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Creators/Authors contains: "Johansson, Karl Henrik"

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  1. ; ; ; ; ; ; (, Automatica)
    Free, publicly-accessible full text available January 1, 2026
  2. ; ; ; ; (, Mathematical Biosciences)
    Jain, Harsh; Hao, Wenrui; Rempala, Grzegorz; Wang, Yangyang (Ed.)
    Free, publicly-accessible full text available November 1, 2025
  3. ; ; ; ; ; (, 5th Annual Learning for Dynamics and Control Conference)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; (, IEEE Transactions on Signal Processing)
    Full Text Available 
  5. ; ; ; ; (, Automatica)
    Full Text Available 
  6. ; ; ; (, SIAM Journal on Control and Optimization)
    Full Text Available 
  7. ; ; ; ; (, IFAC-PapersOnLine)
    Full Text Available 
  8. ; ; ; (, IEEE Control Systems Letters)
    Full Text Available 
  9. ; ; ; (, IEEE Transactions on Control of Network Systems)
    null (Ed.)
    Full Text Available 
  10. ; ; ; (, 2020 59th IEEE Conference on Decision and Control (CDC))
    In this paper we study a discrete-time SIS (susceptible-infected-susceptible) model, where the infection and healing parameters and the underlying network may change over time. We provide conditions for the model to be well-defined and study its stability. For systems with homogeneous infection rates over symmetric graphs, we provide a sufficient condition for global exponential stability (GES) of the healthy state, that is, where the virus is eradicated. For systems with heterogeneous virus spread over directed graphs, provided that the variation is not too fast, a sufficient condition for GES of the healthy state is established. Appealing to the first stability result, we present two data-driven mitigation strategies that set the healing parameters in a centralized and a distributed manner, respectively, in order to drive the system to the healthy state. 
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