Abstract In order to guarantee the control performance of the batch processes when uncertainties and disturbances exist, a neuro-fuzzy model (NFM) predictive controller based on batch-wise model modification is developed. Model modification along batch-axis is used to improve the accuracy of neuro-fuzzy model, and predictive control along time-axis can guarantee the optimal control consequence, which lead to superior tracking performance and better robustness compared with the conventional quadratic criterion based iterative learning control (Q-ILC) approach. An illustrative example is presented to verify the effectiveness of the investigated approach.
Batch-Wise Updating Neuro-Fuzzy Model Based Predictive Control for Batch Processes
2014-01-01
10 pages
Article/Chapter (Book)
Electronic Resource
English
batch processes , neuro-fuzzy system , model predictive control , batch-to-batch model modification Computer Science , Artificial Intelligence (incl. Robotics) , Computational Intelligence , Computer Imaging, Vision, Pattern Recognition and Graphics , Simulation and Modeling , System Performance and Evaluation
Batch-Wise Updating Neuro-Fuzzy Model Based Predictive Control for Batch Processes
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