تقرير
Neural Horizon Model Predictive Control -- Increasing Computational Efficiency with Neural Networks
| العنوان: | Neural Horizon Model Predictive Control -- Increasing Computational Efficiency with Neural Networks |
|---|---|
| المؤلفون: | Alsmeier, Hendrik, Savchenko, Anton, Findeisen, Rolf |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Systems and Control, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | 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. Comment: 6 pages, 4 figures, 4 tables, American Control Conference (ACC) 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2408.09781 |
| رقم الأكسشن: | edsarx.2408.09781 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |