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OptimizationBased Design of PlantFriendly Input Signals Using Geometric Discrepancy Criteria
 14 th IFAC Symposium on System Identification (SYSID 2006
, 2006
"... Abstract: The design of constrained, “plantfriendly ” multisine input signals that optimize a geometric discrepancy criterion arising from Weyl’s Theorem is examined in this paper. Such signals are meaningful for datacentric estimation methods, where uniform coverage of the output statespace is c ..."
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Abstract: The design of constrained, “plantfriendly ” multisine input signals that optimize a geometric discrepancy criterion arising from Weyl’s Theorem is examined in this paper. Such signals are meaningful for datacentric estimation methods, where uniform coverage of the output statespace is critical. The usefulness of this problem formulation is demonstrated by applying it to a linear example and to the nonlinear, highly interactive distillation column model developed by Weischedel and McAvoy (1980). The optimization problem includes a search for both the Fourier coefficients and phases in the multisine signal, resulting in an uniformly distributed output signal displaying a desirable balance between high and low gain directions. The solution involves very little user intervention (which enhances its practical usefulness) and has significant benefits compared to multisine signals that minimize crest factor.
UNPUBLISHED
, 2005
"... AIChE shall not be responsible for statements or opinions contained in papers or printed in publications. The design of constrained, “plantfriendly ” multisine input signals that optimize a geometric discrepancy criterion arising from Weyl’s Theorem is examined in this paper. Such signals are meani ..."
Abstract

Cited by 1 (0 self)
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AIChE shall not be responsible for statements or opinions contained in papers or printed in publications. The design of constrained, “plantfriendly ” multisine input signals that optimize a geometric discrepancy criterion arising from Weyl’s Theorem is examined in this paper. Such signals are meaningful for datacentric estimation methods, where uniform coverage of the output statespace is critical. The usefulness of this problem formulation is demonstrated by applying it to a linear example and to the nonlinear, highly interactive distillation column model developed by Weischedel and McAvoy (1980). The optimization problem includes a search for both the Fourier coefficients and phases in the multisine signal, resulting in an uniformly distributed output signal displaying a desirable balance between high and low gain directions. The solution involves very little user intervention (which enhances its practical usefulness) and has significant benefits compared to multisine signals that minimize crest factor. The effectiveness of data resulting from a Weyl criterionbased signal for ModelonDemand Model Predictive Control (a datacentric multivariable control algorithm) is demonstrated for the distillation column case study.
Stateoftheart in the Solution of ControlRelated Nonlinear Optimisation Problems
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Optimizationbased Design of PlantFriendly Input Signals for ModelonDemand Estimation and Model Predictive Control
"... Abstract — The design of constrained, “plantfriendly ” multisine input signals that optimize a geometric discrepancy criterion arising from Weyl’s Theorem is examined in this paper. Such signals are meaningful for datacentric estimation and control methods, where uniform coverage of the output sta ..."
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Abstract — The design of constrained, “plantfriendly ” multisine input signals that optimize a geometric discrepancy criterion arising from Weyl’s Theorem is examined in this paper. Such signals are meaningful for datacentric estimation and control methods, where uniform coverage of the output statespace contributes greatly to good performance. The optimization problem includes a search for both the Fourier coefficients and phases in the multisine signal, resulting in an uniformly distributed output signal that achieves a desirable balance between high and low gain directions, an important consideration when identifying strongly interactive multivariable systems. The solution involves very little user intervention and has significant benefits compared to multisine signals that minimize crest factor. The usefulness of this problem formulation is shown by applying it to a case study involving composition control of a binary distillation column. I.
Optimal Input Signal Design in DataCentric System Identification
, 2006
"... System identification is the science of modelling the behavior of a dynamical system based on inputoutput data. Input signal design is a critical aspect of system identification since it directly affects the model quality. Highly interactive multiple input multiple output (MIMO) systems present cha ..."
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System identification is the science of modelling the behavior of a dynamical system based on inputoutput data. Input signal design is a critical aspect of system identification since it directly affects the model quality. Highly interactive multiple input multiple output (MIMO) systems present challenges in implementing methodologies like Model on Demand (MoD) that require a good output state space distribution. A powerful optimizationbased framework for input signal design is presented that achieves very good distribution in the output state space and, in addition, makes the input signal “plant friendly”. This new framework was tested in case studies of increasing difficulty. For each case study, an optimization problem was formulated using this new framework and solved using the NLP solver KNITRO and the modelling language AMPL. The results show the excellent directionality information obtained using this approach for both linear and nonlinear systems and the great value of this