This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method ...This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.展开更多
Aim To predict the indexes of quality of the thermal elastomer by polymerization process data. Methods Neural networks were used for learning the relationship between the product quality and the polymerization proce...Aim To predict the indexes of quality of the thermal elastomer by polymerization process data. Methods Neural networks were used for learning the relationship between the product quality and the polymerization process condition variables in an industrial scale batch polymerization reactor. Results The indexes of quality of the product were inferred with acceptable accuracy from easy to measure reaction process condition variables. Conclusion The method proposed in this paper provides on line soft sensors of the indexes of quality of the thermal elastomal.展开更多
基金Projects(61573052,61273132)supported by the National Natural Science Foundation of China
文摘This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.
文摘Aim To predict the indexes of quality of the thermal elastomer by polymerization process data. Methods Neural networks were used for learning the relationship between the product quality and the polymerization process condition variables in an industrial scale batch polymerization reactor. Results The indexes of quality of the product were inferred with acceptable accuracy from easy to measure reaction process condition variables. Conclusion The method proposed in this paper provides on line soft sensors of the indexes of quality of the thermal elastomal.
