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Using Sequential Quadratic Programming for System Identification |
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PP: 19-26 |
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Author(s) |
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Ana S. R. Brásio,
Andrey Romanenko,
Natércia C. P. Fernandes,
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Abstract |
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| System identification plays an important role in the development of process simulators and controllers. The ability to
determine correctly the model parameters directly affects the model quality and, therefore, the model based controller performance.
This work details the development of a system identification approach and its computational implementation based on sequential
quadratic programming (SQP) in which first and second order linear systems, represented in state-space, are identified from simulated
and from real industrial process data. Both single-input single-output and multivariable processes are considered. The resulting
optimization problem may become not trivial to solve as one of the examples illustrates. It is shown how a rescaling of the decision
variables or the use of a priori process knowledge may be used in order to overcome the difficulties and to improve the quality of the
results. |
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