Paper Info
Title
Data-Driven Optimal Control of Bilinear Systems
Abstract
This letter develops a method to learn optimal controls from data for bilinear systems without a priori knowledge of the dynamics. Given an unknown bilinear system, we characterize when the available data is sufficiently informative to solve the optimal control problem. This characterization leads us to propose an online control experiment design procedure that guarantees that any input/state trajectory can be represented as a linear combination of collected input/state data matrices. Leveraging this representation, we transform the original optimal control problem into an equivalent data-based optimization problem with bilinear constraints. We solve the latter by iteratively employing a convex-concave procedure to find a locally optimal control sequence. Simulations show that the performance of the proposed data-based approach is comparable with model-based methods.
Year
DOI
Venue
2022
10.1109/LCSYS.2022.3164983
IEEE CONTROL SYSTEMS LETTERS
Keywords
DocType
Volume
Nonlinear systems, Optimal control, Trajectory, Data models, Linear systems, Optimization, Nonlinear dynamical systems, Data-driven control, biliner systems
Journal
6
ISSN
Citations 
PageRank 
2475-1456
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Zhenyi Yuan100.34
Jorge Cortes21452128.75