NEURAL MODEL PREDICTIVE CONTROL FOR NONLINEAR CHEMICAL PROCESSES

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概要

• 論文の詳細を見る

• 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 a 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 (NMPC) shows good performance and robustness.

• 社団法人 化学工学会の論文
• 1993-08-20

著者

• Song Jeong

  Process Systems Laboratory Dept. Of Chem. Eng. Korea Advanced Institute Of Science & Technology

• PARK Sunwon

  Process Systems Laboratory, Dept. of Chem. Eng., Korea Advanced Institute of Science & Technology

• Park Sunwon

  Process Systems Laboratory Dept. Of Chem. Eng. Korea Advanced Institute Of Science & Technology

関連論文

• NEURAL MODEL PREDICTIVE CONTROL FOR NONLINEAR CHEMICAL PROCESSES
• EXPERIMENTAL STUDY OF BATCH ESTERIFICATION CONVERSION CONTROL USING CONDUCTANCE MEASUREMENT

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