Design of experiments for nonlinear system identification: a set membership approach
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Publication:2207177
DOI10.1016/j.automatica.2020.109036zbMath1453.93064OpenAlexW3031684975MaRDI QIDQ2207177
Milad Karimshoushtari, Carlo Novara
Publication date: 22 October 2020
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.automatica.2020.109036
system identificationmodel predictive controlexperiment designadaptive identificationDoEdata driven controlDoDESM-DoE
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Cites Work
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- Unified set membership theory for identification, prediction and filtering of nonlinear systems
- NSM constrained approximation of Lipschitz functions from data
- Parametric and nonparametric curve fitting
- Dynamic system identification. Experiment design and data analysis
- Optimal experiment design for dynamic system identification
- Optimal estimation theory for dynamic systems with set membership uncertainty: An overview
- From experiment design to closed-loop control
- Set membership identification of nonlinear systems
- Nonlinear black-box modeling in system identification: A unified overview
- A combined moving horizon and direct virtual sensor approach for constrained nonlinear estimation
- Some results on optimal experiment design
- On the optimal worst-case experiment design for constrained linear systems
- Computation of local radius of information in SM-IBC identification of nonlinear systems
- \(H_{\infty}\) set membership identification: a survey
- Sparse set membership identification of nonlinear functions and application to fault detection
- Nonlinear model predictive control from data: a set membership approach
- Set membership inversion and robust control from data of nonlinear systems
- A useful input parameterization for optimal experiment design
- Design of Robust Predictive Control Laws Using Set Membership Identified Models
- Robust nonlinear control design. State-space and Lyapunov techniques
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