Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Information-based complexity and nonparametric worst-case system identification
Journal of Complexity - Special issue: invited articles dedicated to J. F. Traub on the occasion of his 60th birthday
Control of uncertain systems: a linear programming approach
Control of uncertain systems: a linear programming approach
Analysis of linear methods for robust identification in ℓ1
Automatica (Journal of IFAC)
Robust and optimal control
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Stability and Robustness of Multivariable Feedback Systems
Stability and Robustness of Multivariable Feedback Systems
Measuring Distance between Systems under Bounded Power Excitation
SIAM Journal on Control and Optimization
Brief On robustness in system identification
Automatica (Journal of IFAC)
On linear models for nonlinear systems
Automatica (Journal of IFAC)
Least-squares LTI approximation of nonlinear systems and quasistationarity analysis
Automatica (Journal of IFAC)
LTI modelling of NFIR systems: near-linearity and control, LS estimation and linearization
Automatica (Journal of IFAC)
LTI approximation of nonlinear systems via signal distribution theory
Automatica (Journal of IFAC)
Measuring a linear approximation to weakly nonlinear MIMO systems
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
Robustness issues are studied in the context of linear models in linear controller design and in system identification when the true system is nonlinear. The notion of nearly linear system is generalized to include time-varying and open-loop unstable systems. This class of systems, although it presents the simplest possible (global) generalization of linear systems, is in many ways nontrivial for the purpose of linear controller design and linear model identification from input-output data. This is mainly due to the presence of non-conic uncertainty, which is not included in standard treatments of robust control theory and stochastic system identification theory. Signal distribution theory, a realistic non-stochastic signal analysis tool, is used to study the limiting least squares estimates of linear finite impulse response (FIR) and autoregressive with external input (ARX) model parametrizations for two classes of nonlinear systems. Some connections to worst-case analysis of linear model identification are also discussed.