Neue Veröffentlichung in IEEE Conferences „Neural Horizon Model Predictive Control – Increasing Computational Efficiency with Neural Networks“
Hendrik Alsmeier, Anton Savchenko, Rolf Findeisen
24.09.2024
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 net-work 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.
