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.

本研究针对模型预测控制算法在快速应用和低功耗边缘设备中面临的优化挑战，提出了一种基于机器学习的解决方案。通过使用前馈神经网络减少在线优化的计算负担，同时确保安全性和约束满足，该方法在模拟中验证了计算效率的提升，并保持近最佳性能。这一方案可广泛应用于机器人和嵌入式等需快速响应的场景。

神经水平模型预测控制 -- 通过神经网络增加计算效率