Robust control design for linear systems via multiplicative noise

Benjamin J. Gravell*, Peyman Mohajerin Esfahani, Tyler H. Summers

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

4 Citations (Scopus)
26 Downloads (Pure)


Robust stability and stochastic stability have separately seen intense study in control theory for many decades. In this work we establish relations between these properties for discrete-time systems and employ them for robust control design. Specifically, we examine a multiplicative noise framework which models the inherent uncertainty and variation in the system dynamics which arise in model-based learning control methods such as adaptive control and reinforcement learning. We provide results which guarantee robustness margins in terms of perturbations on the nominal dynamics as well as algorithms which generate maximally robust controllers.

Original languageEnglish
Pages (from-to)7392-7399
Volume53 (2020)
Issue number2
Publication statusPublished - 2021
Event21st IFAC World Congress 2020 - Berlin, Germany
Duration: 12 Jul 202017 Jul 2020


  • Robust control (linear case)
  • Robust controller synthesis
  • Stochastic systems
  • Uncertainty descriptions


Dive into the research topics of 'Robust control design for linear systems via multiplicative noise'. Together they form a unique fingerprint.

Cite this