TY - JOUR
T1 - Adaptive integral terminal sliding mode control of unmanned bicycle via ELM and barrier function
AU - Chen, Long
AU - Jin, Zhihui
AU - Shao, Ke
AU - Wang, Guangyi
AU - He, Shuping
AU - Stojanovic, Vladimir
AU - Bahri, Parisa Arabzadeh
AU - Wang, Hai
N1 - Publisher Copyright:
© The Author(s), 2024.
PY - 2024/8/1
Y1 - 2024/8/1
N2 - In this paper, an unmanned bicycle (UB) with a reaction wheel is designed, and a second-order mathematical model with uncertainty is established. In order to achieve excellent balancing performance of the UB system, an adaptive controller is designed, which is composed of nominal feedback control, compensating control using extreme learning machine observer and reaching control via integral terminal sliding mode (ITSM) and barrier function (BF)-based adaptive law. Owing to the features of BF-based ITSM (BFITSM), not only any uncertainty or disturbance upper bound is not needed any longer but also the finite-Time convergence of the closed-loop system can be ensured with a predefined error bound. Moreover, the BF-based control gain can be adaptively adjusted according to the update of the lumped uncertainty such that the overestimation is removed. The stability analysis of the closed-loop system is given according to Lyapunov theory. Comparable experimental results on an actual UB are carried out to validate the superior balancing performance of the proposed controller.
AB - In this paper, an unmanned bicycle (UB) with a reaction wheel is designed, and a second-order mathematical model with uncertainty is established. In order to achieve excellent balancing performance of the UB system, an adaptive controller is designed, which is composed of nominal feedback control, compensating control using extreme learning machine observer and reaching control via integral terminal sliding mode (ITSM) and barrier function (BF)-based adaptive law. Owing to the features of BF-based ITSM (BFITSM), not only any uncertainty or disturbance upper bound is not needed any longer but also the finite-Time convergence of the closed-loop system can be ensured with a predefined error bound. Moreover, the BF-based control gain can be adaptively adjusted according to the update of the lumped uncertainty such that the overestimation is removed. The stability analysis of the closed-loop system is given according to Lyapunov theory. Comparable experimental results on an actual UB are carried out to validate the superior balancing performance of the proposed controller.
KW - balancing barrier function (BF)
KW - extreme learning machine (ELM)
KW - integral terminal sliding mode (ITSM)
KW - unmanned bicycle (UB)
UR - http://www.scopus.com/inward/record.url?scp=85204083487&partnerID=8YFLogxK
U2 - 10.1017/S0263574724000997
DO - 10.1017/S0263574724000997
M3 - 文章
AN - SCOPUS:85204083487
SN - 0263-5747
VL - 42
SP - 2635
EP - 2657
JO - Robotica
JF - Robotica
IS - 8
ER -