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An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system

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Abstract

Advanced model-based control strategies, e.g., model predictive control, can offer superior control of key process variables for multiple-input multiple-output systems. The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization. This work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive control. To showcase this approach, five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor system. This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges. These controllers also had reasonable per-iteration times of ca. 0.1 s. This demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which, in the face of process uncertainties or modelling limitations, allow rapid and stable control over wider operating ranges.

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Acknowledgements

The authors thank the MOE AcRF Grant in Singapore for financial support of the projects on Precision Healthcare Development, Manufacturing and Supply Chain Optimization (Grant No. R-279-000-513-133) and Advanced Process Control and Machine Learning Methods for Enhanced Continuous Manufacturing of Pharmaceutical Products (Grant No. R-279-000-541-114).

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Authors and Affiliations

  1. Department of Chemical & Biomolecular Engineering, Faculty of Engineering, National University of Singapore, Singapore, 117585, Singapore

    Ewan Chee & **aonan Wang

  2. Chemical Engineering & Food Technology Cluster, Singapore Institute of Technology, Singapore, 138683, Singapore

    Wee Chin Wong

Authors
  1. Ewan Chee
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  2. Wee Chin Wong
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  3. **aonan Wang
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Corresponding author

Correspondence to **aonan Wang.

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Code Availability Statement

Access to the GitHub repository containing the source code for this project is available upon request.

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11705_2021_2058_MOESM1_ESM.pdf

An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system

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Chee, E., Wong, W.C. & Wang, X. An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system. Front. Chem. Sci. Eng. 16, 237–250 (2022). https://doi.org/10.1007/s11705-021-2058-6

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  • DOI: https://doi.org/10.1007/s11705-021-2058-6

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