Significant research efforts have been devoted to large-scale dynamical systems, with the aim of understanding their complicated behaviors and managing their responses in real-time. One pivotal technological obstacle in this process is the existence of uncertainty. Although many of these large-scale dynamical systems function well in the design stage, they may easily fail when operating in realistic environment, where environmental uncertainties modulate system dynamics and complicate real-time predication and management tasks. This dissertation aims to develop systematic methodologies to evaluate the performance of large-scale dynamical systems under uncertainty, as a step toward real-time decision support. Two uncertainty evaluation approaches are pursued: the analytical approach and the effective simulation approach. The analytical approach abstracts the dynamics of original stochastic systems, and develops tractable analysis (e.g., jump-linear analysis) for the approximated systems. Despite the potential bias introduced in the approximation process, the analytical approach provides rich insights valuable for evaluating and managing the performance of large-scale dynamical systems under uncertainty. When a system’s complexity and scale are beyond tractable analysis, the effective simulation approach becomes very useful. The effective simulation approach aims to use a few smartly selected simulations to quickly evaluate a complex system’s statistical performance. This approach was originally developed to evaluate a single uncertain variable. This dissertation extends the approach to be scalable and effective for evaluating large-scale systems under a large-number of uncertain variables. While a large portion of this dissertation focuses on the development of generic methods and theoretical analysis that are applicable to broad large-scale dynamical systems, many results are illustrated through a representative large-scale system application on strategic air traffic management application, which is concerned with designing robust management plans subject to a wide range of weather possibilities at 2-15 hours look-ahead time.
17Hybrid decentralized maximum entropy control for large-scale dynamical systems
Hybrid decentralized maximum entropy control for large-scale dynamical systems
Optimized Implementation of Dijkstra’s Algorithm for Efficient Shortest Path Finding in Networked Systems
Dijkstra’s Algorithm, a popular answer based graph-based fully objective shortcut search, plays an important role in various communication systems and optimization problems These rules are important in packages of IoT networks, smart website online visitors management, and telecommunications, of which green-tracking and real-time priority-making are paramount In this paper, we discover now not most effective the classical software of Dijkstra’s Algorithm but additionally introduce optimizations that beautify its performance in largescale dynamic systems. [1] By tailoring the algorithm for particular contexts, including real-time records streams in IoT or adaptive routing in clever towns, we reveal sizeable enhancements in each computational overall performance and aid allocation. Our outcomes highlight the set of rules’s capability to evolve to modern challenges, ensuring quicker, greater accurate decisionmaking throughout numerous industries. The proposed upgrades open the door to further packages in future shrewd networks, offering solutions that might pressure improvements in self sufficient structures and scalable infrastructures. [2]
Read more7Connective stability of a kind of singular nonlinear large-scale dynamical systems
It is important content that the studying connective stability among the stability studying of the large-scale interconnected systems. The many results recently have been given for the normal systems, the studying result of the singular large scale systems, however, is a little. The paper discussed the connective stability of a kind of nonlinear singular large-scale dynamical systems by means of singular Lyapunov equation and vector Lyapunov function. A stable domain of connective parameters is given in the paper which is simple in form. At last we give an example to show the usage of the method and its efficiency.
Read more4Mean-square practical stability of stochastic large-scale dynamical systems
Mean-square practical stability for stochastic large-scale dynamical systems is studied. The vector Lyapunov functions and the basic comparison principle of stochastic systems as well as the decomposition-aggregation method for large-scale systems were used to obtain results for various types of mean-square practical stability of nonlinear stochastic large-scale dynamical systems. The objective is to analyze stochastic large-scale systems in terms of their lower-order subsystems and in terms of their interconnecting structure. These results make it possible to determine the mean-square practical stability of stochastic large-scale dynamical systems by testing the corresponding practical stability of the auxiliary deterministic systems. >
Read more1Computing in Large-Scale Dynamic Systems
Computing in Large-Scale Dynamic Systems
2Sufficient conditions for robust stability of large-scale dynamical systems including delayed states and parameter perturbations in interconnections
The problem of the robust stability of large-scale dynamical systems including delayed states and parameter perturbations in interconnections is considered. By using algebraic Riccati equations and some analytical methods, some sufficient conditions on linear decentralized state feedback controllers are derived so that the systems remain stable in the presence of delayed states and parameter perturbations. Such conditions give some bounds on the perturbations of interconnections with delayed states and uncertain parameters, and result in a quantitative measures of robustness for large-scale dynamical systems including delayed states and uncertain parameters in interconnections. The results obtained in this paper are applicable not only to large-scale systems with multiple time-varying delays, but also to large-scale systems without exact knowledge of the delays, i.e., large-scale systems with uncertain delays.
