This article describes an implementation of real-time simulation and control in DEVS-Scheme, a knowledge-based, discrete event environment. We illustrate a methodology in which the plant, its actuators and sensors are described by discrete event models developed within the event-based control paradigm. A model of the controller is employed to validate its design in a plant/actuator/sensor experimental frame. The same model configuration is then employed for actual control operation by connecting the simulation executive, suitably modified, to a programmable controller that interfaces to the real plant/actuator/sensor system. We show how this methodology is supported by real-time interpretation of the DEVS (Discrete Event System Specification) formalism. A lower bound on the processing speed of a non-deterministic operating system relative to scheduled event times is derived which guarantees correct control timing. We show how the DEVS-based control can be distributed in a hierarchical ...

Ramaswamy, Srinivasan, B.S., University of Madras, May 1989 Master of Science, University of Southwestern Louisiana, July 1992 Doctor of Philosophy, May 1994 Ttile of Dissertation: Hierarchical Time-Extended Petri Nets (H-EPNs) for Integrated Control and Diagnostics of Multilevel Systems Dissertation Directed by Drs. Kimon P. Valavanis & Padmini Srinivasan Pages in Dissertation, 157; Words in Abstract 300 The objective of this research is to develop a model-based approach to the modeling, analysis and design of multilevel systems. Petri Nets are used as the basic modeling tool because of their versatility in the modeling and analysis of dynamic system behavior. Moreover, Petri Nets have a strong mathematical background and Petri Net analysis techniques are well established. Hierarchical Time-Extended Petri Nets (H-EPNs) are proposed as a hybrid modeling and analysis tool for the derivation of the coordination level model [205, 231] of hierarchically decomposable systems, viewe...

CIRSSE REPORT #134-L_

Discrete part manufacturing flows from a design phase in which product information is defined to a manufacturing phase in which the processes for machined parts and tooling are planned and executed. Process planning typically culminates with the generation of numerical control (NC) programs for specific equipment, such as machining centers or turning centers. These NC programs are written in the dialects of the various equipment vendors, for the specific mechanical configuration of the target machine. As a result, porting programs between machines is difficult. Worse, NC programs contain little if any of the product design information. The lack of this information at run time limits any adaptive control that could direct the process so that final parts more closely conform to the original design.

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Simulator : : : : : : : : : : : : : : : : : : : : : : : : : 87 Appendix E. Examples of Human Performance Process Hierarchical Decomposition 92 Appendix F. Scalable Coherent Interfaces 96 Contents (continued) Chapter Page Appendix G. Synopses of Selected High Performance Parallel Machines 98 Appendix H. Glossary of Acronyms 102 References 105 List of Figures Figure Page 1.1 A Holarchy : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 17 2.1 Possible Paths for Human Performance Process Model Creation : : : : : : : 21 6.1 Numerical Aerodynamics Simulation Results for Embarassingly Parallel Benchmarks : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 40 6.2 CM2: Numerical Aerodynamics Simulation Benchmark Results : : : : : : : 41 6.3 Human Performance Process and Architectures : : : : : : : : : : : : : : : : 42 8.1 Heterogeneous Computing Environment : : : : : : : : : : : : : : : : : : : : 50 9.1 High Performance Systems Metrics : : :...

The NIST robot vehicle, a HMMWV with sensors and computer controlled actuators, detects and avoids obstacles while it drives off road at speeds up to 35 km/h. During tests the vehicle drives through the back fields of NIST detecting large obstacles up to 50 m away. Obstacles are sensed using a 30x60 field of view laser range scanner. The planner computes smooth, obstacle free paths that follow an operators commanded path.