Neural Horizon Model Predictive Control -- Increasing Computational Efficiency with Neural Networks
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- August 2024
- DOI:
- 10.48550/arXiv.2408.09781
- arXiv:
- arXiv:2408.09781
- Bibcode:
- 2024arXiv240809781A
- Keywords:
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- Electrical Engineering and Systems Science - Systems and Control;
- Computer Science - Artificial Intelligence;
- Computer Science - Machine Learning
- E-Print:
- 6 pages, 4 figures, 4 tables, American Control Conference (ACC) 2024