Toward Indirect Data-Driven Predictive Control for Heating Phase of Thermoforming Process

Shaping thermoplastic sheets into three-dimensional products is challenging since overheating results in failed manufactured parts and wasted material. To this end, we propose an indirect data-driven predictive control approach using Model Predictive Control capable of handling temperature constraints and heating-power saturation while delivering enhanced precision and overshoot control compared to state-of-the-art methods. We employ a Non-linear Auto-Regressive with Exogenous inputs model, which is linearized to define a linear control-oriented model at each operating point. Using a high-fidelity simulator, several simulation studies have been conducted to evaluate the proposed method’s robustness and performance under parametric uncertainty, indicating overshoot and average steady-state error less than 2°C and 0.9°C (7°C and 2°C) for the nominal (worst-case) scenario. Finally, we applied the proposed method to a lab-scale thermoforming platform, resulting in a close response to the simulation analysis with overshoot and average steady-state error metrics of 5.3°C and 1°C, respectively.