Online Data-Driven Control of Nonlinear Systems Using Semidefinite Programming

被引：0

作者：

Bozza, Augusto ^{[1
]}

Martin, Tim ^{[2
]}

Cavone, Graziana ^{[3
]}

Carli, Raffaele ^{[1
]}

Dotoli, Mariagrazia ^{[1
]}

Allgoewer, Frank ^{[2
]}

机构：

[1] Polytech Bari, Dept Elect & Informat Engn, I-70125 Bari, Italy

[2] Univ Stuttgart, Inst Syst Theory & Automat Control, D-70550 Stuttgart, Germany

[3] Univ Roma Tre, Dept Civil Comp Sci & Aeronaut Technol Engn, I-00146 Rome, Italy

来源：

IEEE CONTROL SYSTEMS LETTERS | 2024年 / 8卷

关键词：

Noise; Vectors; Optimization; Nonlinear dynamical systems; Noise measurement; Estimation; Semidefinite programming; Lyapunov methods; Heuristic algorithms; Aerodynamics; Data-driven control; subspace identification of nonlinear dynamics; feedback linearization; semidefinite programming; linear matrix inequality; OPTIMIZATION;

D O I：

10.1109/LCSYS.2024.3521645

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This letter proposes a novel Data-Driven (DD) method for controlling unknown input-affine nonlinear systems. First, we estimate the system dynamics from noisy data offline through Subspace Identification of Nonlinear Dynamics. Then, at each time step during runtime, we exploit this estimation to deduce a feedback-linearization control law that robustly regulates all the systems consistent with the data. Notably, the control law is derived by solving a Semidefinite Programming (SDP) online. Moreover, closed-loop stability is ensured by constraining a Lyapunov function to descend in each time step using a linear-matrix-inequality representation. Unlike related DD control approaches for nonlinear systems based on SDP, our approach does not require any approximation of the nonlinear dynamics, while requiring the knowledge of a library of candidate basis functions. Finally, we validate our theoretical contributions by simulations for stabilization and tracking, outperforming another DD literature-inspired controller.

引用

页码：3189 / 3194

页数：6

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