TY - GEN
T1 - Neural Network Based Singularity-Free Adaptive Prescribed Performance Control of Two-Mass Systems
AU - Zheng, Dongdong
AU - Sun, Zeyuan
AU - Li, Weixing
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - This paper focuses on the trajectory tracking control problem of two-mass systems, addressing the challenges posed by unknown system dynamics and unknown control gain. To handle these challenges, we first reformulate the system model into a singularity-free form and employ neural networks to approximate the unknown nonlinear functions. To ensure that the tracking errors are bounded by predefined performance boundaries and avoid the potential singularity problem inherent in other indirect adaptive control methods, we develop a singularity-free prescribed performance controller. Additionally, to simplify the controller design procedure, we adopt a high-order command filter and abandon the commonly used backstepping control approach. We employ the Lyapunov approach to analyze the stability of the identification and control algorithms, while simulation results demonstrate the efficacy of the proposed algorithms.
AB - This paper focuses on the trajectory tracking control problem of two-mass systems, addressing the challenges posed by unknown system dynamics and unknown control gain. To handle these challenges, we first reformulate the system model into a singularity-free form and employ neural networks to approximate the unknown nonlinear functions. To ensure that the tracking errors are bounded by predefined performance boundaries and avoid the potential singularity problem inherent in other indirect adaptive control methods, we develop a singularity-free prescribed performance controller. Additionally, to simplify the controller design procedure, we adopt a high-order command filter and abandon the commonly used backstepping control approach. We employ the Lyapunov approach to analyze the stability of the identification and control algorithms, while simulation results demonstrate the efficacy of the proposed algorithms.
UR - http://www.scopus.com/inward/record.url?scp=85174513364&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-6847-3_8
DO - 10.1007/978-981-99-6847-3_8
M3 - Conference contribution
AN - SCOPUS:85174513364
SN - 9789819968466
T3 - Lecture Notes in Electrical Engineering
SP - 73
EP - 84
BT - Proceedings of 2023 Chinese Intelligent Systems Conference - Volume I
A2 - Jia, Yingmin
A2 - Zhang, Weicun
A2 - Fu, Yongling
A2 - Wang, Jiqiang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th Chinese Intelligent Systems Conference, CISC 2023
Y2 - 14 October 2023 through 15 October 2023
ER -