Machine Learning Control Algorithms

Machine Learning Control Algorithms is a computational control methodology within prosthetic control systems that employs data-driven learning techniques to autonomously adapt and optimize control policies based on sensory feedback, distinguishing itself from traditional model-based or rule-driven approaches by its reliance on iterative learning from experience.

371

Publications

15.6K

Citations

1.3K

Authors

497

Institutions

Publications per year

2017–2026

266

Authors

1.3K

  1. JP
    Jan Peters

    Technische Universität Darmstadt, Germany

    4 publications · 173 citations

    4

    h-index

  2. ZW
    Zhe Wu

    University of California, Los Angeles, United States

    4 publications · 188 citations

    4

    h-index

  3. PD
    Panagiotis D. Christofides

    University of California, Los Angeles, United States

    4 publications · 121 citations

    4

    h-index

  4. PM
    Patrick M. Pilarski

    University of Alberta, Canada

    4 publications · 133 citations

    4

    h-index

  5. NM
    Nikolai Matni

    California Institute of Technology, United States

    3 publications · 137 citations

    3

    h-index

1–5 of 1.3K

Venues

  1. IEEE Access

    10 publications · 390 citations

    10

    h-index

  2. Sensors

    6 publications · 125 citations

    7

    h-index

  3. Applied Sciences

    6 publications · 198 citations

    6

    h-index

  4. IEEE Robotics and Automation Letters

    6 publications · 325 citations

    6

    h-index

  5. Computers & Chemical Engineering

    4 publications · 201 citations

    4

    h-index

1–5