TY - UNPB
T1 - Optimal energy shaping via neural approximators
AU - Massaroli, S.
AU - Poli, M.
AU - Califano, F.
AU - Park, J.
AU - Yamashita, A.
AU - Asama, H.
PY - 2021/1/14
Y1 - 2021/1/14
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85170467062&partnerID=MN8TOARS
U2 - 10.48550/arxiv.2101.05537
DO - 10.48550/arxiv.2101.05537
M3 - Preprint
BT - Optimal energy shaping via neural approximators
ER -