Matrix computations (3rd ed.)
LTI approximation of nonlinear systems via signal distribution theory
Automatica (Journal of IFAC)
Squared and absolute errors in optimal approximation of nonlinear systems
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)
Identification of nonlinear systems using Polynomial Nonlinear State Space models
Automatica (Journal of IFAC)
Nonlinear gray-box identification using local models applied to industrial robots
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
The paper addresses the problem of preserving the same LTI approximation of a nonlinear MIMO (multiple-input multiple-output) system. It is shown that when a nonlinear MIMO system is modeled by a multidimensional Volterra series, periodic noise and random multisines are equivalent excitations to the classical Gaussian noise, in a sense that they yield in the limit, as the number of the harmonics M-~, the same linear approximation to the nonlinear MIMO system. This result extends previous results derived for nonlinear SISO (single-input single-output) systems. Based upon the analysis of the variability of the measured FRF (frequency response function) due to the presence of the nonlinearities and the randomness of the excitations, a new class of equivalent input signals is proposed, allowing for a lower variance of the nonlinear FRF measurements, while the same linear approximation is retrieved.