Learning stochastically stable Gaussian process state–space models

Article Properties
Cite
Umlauft, Jonas, and Sandra Hirche. “Learning Stochastically Stable Gaussian Process state–space Models”. IFAC Journal of Systems and Control, vol. 12, 2020, p. 100079, https://doi.org/10.1016/j.ifacsc.2020.100079.
Umlauft, J., & Hirche, S. (2020). Learning stochastically stable Gaussian process state–space models. IFAC Journal of Systems and Control, 12, 100079. https://doi.org/10.1016/j.ifacsc.2020.100079
Umlauft J, Hirche S. Learning stochastically stable Gaussian process state–space models. IFAC Journal of Systems and Control. 2020;12:100079.
Journal Category
Technology
Mechanical engineering and machinery
Refrences
Title Journal Journal Categories Citations Publication Date
Stable Gaussian process based tracking control of Euler–Lagrange systems Automatica
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
 42 2019
Gaussian processes for learning and control: A tutorial with examples 2018
An uncertainty-based control Lyapunov approach for control-affine systems modeled by Gaussian process 2018
Learning control Lyapunov function to ensure stability of dynamical system-based robot reaching motions Robotics and Autonomous Systems
  • Technology: Mechanical engineering and machinery
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Mechanical engineering and machinery
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
 76 2014
Kernel methods in system identification, machine learning and function estimation: A survey Automatica
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
 368 2014
Citations
Title Journal Journal Categories Citations Publication Date
Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models IFAC-PapersOnLine 2023
An efficient two‐stage algorithm for parameter identification of non‐linear state‐space models‐based on Gaussian process regression

 IET Control Theory & Applications
  • Technology: Mechanical engineering and machinery: Control engineering systems. Automatic machinery (General)
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Science: Mathematics: Instruments and machines
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
 1 2023
Learning Discrete-Time Uncertain Nonlinear Systems With Probabilistic Safety and Stability Constraints IEEE Open Journal of Control Systems
  • Technology: Mechanical engineering and machinery: Control engineering systems. Automatic machinery (General)
  • Technology: Mechanical engineering and machinery
 2022
Deterministic error bounds for kernel-based learning techniques under bounded noise Automatica
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
 13 2021
Joint State and Dynamics Estimation With High-Gain Observers and Gaussian Process Models IEEE Control Systems Letters
  • Technology: Mechanical engineering and machinery
 2021
Citations Analysis
Category Category Repetition
Technology: Mechanical engineering and machinery4
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks2
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics2
Technology: Engineering (General). Civil engineering (General)2
Technology: Mechanical engineering and machinery: Control engineering systems. Automatic machinery (General)2
Science: Mathematics: Instruments and machines1
The category Technology: Mechanical engineering and machinery 4 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Deterministic error bounds for kernel-based learning techniques under bounded noise and was published in 2021. The most recent citation comes from a 2023 study titled Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models. This article reached its peak citation in 2023, with 2 citations. It has been cited in 5 different journals, 40% of which are open access. Among related journals, the IFAC-PapersOnLine cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year