Integrating Iterative Learning Estimation with Optimal Control for Batch Productivity Enhancement

Optimal control has wide applications for the control of batch and semi-batch processes to develop an optimum control input policy by extremizing a performance measure. The deployment of optimal control relies heavily on the accuracy of the process models being used for computation of the optimal profile. Often, the process models do not replicate the plants due to various shortcomings such as assumptions made during model formulations, poor first principles knowledge and limited range of experimental data due to short process development cycles. Moreover, scale-up of the processes from lab to manufacturing scale renders the developed models obsolete. The estimated model parameters can significantly differ from their nominal values which calls for the development of a strategy that updates process models so as to achieve an improved and tight control of batch processes. In this paper, we propose a novel methodology based on iterative learning to gradually update models using on-line measurement data at the end of each successive batch run by minimizing the error between plant and model data. In the proposed methodology, we further integrate Iterative Learning Estimation (ILE) with optimal control to update the optimal control input profile with the advent of measurement after each successive batch run. An important aspect of this integration is to ensure that model updates between batch runs generate feasible optimal control trajectories. Simulations are performed for the temperature control of a batch reactor system to validate the proposed methodology.