A course of H∞0Econtrol theory
A course of H∞0Econtrol theory
Analysis and design for robust performance with structured uncertainty
Systems & Control Letters
Robust and optimal control
Design of the l1-suboptimal Robust Controller for the Linear Object with Nonstructured Uncertainty
Automation and Remote Control
Adaptive suboptimal robust control of the first-order plant
Automation and Remote Control
Automation and Remote Control
Robust tracking with unknown upper bounds on the perturbations and measurement noise
Automation and Remote Control
Hi-index | 0.00 |
In robust system analysis and robust controller design, the parameters of the nominal system and admissible perturbation classes are usually assumed to be known. In many cases, these classes are described by constraints on perturbation norms and weight filters. Model validation consists in verifying whether the nominal model and the a priori assumptions on measurement perturbations are consistent. A far more difficult problem is the estimation of norms or weights of perturbations from measurement data. In this paper, model verification and perturbation weight estimation are investigated within the framework the ℓ1-theory of robust control corresponding to the fundamental signal space ℓ∞. For any model in the form of a linear stationary discrete system with structured uncertainty, model validation is reduced to verifying whether a system of inequalities generated by measurements holds or not. For a model with unstructured uncertainty and diagonal weighted perturbations, optimization of perturbation weights is reduced a fractional quadratic programming problem. For a model with perturbations of irreducible multipliers of the transfer matrix of the nominal system, optimization of perturbation weights is reduced a linear programming problem.