System identification: theory for the user
System identification: theory for the user
Robust and optimal control
Stability and Robustness of Multivariable Feedback Systems
Stability and Robustness of Multivariable Feedback Systems
SIAM Journal on Control and Optimization
Least costly identification experiment for control
Automatica (Journal of IFAC)
Brief Robustness analysis tools for an uncertainty set obtained by prediction error identification
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Brief paper: Identification for robust H2 deconvolution filtering
Automatica (Journal of IFAC)
Variance error, interpolation and experiment design
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
This paper presents a new controller validation method for linear multivariable time-invariant models. Classical prediction error system identification methods deliver uncertainty regions which are nonstandard in the robust control literature. Our controller validation criterion computes an upper bound for the worst case performance, measured in terms of the H"~-norm of a weighted closed loop transfer matrix, achieved by a given controller over all plants in such uncertainty sets. This upper bound on the worst case performance is computed via an LMI-based optimization problem and is deduced via the separation of graph framework. Our main technical contribution is to derive, within that framework, a very general parametrization for the set of multipliers corresponding to the nonstandard uncertainty regions resulting from PE identification of MIMO systems. The proposed approach also allows for iterative experiment design. The results of this paper are asymptotic in the data length and it is assumed that the model structure is flexible enough to capture the true system.