Robust Control Design for Linear Systems via Multiplicative Noise
| Autor: | Benjamin Gravell, Tyler H. Summers, Peyman Mohajerin Esfahani |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Adaptive control Stochastic systems Robust control (linear case) Computer science 020208 electrical & electronic engineering Linear system Stability (learning theory) 02 engineering and technology Dynamical Systems (math.DS) Multiplicative noise 020901 industrial engineering & automation Robust controller synthesis Control and Systems Engineering Robustness (computer science) Control theory Optimization and Control (math.OC) 0202 electrical engineering electronic engineering information engineering FOS: Mathematics Reinforcement learning Uncertainty descriptions Robust control Mathematics - Dynamical Systems Mathematics - Optimization and Control |
| Zdroj: | IFAC-PapersOnLine, 53 (2020)(2) |
| ISSN: | 1474-6670 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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