The economic assessment of process control quality using a Taguchi-based method

Abstract This paper derives a measure of dynamic control quality within the Taguchi framework (G. Taguchi, E.A. Elsayed, T. Hsiang, Quality Engineering in Production Systems, McGraw-Hill, New York, 1989) which estimates product quality (in economic terms) by the losses incurred when specified product characteristics depart from their nominal values. In this sense, a control system may be viewed as a “product” with stability and performance properties as its required characteristics. The degree to which system inputs and outputs depart from their nominal values is used to give an estimate of stability and performance quality, respectively. Displaying control quality graphically with stability and performance qualities as the two axes can be used to highlight the trade-off between these two characteristics. This technique was used to evaluate the quality offered by four model-based controllers on a distillation column model responding to a typical disturbance, and how the presence of measurement and valve dynamics affected control quality. It is anticipated that the measures derived in this paper (being both time-based and economic) may prove useful in conveying control quality in a common language understood by engineers, operators and management.

[1]  W. C. Li,et al.  Newton-type control strategies for constrained nonlinear processes , 1989 .

[2]  Michael Nikolaou,et al.  A hybrid approach to nonlinear system stability and performance , 1989 .

[3]  Emad Ali,et al.  On the Tuning of Nonlinear Model Predictive Control Algorithms , 1993 .

[4]  John H. Sheesley,et al.  Quality Engineering in Production Systems , 1988 .

[5]  Geoff Barton,et al.  Interaction between process design and process control: economic analysis of process dynamics , 1991 .

[6]  B. Wayne Bequette,et al.  Nonlinear model‐predictive control: Closed‐loop stability analysis , 1996 .

[7]  Athanassios Kassidas,et al.  Predictive Controller Design with Implicit Economic Criteria , 1993, 1993 American Control Conference.

[8]  Carlos E. Garcia,et al.  Internal model control. 2. Design procedure for multivariable systems , 1985 .

[9]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[10]  C. Economou.  An operator theory approach to nonlinear controller design , 1986 .

[11]  Lorenz T. Biegler,et al.  Process control strategies for constrained nonlinear systems , 1988 .

[12]  Ciann-Dong Yang,et al.  Synthesis of robust-performance controllers using matrix experiments and analysis of variance , 1997 .

[13]  Evanghelos Zafiriou,et al.  Robust Model Predictive Control of Processes with Hard Constraints. , 1990 .

[14]  Lorenz T. Biegler,et al.  Constraint handing and stability properties of model‐predictive control , 1994 .

[15]  John B Lear.  The effects of uncertainty on the economics of optimising control , 1992 .

[16]  George Stephanopoulos,et al.  Chemical Process Control: An Introduction to Theory and Practice , 1983 .

[17]  Moses O. Tadé,et al.  Comparative evaluation of advanced process control techniques for alumina flash calciners , 1998 .

[18]  Benjamin C. Kuo,et al.  AUTOMATIC CONTROL SYSTEMS , 1962, Universum:Technical sciences.

[19]  G. Stein,et al.  Performance and robustness analysis for structured uncertainty , 1982, 1982 21st IEEE Conference on Decision and Control.

[20]  M. Morari,et al.  Newton control laws for nonlinear controller design , 1985, 1985 24th IEEE Conference on Decision and Control.