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.

No abstract

An optimal iterative learning control (ILC) technique based on a quadratic optimal criterion has been implemented and evaluated in an experimental rapid thermal processing (RTP) system fabricating 8-inch silicon wafers. The control technique is based on a time-varying linear state space model which approximates a nonlinear system along a reference trajectory. Also the control technique is able to make improvements in the control performance from one run to next and eventually converge to a minimum achievable tracking error despite model error. Through a series of experiments with wafers on which thermocouples are glued, it was observed that the wafer temperatures are steered to the reference trajectory reducing the diyerences overcoming various disturbances.

No abstract