Multi-Criteria Identification of a Controllable Descending System

Abstract

This paper introduces an effective computational environment for multi-objective decision-making, optimization and identification. The paper adopts multi-objective vector identification methodology and performance assessment provided by the parameter space investigation method (PSI). The main feature of this methodology is in the fact that various design objectives are taken into consideration in their natural form without reducing dimensionality of the problem and therefore without distorting its nature. Therefore, there is no need for artificial convolution and weighting of multiple criteria. Moreover, the design alternatives are assessed explicitly versus multiple given requirements. The main practical purpose of this work is of twofold. First, we introduce an optimization framework and technique that allows to determine feasible and Pareto sets of the numerous uncertainties inherent for real-world engineering systems. This framework tightly couples principal advantages of MatLab/Simulink simulation engine with the unique properties of the multi-objective PSI method. Second, we show key benefits of the MatLab/PSI bundle on the example of identification of the principal aerodynamic characteristics and apparent masses of the controllable circular parachute