Linear approximation model network and its formation via evolutionary computation |
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Authors: | Yun Li Kay Chen Tan |
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Affiliation: | (1) Centre for Systems & Control, and Department of Electronics & Electrical Engineering, University of Glasgow, G12 8LT Glasgow, UK;(2) Department of Electrical Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260 Singapore |
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Abstract: | To overcome the deficiency of ’local model network’ (LMN) techniques, an alternative ’linear approximation model’ (LAM) network
approach is proposed. Such a network models a nonlinear or practical system with multiple linear models fitted along operating
trajectories, where individual models are simply networked through output or parameter interpolation. The linear models are
valid for the entire operating trajectory and hence overcome the local validity of LMN models, which impose the predetermination
of a scheduling variable that predicts characteristic changes of the nonlinear system. LAMs can be evolved from sampled step
response data directly, eliminating the need for local linearisation upon a pre-model using derivatives of the nonlinear system.
The structural difference between a LAM network and an LMN is that the overall model of the latter is a parameter-varying
system and hence nonlinear, while the former remains linear time-invariant (LTI). Hence, existing LTI and transfer function
theory applies to a LAM network, which is therefore easy to use for control system design. Validation results show that the
proposed method offers a simple, transparent and accurate multivariable modelling technique for nonlinear systems. |
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Keywords: | Modelling system identification linear approximation model networks evolutionary computation local model networks |
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