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Grouped neural network model-predictive control
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DOI：10.1016/S0967-0661(02)00184-3.png)
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Abstract
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This work provides experimental demonstration for a previously proposed parallel model structure for general nonlinear model-predictive control (NMPC). The model comprises of a group of sub-models, each providing prediction of one process output at one selected future point in time. The sub-models are mutually independent and therefore can run in parallel. This work uses neural networks (NNs) for each sub-model, and terms the prediction model as a grouped neural network (GNN). NMPC based on the GNN model is referred to as GNNMPC. This work demonstrates implementation of GNNMPC on a nonlinear, multivariable, constrained pilot-scale distillation unit. (C) 2002 Elsevier Science Ltd. All rights reserved.
Keywords:
experimental
nonlinear
model-predictive
constrained
distillation
control
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