Showing papers by "Bernard P. Zeigler published in 2005"

Variable Structure in DEVS Component-Based Modeling and Simulation

Abstract: Variable structure refers to the ability of a system to dynamically change its structure according to different situations. It provides component-based modeling and simulation environments with powerful modeling capability and the flexibility to design and analyze complex systems. In this article, the authors discuss variable structure--specifically, the structure change and interface change capability--in DEVS-based modeling and simulation environments. The operations of structure change and interface change are discussed, and their respective operation boundaries are defined. Three examples are given to illustrate the role of variable structure and how it can be used to model and design adaptive complex systems. Principles for the implementation of variable structure are also presented and illustrated in the DEVSJAVA modeling and simulation environment.

Model continuity in the design of dynamic distributed real-time systems

Abstract: Model continuity refers to the ability to transition as much as possible a model specification through the stages of a development process. In this paper, the authors show how a modeling and simulation environment, based on the discrete event system specification formalism, can support model continuity in the design of dynamic distributed real-time systems. In designing such systems, the authors restrict such continuity to the models that implement the system's real-time control and dynamic reconfiguration. The proposed methodology supports systematic modeling of dynamic systems and adopts simulation-based tests for distributed real-time software. Model continuity is emphasized during the entire process of software development $the control models of a dynamic distributed real-time system can be designed, analyzed, and tested by simulation methods, and then smoothly transitioned from simulation to distributed execution. A dynamic team formation distributed robotic system is presented as an example to show how model continuity methodology effectively manages the complexity of developing and testing the control software for this system.

Discrete event multi-level models for systems biology

Abstract: Diverse modeling and simulation methods are being applied in the area of Systems Biology. Most models in Systems Biology can easily be located within the space that is spanned by three dimensions of modeling: continuous and discrete; quantitative and qualitative; stochastic and deterministic. These dimensions are not entirely independent nor are they exclusive. Many modeling approaches are hybrid as they combine continuous and discrete, quantitative and qualitative, stochastic and deterministic aspects. Another important aspect for the distinction of modeling approaches is at which level a model describes a system: is it at the “macro” level, at the “micro” level, or at multiple levels of organization. Although multi-level models can be located anywhere in the space spanned by the three dimensions of modeling and simulation, clustering tendencies can be observed whose implications are discussed and illustrated by moving from a continuous, deterministic quantitative macro model to a stochastic discrete-event semi-quantitative multi-level model.

Enhancing DoDAF with a DEVS-based system lifecycle development process

Abstract: A recent DoD mandate requires that the DoD architectural framework (DoDAF) be adopted to express high level system and operational requirements and architectures. DoDAF is the basis for integrated architectures and provides broad levels of specification related to operational, system, and technical views. The combination of DoDAF operational views, which capture the requirements of the architecture, and system views which provide its technical attributes, forms the basis for semi-automated construction of the needed simulation models. In this paper, we describe an approach to support specification of DoDAF architectures within a development environment based on DEVS (discrete event system specification). The result is an enhanced system lifecycle development process that includes both development and testing in an integral manner. This paper discusses the motivation to carve out a methodology to develop DEVS models for any DoDAF-UML architecture specifications and empower DoDAF with integrated M&S.

A simulation-based virtual environment to study cooperative robotic systems

Abstract: Simulation plays important roles in experimenting with, understanding, and evaluating the performance of cooperative robotic systems. Typically, simulation-based studies of robotic systems are conducted on the computer, without involving any real system components. This paper presents a new hybrid approach to simulation that allows real robots as well as robot models to work together in a simulation-based virtual environment. This capability of robot-in-the-loop simulation effectively bridges conventional simulation and real system execution, augmenting them to constitute an incremental study and measurement process. It is especially useful for large-scale cooperative robotic systems whose complexity and scalability severely limit experimentations in a physical environment using real robots. We present the architecture of this simulation-based virtual environment that, together with an incremental study process and associated experimental frames, can support systematic analysis of cooperative robotic systems. An example of robotic convoy is provided. Some measurement metrics are developed and simulation results are described. An experimental setup for robot-in-the-loop simulation is discussed.

Discrete event simulation of large-scale spatial continuous systems

Abstract: Complex spatially-extended systems consist of numerous sub-systems leading to large simulation execution times. One approach to reducing these execution times is designing a simulation engine to allocate its attention to subsystems in proportion to their activity levels. In this paper, we consider a large scale simulation of a physics-based fire spread model. This model is discretized using a recently developed numerical method called quantization and implemented using discrete event simulation. In this paper, we provide comparisons between the quantization method and usual Euler discrete-time methods. The aim is to demonstrate the ability of quantization and discrete event simulation to focus on active sub-systems, thus significantly reducing execution time for large heterogeneous systems.

Robots in the loop: supporting an incremental simulation-based design process

Abstract: This paper presents the results of applying an incremental simulation-based design process to study a robotic convoy system. Robot-in-the-loop simulation, as a major step in this process, allows the system to be measured with combined robot models and real robots. This capability effectively bridges the gap between conventional simulation where models are used and real system experiment where real robots are used. For each step in this incremental process, the simulation/experiment setup is described. The measurement data are then presented and compared. These experiments and results demonstrate the capabilities of robot-in-the-loop simulation and justify the effectiveness of using the incremental simulation-based design process.

Dynamic Multiplexing and High-Performance Modeling in Distributed Simulation

Abstract: This article reviews existing message traffic reduction schemes and proposes a dynamic multiplexing approach as an efficient message traffic reduction scheme. This approach is based on a dynamic encoding of the joint output of sender components into a single message and is applicable to a model involving distributed components moving and interacting in multidimensional space by combining with predictive interest-based quantization. This article applies this approach to a projectile/missile application with realistic multidimensional dynamics. The authors investigate the dependence of simulation accuracy and required network bandwidth on the number of active components and basic time granule. Analysis and empirical data clearly indicate the advantages of dynamic multiplexing in saving communication and computation resources in a large-scale distributed system.