@inproceedings{ddcc5cc1eb2e4d3f97e0a1b8bc9a7a16,
title = "Self-triggered Predictive Gait Tracking Control for Footed Robot",
abstract = "In this paper, a self-triggered model predictive control algorithm is proposed based on the gait tracking model of a footed robot considering the bounded disturbances. The feasibility and stability of the algorithm are analyzed, and the comparison with the traditional model predictive control based on time series quadratic programming is also presented. On the premise of ensuring the stability and accuracy of the algorithm, it effectively reduces the computational complexity during the online optimiazation process. At the end of this paper, the simulation results of gait tracking are given to verify the effectiveness of the algorithm.",
keywords = "Gait tracking, Model predictive control, Self-triggered",
author = "Hao Zhou and Xuemei Ren",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 17th Chinese Intelligent Systems Conference, CISC 2021 ; Conference date: 16-10-2021 Through 17-10-2021",
year = "2022",
doi = "10.1007/978-981-16-6320-8_18",
language = "English",
isbn = "9789811663192",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "173--183",
editor = "Yingmin Jia and Weicun Zhang and Yongling Fu and Zhiyuan Yu and Song Zheng",
booktitle = "Proceedings of 2021 Chinese Intelligent Systems Conference",
address = "Germany",
}