@inbook{0be09e73d4ed4c7e9f68fbc3ad8ba213,
title = "Data-Driven Design of Energy-Shaping Controllers for Swing-Up Control of Underactuated Robots",
abstract = "We propose a novel data-driven procedure to train a neural network for the swing-up control of underactuated robotic systems. Our approach is inspired by several recent developments ranging from nonlinear control theory to machine learning. We embed a neural network indirectly into the equations of motion of the robotic manipulator as its control input. Using familiar results from passivity-based and energy-shaping control literature, this control function is determined by the appropriate gradients of a neural network, acting as an energy-like (Lyapunov) function. We encode the task of swinging-up robotic systems through the use of transverse coordinates and goal sets; which drastically accelerates the rate of learning by providing a concise target for the neural network. We demonstrate the efficacy of the algorithm with both numerical simulations and experiments.",
author = "Wankun Sirichotiyakul and Satici, {Aykut C.}",
note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2021",
doi = "10.1007/978-3-030-71151-1_29",
language = "American English",
series = "Springer Proceedings in Advanced Robotics",
publisher = "Springer Science and Business Media B.V.",
pages = "323--333",
booktitle = "ISER 2020: Experimental Robotics",
address = "Netherlands",
}