Towards Indirect Data-Driven Predictive Control for Heating Phase of Thermoforming Process
<|reference_start|>Towards 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 (MPC) capable of handling temperature constraints and heating-power saturation while delivering enhanced precision, overshoot control, and settling times compared to state-of-the-art methods. We employ a Non-linear Auto-Regressive with Exogenous inputs (NARX) model 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 $2^\circ \mathrm{C}$ and $0.7^\circ \mathrm{C}$ ($7^\circ \mathrm{C}$ and $2^\circ \mathrm{C}$) 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 less than $5.3^\circ \mathrm{C}$ and $1^\circ \mathrm{C}$, respectively.<|reference_end|>
@article{hosseinionari2024towards,
    title={Towards Indirect Data-Driven Predictive Control for Heating Phase of
  Thermoforming Process},
    author={Hadi Hosseinionari, Mohammad Bajelani, Klaske van Heusden, Abbas S.
  Milani, Rudolf Seethaler},
    journal={arXiv preprint arXiv:2407.17013},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17013},
    primaryClass={eess.SY cs.SY}
}