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Dvorak, Daniel L.

Publications and source records attributed to Dvorak, Daniel L..

9 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Ontology and modeling patterns for state-based behavior representation

This paper provides an approach to capture state-based behavior of elements, that is, the specification of their state evolution in time, and the interactions amongst them. Elements can be components (e.g., sensors, actuators) or environments, and are characterized by state variables that vary with time. The behaviors of these elements, as well as interactions among them are represented through constraints on state variables. This paper discusses the concepts and relationships introduced in this behavior ontology, and the modeling patterns associated with it. Two example cases are provided to illustrate their usage, as well as to demonstrate the flexibility and scalability of the behavior ontology: a simple flashlight electrical model and a more complex spacecraft model involving instruments, power and data behaviors. Finally, an implementation in a SysML profile is provided.

state variable↗
NASA NTRS2013

Two Mechanisms to Avoid Control Conflicts Resulting from Uncoordinated Intent

This software implements a real-time access control protocol that is intended to make all connected users aware of the presence of other connected users, and which of them is currently in control of the system. Here, "in control" means that a single user is authorized and enabled to issue instructions to the system. The software The software also implements a goal scheduling mechanism that can detect situations where plans for the operation of a target system proposed by different users overlap and interact in conflicting ways. In such situations, the system can either simply report the conflict (rejecting one goal or the entire plan), or reschedule the goals in a way that does not conflict. The access control mechanism (and associated control protocol) is unique. Other access control mechanisms are generally intended to authenticate users, or exclude unauthorized access. This software does neither, and would likely depend on having some other mechanism to support those requirements.

Mishkin, Andrew H.↗
NASA NTRS2012

Real-Time Projection to Verify Plan Success During Execution

The Mission Data System provides a framework for modeling complex systems in terms of system behaviors and goals that express intent. Complex activity plans can be represented as goal networks that express the coordination of goals on different state variables of the system. Real-time projection extends the ability of this system to verify plan achievability (all goals can be satisfied over the entire plan) into the execution domain so that the system is able to continuously re-verify a plan as it is executed, and as the states of the system change in response to goals and the environment. Previous versions were able to detect and respond to goal violations when they actually occur during execution. This new capability enables the prediction of future goal failures; specifically, goals that were previously found to be achievable but are no longer achievable due to unanticipated faults or environmental conditions. Early detection of such situations enables operators or an autonomous fault response capability to deal with the problem at a point that maximizes the available options. For example, this system has been applied to the problem of managing battery energy on a lunar rover as it is used to explore the Moon. Astronauts drive the rover to waypoints and conduct science observations according to a plan that is scheduled and verified to be achievable with the energy resources available. As the astronauts execute this plan, the system uses this new capability to continuously re-verify the plan as energy is consumed to ensure that the battery will never be depleted below safe levels across the entire plan.

Wagner, David A.↗
NASA NTRS2009

Combinatorial Generation of Test Suites

Testgen is a computer program that generates suites of input and configuration vectors for testing other software or software/hardware systems. As systems become ever more complex, often, there is not enough time to test systems against all possible combinations of inputs and configurations, so test engineers need to be selective in formulating test plans. Testgen helps to satisfy this need: In response to a test-suite-requirement-specification model, it generates a minimal set of test vectors that satisfies all the requirements.

Dvorak, Daniel L.↗
NASA NTRS2005

A unifying framework for systems modeling, control systems design, and system operation

Current engineering practice in the analysis and design of large-scale multi-disciplinary control systems is typified by some form of decomposition- whether functional or physical or discipline-based-that enables multiple teams to work in parallel and in relative isolation. Too often, the resulting system after integration is an awkward marriage of different control and data mechanisms with poor end-to-end accountability. System of systems engineering, which faces this problem on a large scale, cries out for a unifying framework to guide analysis, design, and operation. This paper describes such a framework based on a state-, model-, and goal-based architecture for semi-autonomous control systems that guides analysis and modeling, shapes control system software design, and directly specifies operational intent. This paper illustrates the key concepts in the context of a large-scale, concurrent, globally distributed system of systems: NASA's proposed Array-based Deep Space Network.

systems modeling↗
NASA NTRS2004

Hard Real-Time: C++ Versus RTSJ

In the domain of hard real-time systems, which language is better: C++ or the Real-Time Specification for Java (RTSJ)? Although ordinary Java provides a more productive programming environment than C++ due to its automatic memory management, that benefit does not apply to RTSJ when using NoHeapRealtimeThread and non-heap memory areas. As a result, RTSJ programmers must manage non-heap memory explicitly. While that's not a deterrent for veteran real-time programmers-where explicit memory management is common-the lack of certain language features in RTSJ (and Java) makes that manual memory management harder to accomplish safely than in C++. This paper illustrates the problem for practitioners in the context of moving data and managing memory in a real-time producer/consumer pattern. The relative ease of implementation and safety of the C++ programming model suggests that RTSJ has a struggle ahead in the domain of hard real-time applications, despite its other attractive features.

programming model↗
NASA NTRS2004

Adaptable state based control system

An autonomous controller, comprised of a state knowledge manager, a control executor, hardware proxies and a statistical estimator collaborates with a goal elaborator, with which it shares common models of the behavior of the system and the controller. The elaborator uses the common models to generate from temporally indeterminate sets of goals, executable goals to be executed by the controller. The controller may be updated to operate in a different system or environment than that for which it was originally designed by the replacement of shared statistical models and by the instantiation of a new set of state variable objects derived from a state variable class. The adaptation of the controller does not require substantial modification of the goal elaborator for its application to the new system or environment.

Rasmussen, Robert D.↗
NASA NTRS1997

Infusion of Autonomy Technology into Space Missions: DS1 Lessons Learned

The impact of infusing breakthrough autonomy technology into a flight project was a big surprise. Valuable technical and cultural lessons, many of general applicability when intorducing system-level autonomy, have been learned by infusing the Remote Agent (RA) into NASA's Deep Space 1 (DS1) Spacecraft.

Autonomy↗