Features of Heterogeneous Dynamic Systems

This project is part of a research effort to design a hydrogen plant and its interface with a nuclear reactor. This project developed a dynamic modeling, simulation and optimization environment for nuclear hydrogen production systems. A hybrid discrete/continuous model captures both the continuous dynamics of the nuclear plant, the hydrogen plant, and their interface, along with discrete events such as major upsets. This hybrid model makes us of accurate thermodynamic sub-models for the description of phase and reaction equilibria in the thermochemical reactor. Use of the detailed thermodynamic models will allow researchers to examine the process in detail and have confidence in the accurary of the property package they use.

Noise contamination is a common challenge in the analysis of time series data, where stochastic perturbations can obscure deterministic dynamics and complicate the interpretation of signals from chaotic and physiological systems. Reliable identification of noise regimes and their intensity is therefore essential for robust analysis of dynamical and biomedical signals, where incorrect attribution of stochastic perturbations can lead to misleading interpretations of system behavior. For this reason, the present study examines the role of complexity-based descriptors for identifying stochastic perturbations in time series and analyzes how these metrics respond to different noise regimes across heterogeneous dynamical systems. A supervised learning approach based on complexity descriptors was developed to analyze controlled perturbations in multiple signal types. Gaussian, pink, and low-frequency noise disturbances were injected at predefined intensity levels into the Rössler and Lorenz chaotic systems, the Hénon map, and synthetic electrocardiogram signals, while AR(1) processes were used for validation on inherently stochastic signals. From these systems, eighteen entropy-based, fractal, statistical, and singular value decomposition-based complexity metrics were extracted from either raw signals or reconstructed phase spaces. These features were used to perform three classification tasks that capture different aspects of noise characterization, including detecting the presence of noise, identifying the perturbation type, and discriminating between different noise intensities. In addition to predictive modeling, the study evaluates the complexity profiles and feature relevance of the metrics under varying perturbation regimes. The results show that no single complexity metric consistently discriminates noise regimes across all systems. Instead, system-specific relevance patterns emerge. Under given experimental constraints (data partitioning, machine learning algorithm, etc.), Approximate Entropy provides the strongest discrimination for the Lorenz system and the Hénon map, the Coefficient of Variation, Sample and Permutation Entropy dominate classification for ECG signals, and the Condition Number and Variance of first derivative together with Fisher Information are most informative for the Rössler system. Across all datasets, the proposed framework achieves an average accuracy of 99% for noise presence detection, 98.4% for noise type classification, and 98.5% for noise intensity classification. These findings demonstrate that complexity metrics capture structural and statistical signatures of stochastic perturbations across a diverse set of dynamic systems.

This chapter discusses the challenges of creating an instruction set simulator (ISS) and system modeling environment for configurable, extensible processors. Processor generation technology is highly automated and is used by designers to create application-specific instruction set processors (ASIPs) targeted to specific application domains. The main challenges for creating an ISS lie in the configurable and extensible nature of the processor, which is tailored by designers at design time, not fixed in advance. Thus simulation models need to be created on the fly when designers have configured a new version of the processor. They need to be created automatically and offer good performance with a variety of use modes. In addition, multiple speed-accuracy trade-offs are necessary to support these various use modes, such as instruction-accurate, cycle-accurate, and pin-level. Furthermore, these ISS models must themselves fit into system modeling and simulation environments, both generated along with the ASIP and commercial third party ESL/EDA tools. Again, multiple use models and trade-offs in simulation scope, speed, and accuracy must be supported. Standards to ease integration and model interoperability in this area have arrived only recently and are just beginning to make life easier for model providers.

The prediction of the transient dynamic response of monolithic structural systems, as well as of heterogeneous (numerical/ physical) subsystems, decomposed by computational or physical considerations typical of hardware-in-the-loop and pseudo-dynamic tests using numerical integration, has become an accepted practice almost to the extent that such solutions in non-linear problems often are considered to be exact solutions. It is for this reason that this chapter is placed immediately at the beginning of the book.In light of the large body of literature on computational methods developed for both testing and control techniques applied to linear and non-linear systems, no attempt is made to cover this subject in greater depth. Rather the concepts upon which ad hoc computational methods rely are presented in a common framework along with a few applications.

In this paper we study a class of dynamical systems defined by Pfaffian maps. It is a sub-class of o-minimal dynamical systems which capture rich continuous dynamics and yet can be studied using finite bisimulations. The existence of finite bisimulations for o-minimal dynamical and hybrid systems has been shown by several authors; see e.g. Brihaye et al (2004), Davoren (1999), Lafferriere et al (2000). The next natural question to investigate is how the sizes of such bisimulations can be bounded. The first step in this direction was done by Korovina et al (2004) where a double exponential upper bound was shown for Pfaffian dynamical and hybrid systems. In the present paper we improve this bound to a single exponential upper bound. Moreover we show that this bound is tight in general, by exhibiting a parameterized class of systems on which the exponential bound is attained. The bounds provide a basis for designing efficient algorithms for computing bisimulations, solving reachability and motion planning problems.

