Estimation of parameter bounds from bounded-error data: a survey
Mathematics and Computers in Simulation - Parameter identifications with error bound
Optimal estimation theory for dynamic systems with set membership uncertainty: an overview
Automatica (Journal of IFAC)
Feasible parameter set for linear models with bounded errors in all variables
Automatica (Journal of IFAC)
Realization of stable models with subspace methods
Automatica (Journal of IFAC)
Global Optimization with Polynomials and the Problem of Moments
SIAM Journal on Optimization
GloptiPoly: Global optimization over polynomials with Matlab and SeDuMi
ACM Transactions on Mathematical Software (TOMS)
SIAM Journal on Optimization
A new kernel-based approach for linear system identification
Automatica (Journal of IFAC)
Hi-index | 22.14 |
In this paper, we consider the identification of linear systems, a priori known to be stable, from input-output data corrupted by bounded noise. By taking explicitly into account a priori information on system stability, a formal definition of the feasible parameter set for a stable linear system is provided. On the basis of a detailed analysis of the geometrical structure of the feasible set, convex relaxation techniques are presented to solve nonconvex optimization problems arising in the computation of parameter uncertainty intervals. Properties of the computed relaxed bounds are discussed. A simulated example is presented to show the effectiveness of the proposed technique.