Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes
<|reference_start|>Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes: Koopman-based modeling and model predictive control have been a promising alternative for optimal control of nonlinear processes. Good Koopman modeling performance significantly depends on an appropriate nonlinear mapping from the original state-space to a lifted state space. In this work, we propose an input-augmented Koopman modeling and model predictive control approach. Both the states and the known inputs are lifted using two deep neural networks (DNNs), and a Koopman model with nonlinearity in inputs is trained within the higher-dimensional state-space. A Koopman-based model predictive control problem is formulated. To bypass non-convex optimization induced by the nonlinearity in the Koopman model, we further present an iterative implementation algorithm, which approximates the optimal control input via solving a convex optimization problem iteratively. The proposed method is applied to a chemical process and a biological water treatment process via simulations. The efficacy and advantages of the proposed modeling and control approach are demonstrated.<|reference_end|>
@article{li2024machine,
    title={Machine learning-based input-augmented Koopman modeling and predictive
  control of nonlinear processes},
    author={Zhaoyang Li, Minghao Han, Dat-Nguyen Vo, Xunyuan Yin},
    journal={arXiv preprint arXiv:2408.02315},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02315},
    primaryClass={eess.SY cs.SY}
}