On linearity of spline algorithms
Journal of Complexity
Optimal estimation theory for dynamic systems with set membership uncertainty: an overview
Automatica (Journal of IFAC)
A class of algorithms for identification in H∞
Automatica (Journal of IFAC)
Identification in H∞ using Pick's interpolation
Systems & Control Letters
Time domain identification for robust control
Systems & Control Letters
The least-squares identification of FIR systems subject to worst-case noise
Systems & Control Letters
Automatica (Journal of IFAC)
Consistent parameter bounding identification for linearly parametrized model sets
Automatica (Journal of IFAC)
Worst-case analysis of the least-squares method and related identification methods
Systems & Control Letters
Automatica (Journal of IFAC) - Special issue on trends in system identification
Worst-case control-relevant identification
Automatica (Journal of IFAC) - Special issue on trends in system identification
Sample complexity of worst-case H∞ -identification
Systems & Control Letters
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
Robustness in Identification and Control
Robustness in Identification and Control
Brief Unfalsified model parametrization based on frequency domain noise information
Automatica (Journal of IFAC)
Comparing different approaches to model error modeling in robust identification
Automatica (Journal of IFAC)
Closed-loop model set validation under a stochastic framework
Automatica (Journal of IFAC)
Brief Robustness in H∞ identification
Automatica (Journal of IFAC)
Comparing internal model control and sliding-mode approaches for vehicle yaw control
IEEE Transactions on Intelligent Transportation Systems
Unified Set Membership theory for identification, prediction and filtering of nonlinear systems
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
Robustness had become in past years a central issue in system and control theory, focusing the attention of researchers from the study of a single model to the investigation of a set of models, described by a set of perturbations of a ''nominal'' model. Such a set, often indicated as an uncertainty model set or model set for short, has to be suitably constructed to describe the inherent uncertainty about the system under consideration and to be used for analysis and design purposes. H"~ identification methods deliver uncertainty model sets in a suitable form to be used by well-established robust design techniques, based on H"~ or @m optimization methods. The literature on H"~ identification is now very extensive. In this paper, some of the most relevant contributions related to assumption validation, evaluation of bounds on unmodeled dynamics, convergence analysis and optimality properties of linear, two-stage and interpolatory algorithms are surveyed from a deterministic point of view.