Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
From MaRDI portal
Publication:1796994
DOI10.1016/j.automatica.2018.03.046zbMath1400.93079arXiv1611.03537OpenAlexW2561027863MaRDI QIDQ1796994
Publication date: 17 October 2018
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1611.03537
Control/observation systems governed by partial differential equations (93C20) Feedback control (93B52) Nonlinear systems in control theory (93C10) Time series analysis of dynamical systems (37M10)
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Uses Software
Cites Work
- A kernel-based method for data-driven Koopman spectral analysis
- On the numerical approximation of the Perron-Frobenius and Koopman operator
- qpOASES: a parametric active-set algorithm for~quadratic programming
- Nonlinear model predictive control. Theory and algorithms.
- A data-driven approximation of the koopman operator: extending dynamic mode decomposition
- On convergence of extended dynamic mode decomposition to the Koopman operator
- Comparison of systems with complex behavior
- Constrained model predictive control: Stability and optimality
- Spectral properties of dynamical systems, model reduction and decompositions
- On dynamic mode decomposition: theory and applications
- Dynamic Mode Decomposition with Control
- Sparse Sensor Placement Optimization for Classification
- Applied Koopmanism
- The Korteweg–deVries Equation: A Survey of Results
- Generalizing Koopman Theory to Allow for Inputs and Control