Optimal Iterative Learning Control Based on a Time-Parametrized Linear Time-Varying Model for Batch Processes

In this paper, an optimal iterative learning control (ILC) algorithm based on a time-parametrized linear time-varying (LTV) model for batch processes is proposed. Utilizing the repetitive nature of batch processes, a time-parametrized LTV model is used to represent the nonlinear behavior, with its consistence and variance properties established. Furthermore, an optimal ILC algorithm based on the time-parametrized LTV model is developed, and its convergence property is analyzed. Simulations have demonstrated the effectiveness and excellent performance of the proposed method.