The authors study the logic and synchronization characteristics of general dynamical systems called hybrid dynamical systems (HDSs). The proposed theory generalizes the notion of discrete-event dynamical systems by handling numerics as well as symbolics. This theory is supported by the programming language SIGNAL and a mathematical model of relational style. This framework makes it possible to formulate in the same way HDS programming or specification and HDS control. The core of the theory is the notion of HDS resolution, which is based on a reduction technique that maps any HDS specification program into a polynomial dynamical system on the finite field of integers modulo 3; all the algorithms are then based on the study of this dynamical system. >

This paper describes computational models of situated work in complex, heterogeneous dynamic systems that include humans, physical systems, computer agents and regulatory requirements, using aviation as the domain being examined but highlighting modeling principles generalizable to other domains. Work is defined formally here as purposeful activity acting on, and responding to, the environment as required by the situation. This work is performed by a team of automated and human agents, and involves both cognitive and physical activity. At its most atomic, work may be represented as actions that evaluate the current situation, and then act on the environment, as well as `teamwork' actions and `decision actions' whereby agents evaluate the situation to select the appropriate sets of actions (i.e. strategies). Work is thus a response to the situation, with strategies chosen in response to the physical environment, the allocation of responsibility within the team; and agent status including expertise, the demands on the agent, and resources available to the agent such as time and information. In addition, a description of the structures inherent to work is also used to organize the actions into an abstraction hierarchy: at the bottom are the resources and actions, but at higher levels of abstraction more aggregate functions provide descriptions that relate the detailed actions to the specific goals of the work. Historically, such models have been used qualitatively, but had a disconnect from computational modeling of situated behavior. Therefore, this paper details how they may be dynamically simulated to examine how work can be studied dynamically as situated within a dynamic environment driving, and responding to, agent activity.

The complexity of diagnosis tasks in large-scale, heterogeneous dynamic systems has turned the attention in this area toward model-based methods. Most model-based diagnostic systems try to find a set of failed components that can explain observed discrepancies in system behavior consistently with constraints represented by the models.Observations are usually sensor-based and discrepancies are detected by fault detection algorithms that compareexpected and observed system behavior. We have developed and field tested a real-time robust diagnostic system,which uses hierarchical, multiple-aspect models of plants. The models include the functional structure, timed failurepropagation graphs, physical component structure and component failure modes. The diagnostic reasoning applies structural and temporal constraints for the generation and validation of fault hypotheses using the predictor- corrector principle. The diagnosis is generated in real-time amid an evolving alarm scenario, and uses progressivedeepening control strategy. The robust diagnostic system has been tested and demonstrated using ECLSS modelsobtained from the Boeing Company.

This paper presents a study on Nash equilibrium seeking in noncooperative games within multi-robot systems, a topic of increasing importance in diverse sectors including civil, security, and military. Unlike conventional approaches where players can directly observe the actions of others, our method assumes limited visibility, where players can only communicate through an undirected and connected communication graph. We introduce a novel distributed control approach, integrating gradient play with a consensus protocol. This method facilitates effective Nash equilibrium seeking by leveraging information shared among neighboring robots in heterogeneous linear dynamic systems. The proposed solution employs a high-level distributed Nash equilibrium-seeking algorithm, serving as an optimal reference generator for each robot to track the Nash equilibrium, and an advanced output regulation technique, aiming to regulate the output (e.g., position) of the robots with respect to the obtained references. Theoretical analysis confirms the convergence of our algorithm through Lyapunov stability analysis. The effectiveness and practical applicability of our approach are validated through numerical simulations and empirical testing with physical robots, highlighting its efficacy and utility in real-world scenarios.

Discrete event system specification (DEVS), a formalism for modeling and simulation, has been recently applied to the engineering of CPS, which also makes the necessity of model checking for DEVS arisen. Model-transformation-based approaches were first proposed; timed or linear hybrid automata are obtained from DEVS models, and then verified using model checkers. But, due to the different semantics in state transitions, the transformation often involves manual tasks, such as abstraction and approximation. To eliminate such manual tasks, we have proposed a DEVS subclass that is less expressive, but verifiable. For reachability analysis, its models can be interpreted as transition systems and region automata (RA). Although our DEVS subclass and RA have different semantics with respect to model execution, they are equivalent in terms of reachability analysis. Thus, the reachability could be analyzed based on model checking techniques of timed automata. In many DEVS-based environments, models should be developed by programming languages. To enables the modelers to specify CPS in a visual way, we have proposed a visual modeling language based on our subclass. In our language, the behavior and structure of CPS are graphically specified by basic model diagrams and coupled model diagrams, respectively. Based on our subclass and languages, we have implemented a proof-of-concept environment that supports visual modeling of CPS as well as simulation and reachability analysis of the models. To enable the consideration of the hybrid nature and heterogeneity in CPS, DEVS subclasses, languages, verification techniques and environments will be further investigated.

