Economic oriented stochastic optimization in process control using Taguchi's method

András Király, László Dobos, János Abonyi

Research output: Article

Abstract

The optimal operating region of complex production systems is situated close to process constraints related to quality or safety requirements. Higher profit can be realized only by assuring a relatively low frequency of violation of these constraints. We defined a Taguchi-type loss function to aggregate these constraints, target values, and desired ranges of product quality. We evaluate this loss function by Monte-Carlo simulation to handle the stochastic nature of the process and apply the gradient-free Mesh Adaptive Direct Search algorithm to optimize the resulted robust cost function. This optimization scheme is applied to determine the optimal set-point values of control loops with respect to pre-determined risk levels, uncertainties and costs of violation of process constraints. The concept is illustrated by a well-known benchmark problem related to the control of a linear dynamical system and the model predictive control of a more complex nonlinear polymerization process. The application examples illustrate that the loss function of Taguchi is an ideal tool to represent performance requirements of control loops and the proposed Monte-Carlo simulation based optimization scheme is effective to find the optimal operating regions of controlled processes.

Original languageEnglish
Pages (from-to)547-563
Number of pages17
JournalOptimization and Engineering
Volume14
Issue number4
DOIs
Publication statusPublished - okt. 25 2013

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ASJC Scopus subject areas

  • Software
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Control and Optimization
  • Electrical and Electronic Engineering

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