Coverage in fault-tolerant control

Wu, N. Eva

doi:10.1016/j.automatica.2003.11.015

Cited by 80 publications

(43 citation statements)

References 15 publications

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“…In an unsupervised environment, c i = 1 for the first transmission attempt, and c i = 0 for any re-transmissions. In a supervised environment, on the other hand, 0 < c i < 1 in general (Wu, 2004). The factors affecting c i include lack of observability of state, or erroneous state estimation, failure of a supervising node or cluster, fading channel linking the supervising cluster and cluster i, and collision among packets in which case a more elaborate queuing network model becomes appropriate.…”

Section: A Motivating Examplementioning

confidence: 99%

Control Reconfiguration of Command and Control Systems

Wu¹

2007

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Washington Headquarters Service, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC 20503. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) JAN 20072. REPORT TYPE Final DATES COVERED (From -To CS PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)The SPONSOR/MONITOR'S ACRONYM(S) 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)AFRL/IFSB 525 Brooks Rd Rome NY 13441-4505 SPONSORING/MONITORING AGENCY REPORT NUMBER AFRL-IF-RS-TR-2007-20 DISTRIBUTION AVAILABILITY STATEMENT APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. PA# 07-026 SUPPLEMENTARY NOTES ABSTRACTCurrently, an air operation center (AOC) for a major regional conflict is composed of more than 400 personnel, hundreds of computer servers and an extensive communication infrastructure. So, in addition to the goals of achieving a faster, more real time response, there is also a desire to reduce the manning and equipment associated with the endeavor. This means that the management of redundancy must be optimized. An important consideration in the design and fielding of such systems is its capacity to accommodate faults through control reconfiguration using either direct or analytic redundancy. The latter relies on exploiting the inherent dynamic and static relationship of the system variables, and having the advantage of most efficient use of the components. This research applied the concepts of control reconfigurability to C2 systems modeled as stochastic discrete event systems. SUBJECT TERMS Executive SummaryCurrently, an air operation center (AOC) for a major regional conflict is composed of more than 400 personnel, hundreds of computer servers and an extensive communication infrastructure. So in addition to the goals of achieving a faster, more real time response, there is also a desire to reduce the manning and equipment associated with the endeavor. This means that the management of redundancy must be optimized. An important consideration in the design and fielding of such systems is its capacity to accommodate faults through control reconfiguration using either direct or analytic redundancy. The latter relies on exploiting the inherent dynamic and static relationship of the system variables, and having the advantage of most efficient use of the components. The proposed research seeks to apply the concepts of control reconfigurability to C2 systems modeled as stochastic discrete event systems.This report contains ten self-conta...

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Section: A Motivating Examplementioning

confidence: 99%

Control Reconfiguration of Command and Control Systems

Wu¹

2007

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“…1 This problem considers the design of a lateral-directional axis controller during powered approach to a carrier landing with two command inputs from the pilot: lateral stick and rudder pedal. At an angle-of-attack of 10.5 degrees and airspeed of 140 knots, the nominal linearized F-14 model has four states: lateral velocity, yaw rate, roll rate, and roll angle, denoted by v, r, p, and φ, respectively, two control inputs: differential stabilizer deflection and rudder deflection, denoted by δ dstab and δ rud , respectively, and four outputs: roll rate, yaw rate, lateral acceleration, and side-slip angle, denoted by p, r, y ac , and β, respectively.…”

Section: Model Descriptionmentioning

confidence: 99%

“…As fault occurrences and system failures are rare events, dynamic models are usually not suitable for reliability analysis. For example, Wu used serial-parallel block diagrams and Markov models for evaluation purpose, and defined a coverage concept to relate reliability and control actions [1]. Walker proposed Markov and semi-Markov models to describe the transitions of fault and FDI modes, but control actions are not considered [2].…”

Section: Introductionmentioning

confidence: 99%

Reliability Monitoring of Fault Tolerant Control Systems with Demonstration on an Aircraft Model

Zhao

Yang

2007

Journal of Control Science and Engineering

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This paper proposes a reliability monitoring scheme for active fault tolerant control systems using a stochastic modeling method. The reliability index is defined based on system dynamical responses and a safety region; the plant and controller are assumed to have a multiple regime model structure, and a semi-Markov model is built for reliability evaluation based on the safety behavior of each regime model estimated by using Monte Carlo simulation. Moreover, the history data of fault detection and isolation decisions is used to update its transition characteristics and reliability model. This method provides an up-to-date reliability index as demonstrated on an aircraft model.

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“…An ongoing research contribution is made by Wu (Wu, 2001;2004;Wu and Patton, 2003). In her latest results, reliability was evaluated from a Markov process model built from serial-parallel block diagrams which describe functional relations among subsystems and components.…”

Section: Introductionmentioning

confidence: 99%

Reliability Modeling of Fault Tolerant Control Systems

Li¹,

Zhao²,

Yang³

2007

International Journal of Applied Mathematics and Computer Science

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This paper proposes a novel approach to reliability evaluation for active Fault Tolerant Control Systems (FTCSs). By introducing a reliability index based on the control performance and hard deadline, a semi-Markov process model is proposed to describe system operation for reliability evaluation. The degraded performance of FTCSs in the presence of imperfect Fault Detection and Isolation (FDI) is reflected by semi-Markov states. The semi-Markov kernel, the key parameter of the process, is determined by four probabilistic parameters based on the Markovian model of FTCSs. Computed from the transition probabilities of the semi-Markov process, the reliability index incorporates control objectives, hard deadline, and the effects of imperfect FDI, a suitable quantitative measure of the overall performance.

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Coverage in fault-tolerant control

Cited by 80 publications

References 15 publications

Control Reconfiguration of Command and Control Systems

Control Reconfiguration of Command and Control Systems

Reliability Monitoring of Fault Tolerant Control Systems with Demonstration on an Aircraft Model

Reliability Modeling of Fault Tolerant Control Systems

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