Read more15Model-order reduction of large-scale second-order MIMO dynamical systems via a block second-order Arnoldi method
In this paper, we present a structure-preserving model-order reduction method for solving large-scale second-order MIMO dynamical systems. It is a projection method based on a block second-order Krylov subspace. We use the block second-order Arnoldi (BSOAR) method to generate an orthonormal basis of the projection subspace. The reduced system preserves the second-order structure of the original system. Some theoretical results are given. Numerical experiments report the effectiveness of this method.
Read more24Control vector Lyapunov functions for large-scale impulsive dynamical systems
Control vector Lyapunov functions for large-scale impulsive dynamical systems
2Revisiting Backoff algorithms in CSMA/CA based MAC for channel reservation in RFID reader networks through broadcasting
Mitigating reader-to-reader collisions is one of the principal challenges in a large-scale dynamic RFID system with a number of readers deployed in order to maximize the system performance (i.e., throughput, fairness and latency). In prior works, contention-based and activity scheduling medium access control (MAC) protocols are commonly used approaches to reduce such problems. Existing protocols typically perform worse in a large-scale RFID dynamic system and require more additional components or are based on unrealistic assumptions. So far, many research efforts have been made to improve the performance or the reliability of Carrier Sense Multiple Access (CSMA) techniques for Mobile Ad-Hoc Networks (MANETs) by using an adaptive Backoff schemes. In this paper, we look at these well known solutions that proved their efficiency in high congestion wireless networks. We evaluate the performance and characterize these solutions when they are used to reserve the wireless channel through broadcasting message for reader-to-tag communication. Based on the application requirements, we study their capacity to mitigate collisions, the channel access latency, the average number of successful requests sent per reader and the fairness index in the context of RFID networks.
Read more3Towards Component-Based Design of Safety-Critical Cyber-Physical Applications
Cyber-physical systems typically involve a large number of mobile autonomous devices that closely interact with each other and their environment. Standard design and development techniques from the embedded domain fail to accurately model the dynamics of such systems and, hence, there is an increasing need for new programming models and abstractions. Component-based design approaches are a promising solution to manage the complexity of large-scale dynamic systems. However, existing such approaches either do not accurately model transitory interactions between components -- which are typical of cyber-physical systems -- or do not provide guarantees for real-time behavior which is essential in many safety-critical applications. To overcome this problem, in this paper, we present a component-based design technique based on DEECo (Dependable Emergent Ensembles of Components). The DEECo framework allows modeling large-scale dynamic systems by a set of interacting components. In contrast to other component-based design approaches from the literature, DEECo provides mechanisms to describe transitory interactions between components. We introduce necessary extensions to the DEECo design flow and integrate it with real-time analysis techniques that allow reasoning about timing behavior at the component-description level. Finally, we illustrate the simplicity and usefulness of our approach on a case study consisting of an intelligent crossroad system.
Read more3A quality of service framework for dependability in large-scale distributed systems
As recognition grows within industry for the advantages that can be gained through the exploitation of large-scale dynamic systems, a need emerges for dependable performance. Future systems are being developed with a requirement to support mission critical and safety critical applications. These levels of criticality require predictable performance and as such have traditionally not been associated with adaptive systems. The software architecture proposed for such systems takes its properties from the service-oriented computing paradigm and the communication model follows a publish/subscribe approach. While adaptive, such architectures do not, however, typically support real-time levels of performance. There is scope, however, for dependability within such architectures through the use of Quality of Service (QoS) methods. QoS is used in systems where the distribution of resources cannot be decided at design time. In this paper a QoS based framework is proposed for providing adaptive and dependable behaviour for future large-scale dynamic systems through the flexible allocation of resources. Simulation results are presented to demonstrate the benefits of the QoS framework and the tradeoffs that occur between negotiation algorithms of varying complexities.
Read more6Thermodynamic modeling, energy equipartition, and nonconservation of entropy for discrete-time dynamical systems
In this paper we develop thermodynamic models for discrete-time large-scale dynamical systems. Specifically, using compartmental dynamical system theory, we develop energy flow models possessing energy conservation, energy equipartition, temperature equipartition, and entropy nonconservation principles for discrete-time, large-scale dynamical systems. Furthermore, we introduce a new and dual notion to entropy, namely, ectropy, as a measure of the tendency of a dynamical system to do useful work and grow more organized, and show that conservation of energy in an isolated thermodynamic system necessarily leads to nonconservation of ectropy and entropy. In addition, using the system ectropy as a Lyapunov function candidate we show that our discrete-time, large-scale thermodynamic energy flow model has convergent trajectories to Lyapunov stable equilibria determined by the system initial subsystem energies.
Read more282Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
5Synthesis of Linear Controllers Using Pairwise Autonomous Subsystem Approach
Synthesis of Linear Controllers Using Pairwise Autonomous Subsystem Approach
137An overview of approximation methods for large-scale dynamical systems
An overview of approximation methods for large-scale dynamical systems
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