文献类型：期刊文献

英文题名：Adaptive self-structuring neural network control for full-state constrained hydraulic systems with disturbance compensation

作者：Liu, Haitao[1];Wang, Rui[1,2];Sun, Feng[2];Xing, Xuefeng[2]

机构：[1]Guangdong Ocean Univ, Sch Mech Engn, Zhanjiang 10566, Peoples R China;[2]Southern Marine Sci & Engn Guangdong Lab, Zhanjiang, Peoples R China

年份：2024

卷号：129

起止页码：1

外文期刊名：APPLIED MATHEMATICAL MODELLING

收录：SCI-EXPANDED(收录号：WOS:001179207200001)、、EI(收录号：20240615519892)、Scopus(收录号：2-s2.0-85184005570)、WOS

基金：This work was supported by the Key Project of Guangdong Province for Promoting High-Quality Economic Development (Marine Economic Development) in 2022: Research and Development of Key Technology and Equipment for Marine Vibroseis System (GDNRC [2022]29).

语种：英文

外文关键词：Hydraulic systems; Dynamic surface control; Self -structuring neural networks; Prescribed performance control

外文摘要：An adaptive self -structuring neural network for full -state constrained systems with a prescribed performance control scheme is proposed for hydraulic systems to achieve asymptotic tracking under parametric uncertainties and time -varying disturbances. To overcome external disturbances in hydraulic systems, a self -structuring neural network is employed to approximate mismatched disturbances, reducing the computational burden. Additionally, an output feedback filtering control technique is introduced to address the challenges of uncertain parameter sets, which deteriorates the control performance when affected by oscillations triggered by highfrequency noise. The prescribed performance control achieves the desired transient and steadystate performance of the system by constructing a sequence of error transition variables that guarantee that all the state errors of the hydraulic systems are within the appointed -time reachable performance function boundary range. For the proposed control scheme, the stability of the closed -loop system can be demonstrated via Lyapunov theory, and all signals are guaranteed to be bounded. Ultimately, the tracking performance of the proposed controller is verified through abundant comparative experiments under the influence of parameter uncertainty and time -varying disturbances.