Neural Horizon Model Predictive Control -- Increasing Computational Efficiency with Neural Networks
<|reference_start|>Neural Horizon Model Predictive Control -- Increasing Computational Efficiency with Neural Networks: The expansion in automation of increasingly fast applications and low-power edge devices poses a particular challenge for optimization based control algorithms, like model predictive control. Our proposed machine-learning supported approach addresses this by utilizing a feed-forward neural network to reduce the computation load of the online-optimization. We propose approximating part of the problem horizon, while maintaining safety guarantees -- constraint satisfaction -- via the remaining optimization part of the controller. The approach is validated in simulation, demonstrating an improvement in computational efficiency, while maintaining guarantees and near-optimal performance. The proposed MPC scheme can be applied to a wide range of applications, including those requiring a rapid control response, such as robotics and embedded applications with limited computational resources.<|reference_end|>
@article{alsmeier2024neural,
    title={Neural Horizon Model Predictive Control -- Increasing Computational
  Efficiency with Neural Networks},
    author={Hendrik Alsmeier, Anton Savchenko, Rolf Findeisen},
    journal={arXiv preprint arXiv:2408.09781},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09781},
    primaryClass={eess.SY cs.AI cs.LG cs.SY}
}