Towards Indirect Data-Driven Predictive Control for Heating Phase of Thermoforming Process
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arXiv
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| Format: | Preprint |
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2024
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| author | Hosseinionari, Hadi Bajelani, Mohammad van Heusden, Klaske Milani, Abbas S. Seethaler, Rudolf |
| author_facet | Hosseinionari, Hadi Bajelani, Mohammad van Heusden, Klaske Milani, Abbas S. Seethaler, Rudolf |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17013 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Indirect Data-Driven Predictive Control for Heating Phase of Thermoforming Process Hosseinionari, Hadi Bajelani, Mohammad van Heusden, Klaske Milani, Abbas S. Seethaler, Rudolf Systems and Control 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. |
| title | Towards Indirect Data-Driven Predictive Control for Heating Phase of Thermoforming Process |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2407.17013 |