Neural-network based adaptive sliding mode control for Takagi-Sugeno fuzzy systems

被引：32
作者：
Sun, Xingjian [1 ]
Zhang, Lei [1 ]
Gu, Juping [1 ]
机构：
[1] Nantong Univ, Sch Elect Engn, Nantong 226019, Peoples R China
来源：
INFORMATION SCIENCES | 2023年 / 628卷
关键词：
T-S fuzzy system; Sliding mode control; Neural-network; TIME-DELAY SYSTEMS; PREDICTIVE CONTROL; STABILIZATION;
D O I：
10.1016/j.ins.2022.12.118
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In the present study, the adaptive sliding mode control (ASMC) strategy is investigated for a class of complex nonlinear systems with matched and unknown nonlinearities and external disturbances. The nonlinearities and external disturbances are approached by a Gaussian radial basic neural network. A Takagi-Sugeno (T-S) fuzzy model based integral switching function is introduced to solve the ASMC problem, which eliminates the constrain that input gains required to share a common matrix in all fuzzy rules. Then, the switching control term is represented as a proportional integral (PI) control format to reduce the chattering phenomenon. Based on the Lyapunov theory, a set of existence conditions of the sliding mode controller are given such that the stability of the control systems can be guaranteed. Finally, a experimental simulation is utilized to verify the effectiveness of the proposed sliding mode control (SMC) strategy.
引用
收藏
页码：240 / 253
页数：14
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