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, neural networks can model complex effects purely from data. In contrast to such simple models, machine learning approaches such as 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 Neural-MPC, a framework to efficiently integrate large, complex neural network architectures as dynamics models within a model-predictive control pipeline. Our experiments, performed in simulation and the real world on a highly agile quadrotor platform, demonstrate up to 83% reduction in positional tracking error when compared to state-of-the-art MPC approaches without neural network dynamics.
Abstract (translated)
URL
https://arxiv.org/abs/2203.07747
