Nonlinear Model Predictive Control

Nonlinear Model Predictive Control is a model-based control strategy that generalizes predictive control to nonlinear systems by explicitly incorporating nonlinear dynamic models within a receding horizon optimization framework to compute control actions that minimize a nonlinear cost function subject to nonlinear constraints.

2.6K

Publications

143.9K

Citations

5.4K

Authors

1.3K

Institutions

Publications per year

2017–2026

1.2K

Authors

5.4K

  1. FA
    Frank Allgöwer

    University of Stuttgart, Germany

    58 publications · 6K citations

    32

    h-index

  2. MD
    Moritz Diehl

    KU Leuven, Belgium

    71 publications · 3.8K citations

    31

    h-index

  3. RF
    Rolf Findeisen

    Otto-von-Guericke-Universität Magdeburg, Germany

    35 publications · 3.4K citations

    24

    h-index

  4. LT
    Lorenz T. Biegler

    Carnegie Mellon University, United States

    36 publications · 1.5K citations

    22

    h-index

  5. PD
    Panagiotis D. Christofides

    University of California, Los Angeles, United States

    28 publications · 2K citations

    21

    h-index

1–5 of 5.4K

Venues

  1. Journal of Process Control

    106 publications · 6.5K citations

    47

    h-index

  2. IEEE Transactions on Control Systems Technology

    74 publications · 6.6K citations

    44

    h-index

  3. Automatica

    63 publications · 15.3K citations

    42

    h-index

  4. Control Engineering Practice

    72 publications · 9.4K citations

    39

    h-index

  5. Computers & Chemical Engineering

    69 publications · 4.4K citations

    35

    h-index

1–5