Robust control design for linear systems via multiplicative noise

Robust control design for linear systems via multiplicative noise

Gravell, Benjamin J. (University of Texas at Dallas)
Mohajerin Esfahani, P. (TU Delft Team Peyman Mohajerin Esfahani; TU Delft Team Bart De Schutter)
Summers, Tyler H. (University of Texas at Dallas)

2021

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.

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

http://resolver.tudelft.nl/uuid:58364ee5-6d48-40a5-a6eb-d95d811c95e5

https://doi.org/10.1016/j.ifacol.2020.12.1268

1474-6670

IFAC-PapersOnLine, 53 (2020) (2), 7392-7399

21st IFAC World Congress 2020, 2020-07-12 → 2020-07-17, Berlin, Germany

Institutional Repository

journal article

© 2021 Benjamin J. Gravell, P. Mohajerin Esfahani, Tyler H. Summers