Neural Horizon Model Predictive Control -- Increasing Computational Efficiency with Neural Networks
This addresses computational bottlenecks in MPC for robotics and embedded systems, though it appears incremental as it builds on existing MPC frameworks with neural network enhancements.
The paper tackles the computational inefficiency of model predictive control (MPC) for fast or low-power applications by using a neural network to approximate part of the problem horizon, reducing online computation while maintaining safety guarantees and near-optimal performance in simulations.
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.