Neural Horizon Model Predictive Control -- Increasing Computational Efficiency with Neural Networks

• URL: http://arxiv.org/abs/2408.09781v1
• Date: Mon, 19 Aug 2024 08:13:37 GMT
• Title: Neural Horizon Model Predictive Control -- Increasing Computational Efficiency with Neural Networks
• Authors: Hendrik Alsmeier, Anton Savchenko, Rolf Findeisen,
• Abstract summary: We propose a proposed machine-learning supported approach to model predictive control. We propose approximating part of the problem horizon, while maintaining safety guarantees. The proposed MPC scheme can be applied to a wide range of applications, including those requiring a rapid control response.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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