Adaptive Learning-Based Model Predictive Control for Thickening Processes

This work presents an Adaptive Learning-Based Model Predictive Control (ALB-MPC) framework for a thickening process characterized by high complexity and nonlinear dynamics. The approach leverages operational data to identify an accurate process model as a Nonlinear AutoRegressive eXogenous (NARX) structure, built using a learning method known as Lazily Adaptive Constant Kinky Inference (LACKI). Additionally, a neural network is employed as an online tuning mechanism to adapt the predictive controller parameters and enhance control performance. Simulation results indicate that the proposed control framework achieves performance comparable to or better than controllers with fixed parameters.