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A note on persistency of excitation
Overview of total least-squares methods
Structured low-rank approximation and its applications
- I. Markovsky
- Computer ScienceAutom.
- 1 April 2008
Low Rank Approximation - Algorithms, Implementation, Applications
- I. Markovsky
- Computer ScienceCommunications and Control Engineering
- 3 August 2018
Data Approximation by Low-complexity Models details the theory, algorithms, and applications of structured low-rank approximation and describes the applications including: system and control theory; signal processing; computer algebra for approximate factorization and common divisor computation; computer vision for image deblurring and segmentation.
The element-wise weighted total least-squares problem
Data-driven simulation and control
An approach for computing a linear quadratic tracking control signal that circumvents the identification step is presented and the results are derived assuming exact data and the simulated response or control input is constructed off-line.
Algorithms for deterministic balanced subspace identification
Structured Low-Rank Approximation with Missing Data
This paper considers low-rank approximation of affinely structured matrices with missing elements, a singular linear least-norm problem, based on reformulation of the problem as inner and outer optimization.
Bridging direct & indirect data-driven control formulations via regularizations and relaxations
- Florian Dorfler, J. Coulson, I. Markovsky
- MathematicsIEEE Transactions on Automatic Control
- 4 January 2021
We discuss connections between sequential system identification and control for linear time-invariant systems, often termed indirect data-driven control, as well as a contemporary direct data-driven…