한국지능시스템학회:학술대회논문집 (Proceedings of the Korean Institute of Intelligent Systems Conference)
- 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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- Pages.899-902
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- 1993
Neural Model Predictive Control for Nonlinear Chemical Processes
- Song, Jeong-Jun (Process Systems Laboratory, Dept. of Chem. Eng, Korea Advanced Institute of Science & Technololgy) ;
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Park, Sunwon
(Process Systems Laboratory, Dept. of Chem. Eng, Korea Advanced Institute of Science & Technololgy)
- 발행 : 1993.06.01
초록
A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming combined with neural identification network is used to generate the optimum control law for complex continuous chemical reactor systems that have inherent nonlinear dynamics. The neural model predictive controller (MNPC) shows good performances and robustness. To whom all correspondence should be addressed.
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