Abstract
In this paper, the problem of parameter estimation of nonlinear unmanned vehicle systems is studied. By introducing an extended state to model the unknown parameters, the parameter estimation is realized by designing the extended state observer (ESO), and the influence of noise is tackled through extended Kalman filter (EKF). The observability is analyzed, and simulation example shows the effectiveness of the proposed parameter estimation method.
Original language | English |
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Title of host publication | Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control - Swarm Perception and Navigation Technologies |
Editors | Jianglong Yu, Qingdong Li, Yumeng Liu |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 469-476 |
Number of pages | 8 |
ISBN (Print) | 9789819733316 |
DOIs | |
Publication status | Published - 2024 |
Event | 7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023 - Nanjing, China Duration: 24 Nov 2023 → 27 Nov 2023 |
Publication series
Name | Lecture Notes in Electrical Engineering |
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Volume | 1206 LNEE |
ISSN (Print) | 1876-1100 |
ISSN (Electronic) | 1876-1119 |
Conference
Conference | 7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023 |
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Country/Territory | China |
City | Nanjing |
Period | 24/11/23 → 27/11/23 |
Keywords
- Unmanned vehicle system
- extended Kalman filter
- parameter estimation
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Huang, S., Chao, C., Huang, J., & Lv, Y. (2024). Parameter Estimation of Unmanned Vehicle Based on ESO and EKF Algorithm. In J. Yu, Q. Li, & Y. Liu (Eds.), Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control - Swarm Perception and Navigation Technologies (pp. 469-476). (Lecture Notes in Electrical Engineering; Vol. 1206 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-97-3332-3_42