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A novel neuro-fuzzy model-based run-to-run control for batch processes with uncertainties

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4 Author(s)
Jia Li ; Dept. of Autom., Shanghai Univ., Shanghai, China ; Shi Jiping ; Song Yang ; Chiu Min-Sen

In this paper, a run-to-run control with neuro-fuzzy model updating mechanism is developed. This strategy features the ability to learn from previous batches to obtain iteratively the optimal control profile and adjust the neuro-fuzzy model parameters. In addition, an updating algorithm guaranteeing the global convergence of the weights of the model is developed based on the Lyapunov approach. As a result, model uncertainties can be handled. Simulation results show that by updating the model from batch to batch, the control profile converges to the corresponding suboptimal one in the subsequent batches.

Published in:

Control and Decision Conference, 2009. CCDC '09. Chinese

Date of Conference:

17-19 June 2009

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