Active State Model for Autonomous Systems
- Wednesday, 31 January 2007
Autonomous systems would be able to diagnose themselves and respond accordingly.
The concept of the active state model (ASM) is an architecture for the development of advanced integrated fault-detection- and-isolation (FDI) systems for robotic land vehicles, pilotless aircraft, exploratory spacecraft, or other complex engineering systems that will be capable of autonomous operation. An FDI system based on the ASM concept would not only provide traditional diagnostic capabilities, but also integrate the FDI system under a unified framework and provide mechanism for sharing of information between FDI subsystems to fully assess the overall "health" of the system.
The ASM concept begins with definitions borrowed from psychology, wherein a system is regarded as active when it possesses self-image, self-awareness, and an ability to make decisions itself, such that it is able to perform purposeful motions and other transitions with some degree of autonomy from the environment. For an engineering system, self-image would manifest itself as the ability to determine nominal values of sensor data by use of a mathematical model of itself, and self-awareness would manifest itself as the ability to relate sensor data to their nominal values. The ASM for such a system may start with the closed-loop control dynamics that describe the evolution of state variables. As soon as this model was supplemented with nominal values of sensor data, it would possess self-image. The ability to process the current sensor data and compare them with the nominal values would represent self-awareness. On the basis of self-image and self-awareness, the ASM provides the capability for self-identification, detection of abnormalities, and self-diagnosis.
In our practical implementation of the ASM, we use the "gray-box" approach to implementing self-image and self- awareness (see figure). The "gray-box" approach differs from the "black-box" and "white-box" approaches in the following way: It involves the use of mathematical models that are characterized as being of a mixed-signal or "gray" type, meaning that they include both deterministic and stochastic models. The deterministic or the "white" model is used to filter out what is known about the system. What is left after filtering is the residual, or unknown, components of information on the system. The residual information is mathematically modeled by use of stochastic techniques, i.e., "black-box." The behavior or "health" of the system can then be monitored by comparing the residual against its nominal values through the stochastic model. The advantages of the gray-box approach are that (1) it maximizes the use of both sensory information and any information previously available in the form of a mathematical model and (2) offers both sensitivity to truly functional damage and insensitivity to mere operational disturbances.
Another essential component of the ASM is the active system exchange (ASE), which includes a flexible database that stores all relevant FDI and other information to synthesize data and data-driven models sufficient for intelligent decision-making. The ASE would provide crucial information from all FDI subsystems and would allow an intelligent agent such as planning software to make decisions based on summarized understanding of the system health. When the full ASM architecture is implemented, it will provide a framework for a fully autonomous system that would be able to monitor and predict equipment failures, reconfigure the control subsystem of the system in response to an equipment failure, take appropriate action in response to unexpected events, and replan the mission of the system, as needed, in real time.
This work was done by Han Park, Steve Chien, Michail Zak, Mark James, Ryan Mackey, and Forest Fisher of Caltech for NASA's Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free on-line at www.techbriefs.com/tsp under the Information Sciences category. NPO-21243.
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