Abstract
Batch processes have been increasingly used in the production of low volume and high value added products. Consequently, optimization control in batch processes is crucial in order to derive the maximum benefit. In this paper, a run-to-run product quality control based on iterative learning optimization control is developed. Moreover, a rigorous theorem is proposed and proven in this paper, which states that the tracking error under the optimal iterative learning control (ILC) law can converge to zero. In this paper, a typical nonlinear batch continuous stirred tank reactor (CSTR) is considered, and the results show that the performance of trajectory tracking is gradually improved by the ILC.
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