State-feedback Models

State-Feedback Models is a dynamic modeling framework within adaptive management that uses real-time system state observations to iteratively update management actions, distinguishing itself from other models by its explicit reliance on feedback control principles to reduce uncertainty and improve decision-making over time.

125

Publications

9.2K

Citations

281

Authors

161

Institutions

Publications per year

2017–2026

25

Authors

281

  1. AA
    Ari Arapostathis

    The University of Texas at Austin, United States

    3 publications · 96 citations

    3

    h-index

  2. PP
    Pramod P. Khargonekar

    University of Michigan, United States

    3 publications · 211 citations

    3

    h-index

  3. MA
    Mario A. Rotea

    The University of Texas at Dallas, United States

    3 publications · 211 citations

    3

    h-index

  4. MB
    Mark Burgess

    OsloMet – Oslo Metropolitan University, Norway

    2 publications · 32 citations

    2

    h-index

  5. HZ
    Huaguang Zhang

    Northeastern University, China

    2 publications · 40 citations

    2

    h-index

1–5 of 281

Venues

  1. IEEE Transactions on Automatic Control

    21 publications · 3.6K citations

    18

    h-index

  2. Automatica

    7 publications · 1.4K citations

    7

    h-index

  3. International Journal of Control

    4 publications · 110 citations

    4

    h-index

  4. Systems & Control Letters

    4 publications · 204 citations

    4

    h-index

  5. International Journal of Systems Science

    3 publications · 74 citations

    3

    h-index

1–5