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On sampled-data models for nonlinear systems
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DOI：10.1109/TAC.2005.856640.png)
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Abstract
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Models for deterministic continuous-time nonlinear systems typically take the form of ordinary differential equations. To utilize these models in practice invariably requires discretization. In this paper, we show how an approximate sampled-data model can be obtained for deterministic nonlinear systems such that the local truncation error between the output of this model and the true system is of order Delta(r+1), where A is the sampling period and r is the system relative degree. The resulting model includes extra zero dynamics which have no counterpart in the underlying continuous-time system. The ideas presented here generalize well-known results for the linear case. We also explore the implications of these results in nonlinear system identification.
Keywords:
nonlinear systems
sampled-data models
sampling zeros
system identification
zero dynamics
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7
Papers:
1.3W
Citations:
6.7W
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