Probabilistic design of systems with general distributions of parameters

This paper presents a new method for finding optimal solutions of systems w ith design parameters which are random variables distributed with various g eneral and possibly non-symmetrical distributions. A double-bounded density function is used to approximate the distributions. Specifications may requ ire tracking constraints in time domain and stability conditions in frequen cy domain. Using sensitivity information, the proposed method first finds a linearized feasible region. Afterwards it attempts to place a tolerance bo x of the design parameters such that the region with higher yield lies in t he feasible region. The yield is estimated by the joint cumulative density function over a portion of the tolerance box contained in the feasible regi on. Optimal designs are found for a fourth-order servomechanism and actual yields are evaluated by Monte-Carlo simulation. Copyright (C) 2001 John Wil ey & Sons, Ltd.