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.


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