Data-driven quadratic stabilization and LQR control of LTI systems
Automatica, ISSN: 0005-1098, Vol: 153, Page: 111041
2023
- 7Citations
- 5Captures
- 1Mentions
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Findings from Northeastern University Reveals New Findings on Information Technology (Data-driven Quadratic Stabilization and Lqr Control of Lti Systems)
2023 JUL 04 (NewsRx) -- By a News Reporter-Staff News Editor at Information Technology Daily -- Research findings on Information Technology are discussed in a
Article Description
In this paper, we propose a framework to solve the data-driven quadratic stabilization (DDQS) and the data-driven linear quadratic regulator (DDLQR) problems for both continuous and discrete-time systems. Given noisy input/state measurements and a few priors, we aim to find a state feedback controller guaranteed to quadratically stabilize all systems compatible with the a-priori information and the experimental data. In principle, finding such a controller is a non-convex robust optimization problem. Our main result shows that, by exploiting duality, the problem can be recast into a convex, albeit infinite-dimensional, functional Linear Program. To address the computational complexity entailed in solving this problem, we show that a sequence of increasingly tight finite dimensional semi-definite relaxations can be obtained using sum-of-squares and Putinar’s Positivstellensatz arguments. Finally, we show that these arguments can also be used to find controllers that minimize a worst-case (over all plants in the consistency set) closed-loop H2 cost. The effectiveness of the proposed algorithm is illustrated through comparisons against existing data-driven methods that handle ℓ∞ bounded noise.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0005109823001966; http://dx.doi.org/10.1016/j.automatica.2023.111041; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85153303649&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0005109823001966; https://dx.doi.org/10.1016/j.automatica.2023.111041
Elsevier BV
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