Model predictive sensor scheduling

Eriko Iwasa, Kenko Uchida

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    The sensor scheduling is to select a sensor (or a group of sensors) from multiple sensors at each time step so as to perform optimally a task based on the sensed data. In this paper, we pose a model predictive type deterministic/stochastic sensor scheduling problem for discrete-time linear Gaussian time-varying systems, and develop an approach to solve these problems based on the dynamic programming recursion. We show first that, in a special case of deterministic scheduling where the Riccati recursion of error covariance satisfies a specific structural condition, the online optimization using the dynamic programming is reduced to a static optimization, so that the model predictive sensor scheduling can be easily implemented online. Next, we discuss the stochastic scheduling problem, and show an alternative condition of optimization reduction, which lead to a stochastic sensor scheduling easily implemented online. Finally, we propose two practical sensor schedulings for deterministic and stochastic case, and discuss an example to illustrate the two sensor schedulings.

    Original languageEnglish
    Title of host publication2008 International Conference on Control, Automation and Systems, ICCAS 2008
    Pages2260-2265
    Number of pages6
    DOIs
    Publication statusPublished - 2008
    Event2008 International Conference on Control, Automation and Systems, ICCAS 2008 - Seoul
    Duration: 2008 Oct 142008 Oct 17

    Other

    Other2008 International Conference on Control, Automation and Systems, ICCAS 2008
    CitySeoul
    Period08/10/1408/10/17

    Keywords

    • Optimization
    • Predictive control
    • Sensor scheduling

    ASJC Scopus subject areas

    • Control and Systems Engineering

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  • Cite this

    Iwasa, E., & Uchida, K. (2008). Model predictive sensor scheduling. In 2008 International Conference on Control, Automation and Systems, ICCAS 2008 (pp. 2260-2265). [4694184] https://doi.org/10.1109/ICCAS.2008.4694184