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Approximate models for nonlinear process control
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DOI：10.1002/aic.690420813.png)
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摘要
En 中文
A methodology is presented to obtain approximate models from input-output data, particularly oriented to implement a model-predictive control scheme. Causal, time-invariant nonlinear discrete systems with a certain type of continuity condition called fading memory are dealt with. To synthesize the nonlinear model a finite-dimensional linear dynamic part (discrete Laguerre polynomials) is used, followed by a nonlinear nonmemory map (single hidden-layer perceptron). Results of the application to approximate and control a binary distillation column are presented.
Keyword:
NON-LINEAR SYSTEMS
NEURAL NETWORKS
PREDICTIVE CONTROL
IDENTIFICATION
OPTIMIZATION
SERIES
BOUNDS
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期刊
IF:
4
论文数:
1.1W
被引数:
2.9W
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