TY - GEN
T1 - Event-Triggered Prescribed Performance Control for Nonlinear Servo Systems with Unknown Disturbances
AU - Liu, De
AU - Ren, Xuemei
AU - Zheng, Dongdong
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - This paper investigates the tracking control problem of servo systems afflicted by unknown nonlinearities and external disturbances. To tackle this issue, a prescribed performance control (PPC) strategy incorporating neural network compensation is proposed, accompanied by the design of an event-triggering mechanism to significantly optimize communication resources utilization. Initially, a performance function is introduced with the objective of constraining system tracking error and facilitating error transformation. Subsequently, a neural network is developed to estimate and compensate for the lumped unknown dynamics. By ensuring both system performance and stability, an event-triggering mechanism is devised for conserving system resources while achieving the desired tracking objective. Furthermore, it is demonstrated that the system signal remains bounded, and the proposed triggering mechanism exhibits no Zeno behavior. Finally, simulation results validate the effectiveness of the algorithm.
AB - This paper investigates the tracking control problem of servo systems afflicted by unknown nonlinearities and external disturbances. To tackle this issue, a prescribed performance control (PPC) strategy incorporating neural network compensation is proposed, accompanied by the design of an event-triggering mechanism to significantly optimize communication resources utilization. Initially, a performance function is introduced with the objective of constraining system tracking error and facilitating error transformation. Subsequently, a neural network is developed to estimate and compensate for the lumped unknown dynamics. By ensuring both system performance and stability, an event-triggering mechanism is devised for conserving system resources while achieving the desired tracking objective. Furthermore, it is demonstrated that the system signal remains bounded, and the proposed triggering mechanism exhibits no Zeno behavior. Finally, simulation results validate the effectiveness of the algorithm.
KW - Event-triggered
KW - Neural network
KW - Prescribed performance control
KW - Servo system
UR - http://www.scopus.com/inward/record.url?scp=85174542236&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-6886-2_28
DO - 10.1007/978-981-99-6886-2_28
M3 - Conference contribution
AN - SCOPUS:85174542236
SN - 9789819968855
T3 - Lecture Notes in Electrical Engineering
SP - 315
EP - 326
BT - Proceedings of 2023 Chinese Intelligent Systems Conference - Volume III
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 -