Data‐driven mixed‐sensitivity control with automated weighting functions selection
From MaRDI portal
Publication:6190368
DOI10.1002/rnc.6579OpenAlexW4313388105MaRDI QIDQ6190368
Mirko Mazzoleni, Fabio Previdi, Simone Formentin, Unnamed Author
Publication date: 6 February 2024
Published in: International Journal of Robust and Nonlinear Control (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/rnc.6579
robust controlkernel-based system identificationdata-driven robust controlmixed sensitivity loop-shaping
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- State-space solution to weight optimization problem in \(\mathcal H_\infty\) loop-shaping control
- Kernel methods in system identification, machine learning and function estimation: a survey
- A new kernel-based approach for linear system identification
- On the formulation and solution of robust performance problems
- Comparing different approaches to model error modeling in robust identification
- Robust control and \({\mathcal H}_{\infty}\)-optimization -- tutorial paper
- Revisiting total model errors and model validation
- Kernel-based system identification with manifold regularization: a Bayesian perspective
- Randomized algorithms for analysis and control of uncertain systems. With applications
- A flexible transmission system as a benchmark for robust digital control
- On the estimation of transfer functions, regularizations and Gaussian processes-revisited
- Weight optimisation in \(\mathcal H_{\infty}\) loop-shaping
- Design variables for bias distribution in transfer function estimation
- Quantifying the error in estimated transfer functions with application to model order selection
- A Note on the Numerical Solutions of Kernel-Based Learning Problems
- Kernel-based identification of asymptotically stable continuous-time linear dynamical systems
This page was built for publication: Data‐driven mixed‐sensitivity control with automated weighting functions selection
