Just-in-Time-Learning based Extended Prediction Self-Adaptive Control for batch processes

• A new shrinking horizon nonlinear model predictive control (NMPC) for batch-end product quality is developed.
• Local state-space models obtained by the Just-in-Time Learning technique pave the foundation for the proposed NMPC design.
• Better control performance for batch process was achieved and illustrated through simulation studies.

This article presents a new Extended Prediction Self-Adaptive Control (EPSAC) algorithm based on the Just-in-Time Learning (JITL) method. In the proposed JITL-based EPSAC design, linearization of the process model is achieved by a set of local state-space models, each of which can be independently and simultaneously identified by the JITL method along the base trajectory. For the end-product quality control for a simulated semi-batch pH-shift reactive crystallization process where shrinking prediction and control horizons are essential, the proposed EPSAC algorithm not only simplifies the control weight tuning but also provides better and more robust closed-loop control performance than its previous counterpart.