Flexible Closed-Loop Iterative Learning Control for Industrial Batch Processes With State Delay and Time-Varying Uncertainties

Abstract

A robust closed-loop iterative learning control (ILC) method is proposed for industrial batch processes with state delay and time-varying uncertainties from cycle to cycle. Based on a two-dimensional (2D) system description of such a batch process, a closed-loop ILC scheme consisting of dynamic output feedback plus feedforward control is established to realize robust tracking of the setpoint trajectory in both the time (during a cycle) and batch (from cycle to cycle) directions. Only measured output errors of current and previous cycles are used for control design to facilitate practical implementation. A flexible 2D difference Lyapunov function that guarantees monotonical state energy decrease in both the time and batch directions is introduced to establish a sufficient condition in terms of the linear matrix inequality (LMI) for holding robust stability of the closed-loop ILC system. Correspondingly, the ILC controller can be explicitly formulated, together with an adjustable robust H infinity performance level. An illustrative example is given to demonstrate effectiveness and merits of the proposed ILC method.