property models of deductive and inductive inference spaces. In the computationally undecidable problem of human niche construction, an adaptive-inductive pattern formation model with predictive control organized the bipartite recursion between an information structure and its physical expression as hierarchical ensembles of artificial neural network-like structures. The union of architectural identity and bipartite recursion generates a predictive structural model of an evolutionary design process, offering an alternative to the limitations of cognitive descriptive modeling. The low computational complexity of these models enable them to be embedded in physical constructions to create the artificial life forms of a real-time autonomously adaptive human habitat

SOLID-SHELL FINITE ELEMENT MODELS FOR EXPLICIT SIMULATIONS OF CRACK PROPAGATION IN THIN STRUCTURES

Crack propagation in thin shell structures due to cutting is conveniently simulated using explicit finite element approaches, in view of the high nonlinearity of the problem. Solidshell elements are usually preferred for the discretization in the presence of complex material behavior and degradation phenomena such as delamination, since they allow for a correct representation of the thickness geometry. However, in solid-shell elements the small thickness leads to a very high maximum eigenfrequency, which imply very small stable time-steps. A new selective mass scaling technique is proposed to increase the time-step size without affecting accuracy. New ”directional” cohesive interface elements are used in conjunction with selective mass scaling to account for the interaction with a sharp blade in cutting processes of thin ductile shells

Towards a new methodology for design, modelling, and verification of reconfigurable distributed control systems based on a new extension to the IEC 61499 standard

In order to meet user requirements and system environment changes, reconfigurable control systems must dynamically adapt their structure and behaviour without disrupting system operation. IEC 61499 standard provides limited support for the design and verification of such systems. In fact, handling different reconfiguration scenarios at runtime is difficult since function blocks in IEC 61499 cannot be changed at run-time. Hence, this thesis promotes an IEC 61499 extension called reconfigurable function block (RFB) that increases design readability and smoothly switches to the most appropriate behaviour when a reconfiguration event occurs. To ensure system feasibility after reconfiguration, in addition to the qualitative verification, quantitative verification based on probabilistic model checking is addressed in a new RFBA approach. The latter aims to transform the designed RFB model automatically into a generalised reconfigurable timed net condition/event system model (GRTNCES) using a newly developed environment called RFBTool. The GR-TNCES fits well with RFB and preserves its semantic. Using the probabilistic model checker PRISM, the generated GR-TNCES model is checked using defined properties specified in computation tree logic. As a result, an evaluation of system performance and an estimation of reconfiguration risks are obtained. The RFBA methodology is applied on a distributed power system case study.Dynamische Anforderungen und Umgebungen erfordern rekonfigurierbare Anlagen und Steuerungssysteme. Rekonfiguration ermöglicht es einem System, seine Struktur und sein Verhalten an interne oder externe Änderungen anzupassen. Die Norm IEC 61499 wurde entwickelt, um (verteilte) Steuerungssysteme auf Basis von Funktionsbausteinen zu entwickeln. Sie bietet jedoch wenig Unterstützung für Entwurf und Verifikation. Die Tatsache, dass eine Rekonfiguration das System-Ausführungsmodell verändert, erschwert die Entwicklung in IEC 61499 zusätzlich. Daher schlägt diese Dissertation rekonfigurierbare Funktionsbausteine (RFBs) als Erweiterung der Norm vor. Ein RFB verarbeitet über einen Master-Slave-Automaten Rekonfigurationsereignisse und löst das entsprechende Verhalten aus. Diese Hierarchie trennt das Rekonfigurationsmodell vom Steuerungsmodell und vereinfacht so den Entwurf. Die Funktionalität des Entwurfs muss verifiziert werden, damit die Ausführbarkeit des Systems nach einer Rekonfiguration gewährleistet ist. Hierzu wird das entworfene RFB-Modell automatisch in ein generalised reconfigurable timed net condition/event system übersetzt. Dieses wird mit dem Model-Checker PRISM auf qualitative und quantitative Eigenschaften überprüft. Somit wird eine Bewertung der Systemperformanz und eine Einschätzung der Rekonfigurationsrisiken erreicht. Die RFB-Methodik wurde in einem Softwarewerkzeug umgesetzt und in einer Fallstudie auf ein dezentrales Stromnetz angewendet