@inproceedings{ff016bd3c22946cc86a1d0592caba510,
title = "Direct Numerical Solution of the LQR with Input Derivative Regularization Problem",
abstract = "This paper develops a new method for computing the state feedback gain of a Linear Quadratic Regulator (LQR) with input derivative weighting that circumvents solving the Riccati equation. The additional penalty on the derivatives of the input introduces intuitively tunable weights and enables smoother control characteristics without the need of model extension. This is motivated by position controlled mechanical systems. The physical limitations of these systems are usually their velocity and acceleration rather than the position itself. The presented algorithm is based on a discretization approach to the calculus of variations and translating the original problem into a least-squares with equality constraints problem. The control performance is analyzed using a laboratory setup of an underactuated crane-like system.",
keywords = "Calculus of variations, Input derivative weighting, Linear quadratic regulator, Model reduction, Numerical methods for optimal control",
author = "Johannes Handler and Matthew Harker and Gerhard Rath",
note = "Publisher Copyright: Copyright {\textcopyright} 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/); 22nd IFAC World Congress ; Conference date: 09-07-2023 Through 14-07-2023",
year = "2023",
month = jul,
day = "1",
doi = "10.1016/j.ifacol.2023.10.1253",
language = "English",
series = "IFAC-PapersOnLine",
publisher = "Elsevier B.V.",
number = "2",
pages = "4846--4851",
editor = "Hideaki Ishii and Yoshio Ebihara and Jun-ichi Imura and Masaki Yamakita",
booktitle = "IFAC-PapersOnLine",
edition = "2",
}