Stable and robust LQR design via scenario approach
METADATA ONLY
Loading...
Author / Producer
Date
2021-07
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
Linear Quadratic Regulator (LQR) design is one of the most classical optimal control problems, whose well-known solution is an input sequence expressed as a state-feedback. In this work, finite-horizon and discrete-time LQR is solved under stability constraints and uncertain system dynamics. The resulting feedback controller balances cost value and closed-loop stability. Robustness of the solution is modeled using the scenario approach, without requiring any probabilistic description of the uncertainty in the system matrices. The new methods are tested and compared on the Leslie growth model, where we control population size while minimizing a suitable finite-horizon cost function.
Permanent link
Publication status
published
Editor
Book title
Journal / series
Volume
129
Pages / Article No.
109571
Publisher
Elsevier
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Optimal control; LQR design; Reinforcement learning; Lyapunov stability; Scenario approach; Population growth models