Reduced Neural Model Predictive Control Strategies for a Class of Chemical Reactors
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概要
- 論文の詳細を見る
A simple model predictive control strategy based on reduced feedforward neural network (FNN) models is proposed. Under some physical constraint conditions, the short-prediction-horizon predictive control algorithm can carry out the offset-free performance for a class of nonlinear systems with input/output multiplicities. The main issue is to specify the input/output patterns for neural network architecture, and a stable, minimum-phase mode is added to reduce control structures that involve off-line identification algorithms and graphic-based determination. Finally, three examples of chemical reactors exhibiting unstable or nonminimum-phase dynamic behaviors are demonstrated to verify the proposed control scheme.
- 2007-05-01
著者
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WU Wei
Department of Applied Physics and Chemistry, University of Electro-Communications
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Wu Wei
Dep. Of Chemical And Materials Engineering National Yunlin Univ. Of Sci. And Technol.
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Wu Chia-ju
Department Of Electrical Engineering National Yunlin University Of Science And Technology
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CHANG Jun-Xian
Department of Electrical Engineering, National Yunlin University of Science and Technology
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HSU Wei-Ching
Department of Chemical Engineering, National Yunlin University of Science and Technology
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Hsu Wei-ching
Department Of Chemical Engineering National Yunlin University Of Science And Technology
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Chang Jun-xian
Department Of Electrical Engineering National Yunlin University Of Science And Technology
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Wu Wei
Department Of Chemical Engineering National Yunlin University Of Science And Technology
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