Publication detail

Weight-varying Model Predictive Control for Coupled Cyber-Physical Systems: Aerial Grasping Study

NOVÁK, J. CHUDÝ, P. HANÁK, J.

Original Title

Weight-varying Model Predictive Control for Coupled Cyber-Physical Systems: Aerial Grasping Study

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

Advances in numerical optimization methods have enabled utilization of Nonlinear Model Predictive Control (NMPC) for increasingly complex cyber-physical systems. Tasks requiring interaction of physically connected or unconnected multi-degree of freedom dynamical systems are termed as coupled since they require mutual coordination to achieve a desired goal. This paper is focused on studying an aerial grasping problem composed of an Unmanned Aerial Vehicle (UAV) and a robotic manipulator designed for tasks such as transportation or infrastructure repair. A weight-varying approach based on Reinforecement Learning (RL) is proposed to learn a policy adapting the objective parametrization online based on a set of specified features. Considering that computing the control input actions is streamlined to a proven nonlinear optimization solver, the learning process takes less computational resources compared to learning a policy directly at the control input level.

Keywords

Model Predictive Control, Unmanned Aerial Vehicle, Reinforcement Learning

Authors

NOVÁK, J.; CHUDÝ, P.; HANÁK, J.

Released

28. 9. 2024

Location

Castiglione della Pescaia

Pages from

1

Pages to

15

Pages count

15

BibTex

@inproceedings{BUT189120,
  author="Jiří {Novák} and Peter {Chudý} and Jiří {Hanák}",
  title="Weight-varying Model Predictive Control for Coupled Cyber-Physical Systems: Aerial Grasping Study",
  booktitle="Machine Learning, Optimization, and Data Science",
  year="2024",
  series="Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  pages="1--15",
  address="Castiglione della Pescaia"
}