Computationally efficient identification of continuous-time Lur'e-type systems with stability guarantees
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Publication:2063779
DOI10.1016/j.automatica.2021.110012zbMath1480.93093OpenAlexW3216401418MaRDI QIDQ2063779
Mohammad Fahim Shakib, Alexey N. Pavlov, Alexander Yu. Pogromsky, Nathan van de Wouw
Publication date: 3 January 2022
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.automatica.2021.110012
system identificationnonlinear systemsglobal stabilityparameter identificationnonlinear feedbacknumerical algorithmsWienerHammersteinsteady-state errors
Uses Software
Cites Work
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- Hierarchical least squares algorithms for nonlinear feedback system modeling
- Identification of systems with localised nonlinearity: from state-space to block-structured models
- System identification. An introduction.
- Uniform output regulation of nonlinear systems. A convergent dynamics approach.
- Block-oriented nonlinear system identification
- Frequency domain identification of continuous-time output error models. I: Uniformly sampled data and frequency function approximation
- Convergence properties of the generalised least squares identification method
- Fast approximate identification of nonlinear systems
- Identification of block-oriented nonlinear systems starting from linear approximations: a survey
- Identification of linear systems with nonlinear distortions
- On success rates for controlled random search
- Steady-state performance optimization for nonlinear control systems of Lur'e type
- Multistage least squares based iterative estimation for feedback nonlinear systems with moving average noises using the hierarchical identification principle
- Non-linear system identification using Hammerstein and non-linear feedback models with piecewise linear static maps
- Frequency domain curve fitting with maximum amplitude criterion and guaranteed stability
- Least squares parameter estimation of continuous-time ARX models from discrete-time data
- Principles of Optimal Design
- Identification of stable models in subspace identification by using regularization
- Nonlinear system identification using neural state space models, applicable to robust control design
- Adaptive output feedback control of nonlinear systems represented by input-output models
- Nonlinear System Identification: A User-Oriented Road Map
- Specialized Interior-Point Algorithm for Stable Nonlinear System Identification
- Subspace identification with guaranteed stability using constrained optimization
- Model quality in identification of nonlinear systems
- Convex Parameterizations and Fidelity Bounds for Nonlinear Identification and Reduced-Order Modelling
- Sampling and Sampled-Data Models: The Interface Between the Continuous World and Digital Algorithms
- The Describing Function Matrix
- A trust region method based on interior point techniques for nonlinear programming.
