We propose a data-driven outlier-insensitive adaptive robust optimization framework that leverages big data in industries. A Bayesian nonparametric model – the Dirichlet process mixture model – is adopted to extract the information embedded within uncertainty data via a variational inference algorithm. We then devise data-driven uncertainty sets for adaptive robust optimization. This Bayesian n...

Some robust control methods have been developed in the past in order to increase tracking performance in the presence of parametric uncertainties. In the presence of parametric uncertainty, unmodelled dynamics and other sources of uncertainties, robust control laws are used. Corless-Leitmann [1] approach is a popular approach used for designing robust controllers for robot manipulators. In earl...

  An optimal desirability function method is proposed to optimize multiple responses in multiple production scenarios, simultaneously. In dynamic environments, changes in production requirements in each condition create different production scenarios. Therefore, in multiple production scenarios like producing in several production lines with different technologies in a factory, various fitted r...

A Sensory Uncertainty Field (SUF) is a model of the localization uncertainty of a mobile robot. The value of the SUF at a speciic robot connguration q expresses the expected uncertainty of the robot at q, as this would be measured by some localization procedure. Path planning over the SUF provides a way for better localization, and thus fewer failures, during navigation. In this paper we extend...

Determination of mobile localization with time of arrival (TOA) signal is a requirement in cellular mobile communication. In some of the previous methods, localization with non-line-of-sight (NLOS) paths can lead to large position error. Also for simplicity, in most simulations suppose non stationary actual environments as stationary. This paper proposes (residual test + recursive least square)...

An adaptive online flux-linkage estimation method for the sensorless control of switched reluctance motor (SRM) drive is presented in this paper. Sensorless operation is achieved through a binary observer based algorithm. In order to avoid using the look up tables of motor characteristics, which makes the system, depends on motor parameters, an adaptive identification algorithm is used to estim...

In this short discussion, we point out that it is apparently as easy to be fooled by robustness as it is to be fooled by randomness. Our objective is to bring to the attention of applied ecologists that radius-of-stability robustness models are models of local robustness. As such, these models are utterly unsuitable for the treatment/management of a severe uncertainty characterized by a vast un...

The representation form of most signals in practice is non-stationary signal, and the main analysis methods for these signals are centralized in the field of time frequency (or time scale), and a series of results have been acquired. A reversible transformation method based on time sequence bit is proposed from another angle, and this method can be used to analyze non-stationary wide-band noise...

In this paper we study sensitivity analysis of the uncapacitated single level economic lot-sizing problem, which was introduced by Wagner and Whitin about thirty years ago. In particular we are concerned with the computation of the maximal ranges in which the numerical problem parameters may vary individually, such that a solution already obtained remains optimal. Only recently it was discovere...

in this paper the fuzzy structural reliability index was determined through modeling epistemic uncertainty arising from ambiguity in statistical parameters of random variables. the first order reliability method (form) has been used and a robust genetic algorithm in the alpha level optimization method has been proposed for the determination of the fuzzy reliability index. the sensitivity level ...