@inproceedings{5da48de6cf434a16a973d8768442cf8e,
title = "A differential particle scheme and its application to PID parameter tuning of an inverted pendulum",
abstract = "Gradient-free stochastic optimization algorithms are well-known for finding suitable parameter configurations over independent runs ubiquitously. Attaining low variability of convergence performance through independent runs is crucial to allow further generalization over distinct problem domains. This paper investigates the performance of a differential particle system in stabilizing a nonlinear inverted pendulum under diverse and challenging initial conditions. Compared to the relevant algorithms in the literature, our experiments show the feasibility of achieving lower convergence variability to stabilize a nonlinear pendulum over independent runs and initial conditions within a reasonable computational load. ",
keywords = "PID tunning, differential evolution, inverted pendulum, nonlinear control, optimization, particle swarm",
author = "Victor Parque",
note = "Funding Information: This research was supported by JSPS KAKENHI 20K11998. Publisher Copyright: {\textcopyright} 2021 ACM.; 2021 Genetic and Evolutionary Computation Conference, GECCO 2021 ; Conference date: 10-07-2021 Through 14-07-2021",
year = "2021",
month = jul,
day = "7",
doi = "10.1145/3449726.3463225",
language = "English",
series = "GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion",
publisher = "Association for Computing Machinery, Inc",
pages = "1937--1943",
booktitle = "GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion",
}