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

TL;DR

This paper introduces a neural network-accelerated model predictive control method that reduces computational load while ensuring safety and near-optimal performance, suitable for fast, resource-constrained applications.

Contribution

It proposes a novel neural network-based approximation within MPC that maintains safety guarantees and improves computational efficiency in real-time control scenarios.

Findings

Demonstrates improved computational efficiency in simulation

Maintains safety guarantees and near-optimal control performance

Applicable to robotics and embedded systems with limited resources

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.