In this Chapter, two PI-based ILCIterative Learning Control (ILC) schemes are proposed for industrial batch processesBatch process subject to time-varyingTime-varying uncertainties. An important merit of the proposed schemes is that the PI controller designController design in the inner loopInner loop is independent of the outer loopOuter loop ILC design. To accommodate for time-varying process uncertaintiesProcess uncertainties and disturbance, a robust PI controller is designed by assigning the desired pole location of the closed-loop systemClosed-loop system. In contrast, another model-freeModel-free PI design is presented for application to real systems with unknown dynamicsUnknown dynamics. For realizing batch optimizationBatch optimization, robust indirect-typeIterative Learning Control (ILC) ILCIndirect-type ILC updating laws are established to regulate the set-point commandSet-point command of the resulting closed-loop systemsClosed-loop system. Based on a 2D Roesser or FM model description of the batch process dynamics, robust stabilityRobust stability of the resulting 2D closed-loop learning system is rigorously analyzed, and the sufficient conditions in terms of LMI are established, respectively. An illustrative example of injection moldingInjection molding machine is used to validate the effectiveness and advantages of the proposed methods.