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Model-Based Iterative Learning Control for Batch Processes Using Generalized Hinging Hyperplanes
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DOI：10.1021/ie201842a.png)
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Abstract
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A model-based iterative learning control (ILC) strategy using the generalized hinging hyperplanes (GHH) is proposed to track the product quality trajectory in the batch process. As an empirical model with piecewise affine basis functions, GHH is very suitable for constructing the dynamic model of batch processes, in which its gradient information can be easily obtained due to the structure of GHH model. Based on the GHH, a quadratic-criterion-based ILC (Q-ILC) algorithm is constructed, where the input trajectory for the next batch is updated by ILC law and the output tracking error can be gradually reduced from batch to batch. The proposed strategy is demonstrated on a simulated typical batch reactor and compared with the method based on neural networks. The simulation results show the convergence of the output tracking error and the robustness of the proposed method under model plant mismatches and unknown disturbances.
Keywords:
PREDICTIVE CONTROL
IDENTIFICATION
STRATEGY
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