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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.