Hybrid dynamical systems are those with interaction between continuous and discrete dynamics. For the analysis and control of such systems concepts and theories from either the continuous or the discrete domain are typically readapted. In this thesis the ideas from perturbation theory are readapted for approximating a hybrid system using a continuous one. To this purpose, hybrid systems that possess a two-time scale property, i.e. discrete states evolving in a fast time-scale and continuous states in a slow time-scale, are considered. Then, as in singular perturbation or averaging methods, the system is approximated by a slow continuous time system. Since the hybrid nature of the process is removed by averaging, such a procedure is referred to as dehybridization in this thesis. It is seen that fast transitions required for dehybridization correspond to fast switching in all but one of the discrete states (modes). Here, the notion of dominant mode is defined and the maximum time interval spent in the non-dominant modes is considered as the 'small' parameter which determines the quality of approximation. It is shown that in a finite time interval, the solutions of the hybrid model and the continuous averaged one stay 'close' such that the error between them goes to zero as the 'small' parameter goes to zero. To utilize the ideas of dehybridization for control purposes, a cascade control design scheme is proposed, where the inner-loop artificially creates the two-time scale behavior, while the outer-loop exponentially stabilizes the approximate continuous system. It is shown that if the origin is a common equilibrium point for all modes, then for sufficiently small values of the 'small' parameter, exponential stability of the hybrid model can be guaranteed. However, it is shown that if the origin is not an equilibrium point for some modes, then the trajectories of the hybrid model are ultimately bounded, the bound being a function of the 'small' parameter. The analysis approach used here defines the hybrid system as a perturbation of the averaged one and works along the lines of robust stability. The key technical diffierence is that though the norm of the perturbation is not small, the norm of its time integral is small. This thesis was motivated by the stick-slip drive, a friction-based micropositioning setup, which operates in two distinct modes 'stick' and 'slip'. It consists of two masses which stick together when the interfacial force is less than the Coulomb frictional force, and slips otherwise. The proposed methodology is illustrated through simulation and experimental results on the stick-slip drive.

In this paper, a novel intelligent modeling framework is introduced for modeling hybrid dynamical systems, which may contain both continuous-valued and discrete-valued dynamics and/or signals. The proposed modeling framework consists of a fuzzy relational hybrid model (FRHM) structure and an online identification algorithm. The FRHM structure has some useful properties, e.g., the ability of modeling both hard and soft switchings (even simultaneously), model transparency (in view of knowledge extraction and knowledge embedment), and possibility of connecting disjoint models through mathematical operations (to construct larger model structures). In addition, the proposed FRHM structure can be used to model a wide variety of hybrid dynamical systems, including nonlinear dynamical systems, dynamical systems with switchings or logical functions, dynamical systems with discrete-valued or qualitative inputs and/or outputs, etc. The fuzzy relational hybrid dynamical system is an important product of the proposed modeling structure, which, in this paper, has applied to modeling an asynchronous dc–dc buck converter as the case study.

This paper deals with a model builder for Hybrid Dynamic Systems (HDS), where technological components including sensors in an iconic format can be automatically dragged from a database and interconnected to produce the overall HDS, following industrial technological schemes. Once the model has been created, its consistency can be checked, and a block diagram simulation can be performed. The theory behind the software is based on Hybrid Bond Graphs (HBG), whose structural and causal properties will be exploited for the automatic generation of the diagnosis algorithms, which will be implemented online. Compared with developed programs for automated modeling of HDS, the presented methodology is valid for continuous and hybrid systems, and the HBG is used not only for block diagram simulation builders but also for the generation of Fault Detection and Isolation algorithms. The theory is illustrated by an industrial application that consists of the pneumo-electrical control of metro rolling stock.

In this work, we investigate the effect of terrain modelling on the performance of planetary rovers by evaluating various design concepts under typical rover manoeuvres. The analysis is based on implementations in CMLabs’ Vortex, a dynamics simulation environment for complex mechanical systems. Vortex can be used to model the contact of wheels under various terrain conditions. The particular focus is on motion on soft terrain, investigating the physical phenomena exhibited by such terrain, and the possibility of modelling these with the primary purpose of representing the effects of the terrain on rover behaviour. Two semi-empirical terramechanics models are used to represent the effect of soft soil. The implementation of these terramechanics models in Vortex and their adaptation to fit a multibody dynamics simulation environment are explained in detail. The effects of terrain modelling approaches on the performance of rover concepts under typical rover manoeuvres are investigated. These include: (i) stra...

This paper shows the results of the first phase of research «Structure of Integral Diagnostic System to Small and Medium Enterprise Focused by Dynamics Systems». In this phase has been designed, developed and systematized model to Pymes diagnostic, which combines different kind of continuously simulation models in each of seven functional areas to divide a productive organization such as: Quality, Financial, Environment, Marketing, Production, Human Resources and Technology. These models can understand separated de good or bad management in these functional areas. We built and integral diagnostic model to Pyme in an simulation environment, which it linking Industrial Dynamic concepts and system focused in order to support financial decisions to minimized risk of Pyme's loans and as a tool of diagnosis and business improvement. In addition, was possible to get a cumulus of characteristic, relations and rules to build knowledge base, in order to develop an Expert Diagnostic System in a future.