We propose a novel control technique for batch and semibatch processes. The main novelty of the technique lies in that it can accommodate several different aspects of industrial batch process control, such as the following of prespecified reference trajectories, the satisfaction of process constraints, and the meeting of product quality targets, altogether in a single framework. This; simplifies the design and enables the user to make a systematic trade-off among the different objectives. Another novelty is the feature of iterative learning that allows the controller to use the information collected in the previous batches. This is done by establishing a batch-to-batch correlation model giving the integral control action with respect to the batch index. This feature allows the performance to be improved gradually as the batch run is repeated and makes it possible to attain precise control despite significant model errors and disturbances. The performance of the proposed technique is illustrated using a simple nonlinear semibatch reactor model.