Data-Driven Control of Rotational Molding Process

This paper presents a data-driven modeling and control formulation for achieving a desired product quality in a uni-axial rotational molding process. To this end, a data driven state-space model of the process is first identified using experimental data. For a given trajectory of input moves (heater and cold air profiles), this dynamic model is able to predict the evolution of the measured variable (internal product temperature). The dynamic model is augmented with a quality model, which, relates the terminal predictions from the dynamic model to the quality variables (sinkhole area, ultrasonic spectra amplitude, impact test metric and viscosity). The dynamic and quality model are in turn utilized within a model predictive control (MPC) framework to achieve tight quality control for new batches. Experimental results demonstrate the utility of the MPC in achieving improved and tight quality control.