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System Inversion for Nonlinear Descriptor Systems
Author(s) -
Röbenack K.,
Reinschke K.J.
Publication year - 2002
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/1617-7061(200203)1:1<51::aid-pamm51>3.0.co;2-7
Subject(s) - univariate , taylor series , nonlinear system , computation , automatic differentiation , computer science , inversion (geology) , mathematics , symbolic computation , algorithm , control theory (sociology) , process (computing) , transfer function , function (biology) , signal (programming language) , mathematical optimization , multivariate statistics , artificial intelligence , mathematical analysis , control (management) , engineering , machine learning , operating system , structural basin , biology , paleontology , quantum mechanics , evolutionary biology , programming language , physics , electrical engineering
Abstract In control engineering, one problem of output tracking is the computation of an input signal for a prescribed output curve. We consider process models given by nonlinear semi‐explicit descriptor systems. The authors derive a method to compute function values of the input signal by means of an univariate Taylor expansion of the systems' variables. To avoid computationally intensive symbolic calculations, we resort to automatic differentiation. Several automatic differentiation packages such as ADOL‐C or TADIFF can be applied to compute the derivatives needed.