Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Control of uncertain systems: a linear programming approach
Control of uncertain systems: a linear programming approach
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
Bounded Power Signal Spaces for Robust Control and Modeling
SIAM Journal on Control and Optimization
Squared and absolute errors in optimal approximation of nonlinear systems
Automatica (Journal of IFAC)
Brief Frequency response function measurements in the presence of nonlinear distortions
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)
On robustness in control and LTI identification: Near-linearity and non-conic uncertainty
Automatica (Journal of IFAC)
Linear approximations of nonlinear FIR systems for separable input processes
Automatica (Journal of IFAC)
LTI modelling of NFIR systems: near-linearity and control, LS estimation and linearization
Automatica (Journal of IFAC)
Measuring a linear approximation to weakly nonlinear MIMO systems
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
L"2 and L"1 optimal linear time-invariant (LTI) approximation of discrete-time nonlinear systems, such as nonlinear finite impulse response (NFIR) systems, is studied via a signal distribution theory motivated approach. The use of a signal distribution theoretic framework facilitates the formulation and analysis of many system modelling problems, including system identification problems. Specifically, a very explicit solution to the L"2 (least squares) LTI approximation problem for NFIR systems is obtained in this manner. Furthermore, the L"1 (least absolute deviations) LTI approximation problem for NFIR systems is essentially reduced to a linear programming problem. Active LTI modelling emphasizes model quality based on the intended use of the models in linear controller design. Robust stability and LTI approximation concepts are studied here in a nonlinear systems context. Numerical examples are given illustrating the performance of the least squares (LS) method and the least absolute deviations (LAD) method with LTI models against nonlinear unmodelled dynamics.