Optimal Energy Shaping via Neural Approximators
Abstract
We introduce optimal energy shaping as an enhancement of classical passivity-based control methods. A promising feature of passivity theory, alongside stability, has traditionally been claimed to be intuitive performance tuning along the execution of a given task. However, a systematic approach to adjust performance within a passive control framework has yet to be developed, as each method relies on few and problem-specific practical insights. Here, we cast the classic energy-shaping control design process in an optimal control framework; once a task-dependent performance metric is defined, an optimal solution is systematically obtained through an iterative procedure relying on neural networks and gradient-based optimization. The proposed method is validated on state-regulation tasks.
Keywords
Cite
@article{arxiv.2101.05537,
title = {Optimal Energy Shaping via Neural Approximators},
author = {Stefano Massaroli and Michael Poli and Federico Califano and Jinkyoo Park and Atsushi Yamashita and Hajime Asama},
journal= {arXiv preprint arXiv:2101.05537},
year = {2021}
}