We revisit robust complexand mixed-μ synthesis problems based on upper bounds and show that they can be recast as specially structured controller design programs. The proposed reformulations suggest a streamlined handling of μ synthesis problems using recently developed (local) nonsmooth optimization methods where both scalings or multipliers and a controller of given structure are obtained sim...

In this paper the problem of robust control for a class of linear infinite dimensional systems under mixed disturbances of the multiplicative type is addressed. The Lyapunov function approach is used for proving that there is a controller that stabilizes this class of systems under the presence of uncertainties and perturbations, and guarantees some tolerance level for the joint cost functional...

The linear quantile regression estimator is very popular and widely used. It is also well known that this estimator can be very sensitive to outliers in the explanatory variables. In order to overcome this disadvantage, the usage of the least trimmed quantile regression estimator is proposed to estimate the unknown parameters in a robust way. As a prominent measure of robustness, the breakdown ...

The classic overall profit needs precise information of inputs, outputs, inputs and outputs price vectors. In real word, all data are not certain. Therefore, in this case, stochastic and fuzzy methods use for measuring overall profit efficiency. These methods require more information about the data such as probability distribution function or data membership function, which in some cases may no...

This paper presents a range of optimization based approaches to fault diagnosis. A variety of fault diagnosis problems are reformulated in the so-called standard problem setup introduced in the literature on robust control. Once the standard problem formulations are given, the fault diagnosis problems can be solved by standard optimization techniques. The proposed methods include: (1) fault dia...

Recently a general framework for studying robust output regulation problem for nonlinear systems is established. This framework is able to convert the robust output regulation problem for nonlinear systems into a robust stabilization problem, thus offering greater flexibility to incorporate recent new stabilization techniques for tackling the global output regulation problem. This paper success...

The input data to grammar learning algorithms often consist of overt forms that do not contain full structural descriptions. This lack of informationmay contribute to the failure of learning. Past work on Optimality Theory introduced Robust Interpretive Parsing (RIP) as a partial solution to this problem.We generalize RIP and suggest replacing the winner candidate with a weighted mean violation...

This paper is concerned with the analysis of a recently-proposed robust control policy for linear discrete-time systems subject to bounded state disturbances with mixed constraints on the states and inputs, which parameterizes the input as an affine function of the past disturbance sequence. The paper shows that this disturbance feedback policy is equivalent to the class of affine state feedbac...

We consider a rather general class of mathematical programming problems with data uncertainty, where the uncertainty set is represented by a system of convex inequalities. We prove that the robust counterparts of this class of problems can be equivalently reformulated as finite and explicit optimization problems. Moreover, we develop simplified reformulations for problems with uncertainty sets ...