학술논문

Learning Controllers for Performance Through LMI Regions
Document Type
Periodical
Source
IEEE Transactions on Automatic Control IEEE Trans. Automat. Contr. Automatic Control, IEEE Transactions on. 68(7):4351-4358 Jul, 2023
Subject
Signal Processing and Analysis
Symmetric matrices
Eigenvalues and eigenfunctions
Noise measurement
Linear matrix inequalities
Transient analysis
Data models
Damping
Control design
data-driven control
linear feedback control systems
linear matrix inequalities
Lyapunov methods
matrix stability
noisy data
performance specification
robust control
transient behaviour
uncertain systems
Language
ISSN
0018-9286
1558-2523
2334-3303
Abstract
In an experiment, an input sequence is applied to an unknown linear time-invariant system (in continuous or discrete time) affected also by an unknown-but-bounded disturbance sequence; the corresponding state sequence (and state derivative sequence, in continuous time) is measured. The goal is to design directly from the input and state sequences a controller that enforces a certain performance specification on the transient behavior of the unknown system. The performance specification is expressed through a subset of the complex plane where closed-loop eigenvalues need to belong, a so called linear matrix inequality (LMI) region. For this control design problem, we provide here convex programs to enforce the performance specification from data in the form of LMIs. For generic LMI regions, these are sufficient conditions to assign the eigenvalues within the LMI region for all possible dynamics consistent with data, and become necessary and sufficient conditions for special LMI regions. In this way, we extend classical model-based conditions from a work in the literature to the setting of data-driven control from noisy data. Numerical examples substantiate the analysis.