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Convex formulations are derived for the minimization of uncertainty bounds with respect to a nominal model and given input-output data for general uncertainty models of LFT type. The known data give rise to data-matching conditions that have to be satisfied. It is shown how these conditions, which originally are in the form of BMIs for a number of uncertainty models, can be transformed to LMIs, thus...
This paper deals with a convex optimization approach to the problem of robust network-based H∞ control for linear systems connected over a common digital communication network with norm-bounded parameter uncertainties. Firstly, we investigate the effect of both the output quantization levels and the network conditions under static quantizers. Secondly, by introducing a descriptor technique, using...
The paper considers non-fragile guaranteed cost for an interval system with state and input delay. An interval system described by matrix factorization and the attention is focused on the design of memoryless state feedback controllers such that the resulting closed-loop system is not only robustly stable but also guaranteed to be no more than a certain upper bound with all admissible uncertainties...
In this paper, we present a robust Iterative Learning Control (ILC) design for linear systems in the presence of time-varying parametric uncertainties. The robust ILC design is formulated as a min-max problem using a quadratic performance criterion subject to constraints of the control input update where the system model contains time-varying parametric uncertainties. An upper bound of the worst-case...
In this paper, a new robust Iterative Learning Control (ILC) algorithm has been proposed for linear systems in the presence of iteration-varying parametric uncertainties. The robust ILC design is formulated as a min-max problem using a quadratic performance criterion subject to constraints of the control input update. An upper bound of the maximization problem is derived, then, the solution of the...
A new approach for robust fixed-order H∞ controller design by convex optimization is proposed. Linear time-invariant single-input single-output systems represented by a finite set of complex values in the frequency domain are considered. It is shown that the H∞ robust performance condition can be approximated by a set of linear or convex constraints with respect to the parameters of a linearly parameterized...
This paper presents the mapping equations for integral quadratic constraints (IQCs). In particular it is shown that IQCs with bounded rational multipliers can be mapped into parameter space. Using IQCs not only provides a uniform framework to map specifications into parameter space, but provides a unified approach to parameter space based robustness analysis and synthesis with respect to nonlinearities,...
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