Online Synchronization in Latent Variable Model Predictive Control for Trajectory Tracking of an Uneven Batch Process

The performance of the batch trajectory tracking algorithm is affected by batch-to-batch variation, which includes irregular phase transitions and batch durations. The latent variable model predictive control is modified to obtain the desired specification by applying an online alignment to assign the prediction model and update the future reference. One of the multiple models is selected at every time step to improve the prediction performance using the relationship between the accumulated measurements of the ongoing batch. The future reference is synchronized based on the current measurement of the ongoing batch; therefore, a partially reduced or extended reference trajectory is adaptively applied to the batches. Compared with the conventional latent variable model predictive control algorithm, the proposed method for assigning a prediction model can improve the trajectory tracking per…