Real-Time Neural MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms
Technical University of Munich · University of Zurich · +2 more institutions
Abstract
Model Predictive Control (MPC) has become a popular framework in embedded control for high-performance autonomous systems. However, to achieve good control performance using MPC, an accurate dynamics model is key. To maintain real-time operation, the dynamics models used on embedded systems have been limited to simple first-principle models, which substantially limits their representative power. In contrast to such simple models, machine learning approaches, specifically neural networks, have been shown to accurately model even complex dynamic effects, but their large computational complexity hindered combination with fast real-time iteration loops. With this work, we present Real-time Neural MPC , a framework…
Citation impact
- FWCI
- 32.27
- Percentile
- 100%
- References
- 37
Authors
6Topics & keywords
- Model predictive control
- Computer science
- Artificial neural network
- Leverage (statistics)
- Control engineering
- Pipeline (software)
- Artificial intelligence
- Parametric statistics