We present a method for linear stability analysis of systems with parametric uncertainty formulated in the stochastic Galerkin framework. Specifically, we assume that model partial differential equation, parameter is given form generalized polynomial chaos expansion. The leads to solution eigenvalue problem, and wish characterize rightmost eigenvalue. focus, particular, on problems nonsymmetric...

Air pollutant emission inventories are a vital component of environmental decision-making. Errors in emission factor estimation can lead to errors in emission inventory estimation. Potential sources of error include unaccounted for variability and uncertainty. Variability refers to diversity over time or space. Uncertainty is a lack of knowledge about the true value of a quantity. Probability d...

Abstract We formulate a superhedging theorem in the presence of transaction costs and model uncertainty. Asset prices are assumed continuous uncertainty is modeled parametric setting. Our proof relies on new topological framework which no Krein–Smulian type available.

One obstacle in connecting robust control with models generated from prediction error identification is that very few control design methods are able to directly cope with the ellipsoidal parametric uncertainty regions that are generated by such identification methods. In this contribution we present a sufficient condition for the existence of a H∞ state feedback controller for the multi-input/...

Quantitative methods for characterizing both variability and uncertainty are applied to case studies of emission factors for stationary natural gas-fueled internal combustion engines. NOx and Total Organic Carbon (TOC) emission data sets for lean burn engines were analyzed. Data were available for uncontrolled engines and for engines with pre-combustion chamber (PCC) and "clean burn" NOx contro...

In this paper, we model production technology in a state-contingent framework. We assume that all the firms use the same stochastic technology, but they may have different risk attitudes and information sets, and ex post they may operate in different production environments. Firms maximise ex ante their preference function subject to a stochastic technology constraint; in other words, they are ...

This paper presents a robust control law for controlling robot manipulators with parameter uncertainty, applied to a 4-DOF robot. The design of the controller is based on the Lyapunov function and the control law that is designed to compensate the uncertainties arising from unknown robot parameters and unknown payload. Major simulations results using numerical values of an industrial SCARA robo...

Scenario analysis within a multi-stage stochastic programming formulation offers an attractive framework for modelling uncertainties in long-range planning models. However, whether the expected outcome is implicitly / explicitly evaluated, such formulations lead to computationally intensive optimization problems. Focussing here on the scenario planning / multi-period approach for mixed integer ...