Adaptive Iterative Learning Control for Industry Batch Process with Time-Varying and Unknown Parameters

Abstract

The batch process is a typical manufacturing mode in industry. In this article, an adaptive ILC method is proposed for the batch process with time-varying and unknown parameters. The proposed method involves merging an adaptive updating law that utilizes the steepest descent method to estimate unknown parameters with a controller that adjusts the estimated system. The proposed condition ensures that the estimated parameter error remains bounded and that the estimated state error is stabilized. The controller utilizes the estimated results to steer the estimated system to track the reference trajectory. A numerical experiment is presented to demonstrate the efficiency of the proposed method.