Robust control system design for uncertain systems (aka “plants”) is of great practical interest, and their automation is a key concern in the control community [1]. In the last few decades, several robust control methodologies in time as well as frequency domains have been proposed (cf. [1]). Quantitative Feedback Theory (QFT) [2] is a one such frequency-domain controller design technique base...

Recently several robust control designs have been proposed to the load-frequency control (LFC) problem. However, the importance and difficulties in the selection of weighting functions of these approaches and the pole-zero cancellation phenomenon associated with it produces closed loop poles. Also the order of robust controllers is as high as the plant. This gives rise to complex structure of s...

The aim of this paper is to propose a robust reliable bi-objective supply chain network design (SCND) model that is capable of controlling different kinds of uncertainties, concurrently. In this regard, stochastic bi-level scenario based programming approach which is used to model various scenarios related to strike of disruptions. The well-known method helps to overcome adverse effects of disr...

The version R14SP1 of function hinfsyn (full-order H∞ optimal controller synthesis) of the Robust Control System Toolbox for Matlab is benchmarked on the extensive set of linear models of the COMPleib database. The main conclusion is that sometimes the achieved numerical results are surprising, if not disappointing. This illustrates the difficulty of designing efficient and numerically reliable...

Theory Shortfall Most good models of physical plants are continuous-time models and most physical plants are, in fact, nonlinear. The continuous-time models we use are very often linear because we are not skilled enough to handle nonlinear models. Here I will discuss the linear case. Let us suppose, then, that we have a linear model of a physical plant. There are three aspects of the controller...

Circuit design under process variation can be formulated mathematically as a robust optimization problem with a yield constraint. Existing methods force designers to either resort to overly simplified circuit performance model, or rely on simplistic variability assumptions. On the other hand, accurate yield estimation must incorporate a sophisticated variability model that recognizes both syste...

in this study, a hybrid algorithm is presented to tackle multi-variables robust design problem. the proposed algorithm comprises neural networks (nns) and co-evolution genetic algorithm (cga) in which neural networks are as a function approximation tool used to estimate a map between process variables. furthermore, in order to make a robust optimization of response variables, co-evolution algor...