TY - GEN
T1 - Parameter Estimation of Unmanned Vehicle Based on ESO and EKF Algorithm
AU - Huang, Shengchao
AU - Chao, Chengke
AU - Huang, Jiazhu
AU - Lv, Yuezu
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Unmanned vehicle system
KW - extended Kalman filter
KW - parameter estimation
UR - http://www.scopus.com/inward/record.url?scp=85199414217&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-3332-3_42
DO - 10.1007/978-981-97-3332-3_42
M3 - Conference contribution
AN - SCOPUS:85199414217
SN - 9789819733316
T3 - Lecture Notes in Electrical Engineering
SP - 469
EP - 476
BT - Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control - Swarm Perception and Navigation Technologies
A2 - Yu, Jianglong
A2 - Li, Qingdong
A2 - Liu, Yumeng
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023
Y2 - 24 November 2023 through 27 November 2023
ER -