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Prognostics in the Control Loop

Metadata Updated: December 7, 2023

The term Automated Contingency Management (ACM) has been used to describe intelligent systems capable of mission re-planning and control reconfiguration in the presence of a current health state diagnosis. While a diagnostics driven ACM capability designed to optimize multi-objective performance criteria remains a significant technical challenge, it cannot hope to overcome the fact that it will always be a reactive paradigm. This paper, therefore, introduces an automated contingency management paradigm based on both current heath state (diagnosis) and future health state estimates (prognosis). Including Prognostics in the control loop poses at least two additional challenges to ACM. First, future state prediction will, in general, have uncertainty that increases as the prediction horizon increases so adaptive prognosis routines that manage uncertainty are critical. Secondly, a warning period afforded by prognosis allows ACM to be split into a real- time “reactive” component and a non-real time “planning” component that considers temporal parameters and the potential impact of being proactive with mitigating action. The proactive ACM paradigm was developed and evaluated in the context of a generic mono-propellant system model in Simulink/Stateflow with diagnostics, prognostics and an optimal reconfigurable control system. Applications of Artificial Intelligence (AI) technologies in prognostics enhanced ACM system are briefly discussed. Preliminary results from the on-going research work are presented and the paper is concluded with remarks on future work.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date November 12, 2020
Metadata Updated Date December 7, 2023
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date December 7, 2023
Publisher Dashlink
Identifier DASHLINK_745
Data First Published 2013-05-13
Data Last Modified 2020-01-29
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id f536d8fa-9c91-4e9d-bfba-995cd7b56297
Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL
Program Code 026:029
Source Datajson Identifier True
Source Hash a0ea468756082e7084518601388e71c4a291171fb1af75bad7ec549a15f0fdc6
Source Schema Version 1.1

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