Neural Network-Based Adaptive Sliding-Mode Control for Fractional Order Fuzzy System With Unmatched Disturbances and Time-Varying Delays

被引：31
作者：
Zhang, Huaguang [1 ,2 ]
Yan, Yuqing [2 ]
Mu, Yunfei [2 ]
Ming, Zhongyang [2 ]
机构：
[1] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110004, Peoples R China
[2] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110004, Peoples R China
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2023年 / 53卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Artificial neural networks; Fuzzy systems; Stability criteria; Adaptive systems; Time-varying systems; Delays; Mathematical models; Adaptive dynamic program (ADP); fractional order system (FOS); neural networked; sliding-mode control (SMC); time-varying delay system; NONLINEAR-SYSTEMS; STABILIZATION; DESIGN;
D O I：
10.1109/TSMC.2023.3257415
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This article concentrates on the neural network (NN)-based adaptive sliding-mode control (SMC) for fuzzy fractional-order system (FOS), $\alpha\in(0,1).$ First of all, a novel method of optimal SMC approach is developed for fuzzy FOSs by using the adaptive dynamic program (ADP), integral sliding mode, and NN with unmatched disturbances and time-varying delays. Next, to weaken the influence of the nonlinearities, the SMC strategy is proposed for the specific system, which is established on the corresponding SMD to ensure that the FOS reach the SMS in a finite time. Moreover, it shows that the matrix of SMS can be described by the linear matrix inequality (LMI). Furthermore, the Hamilton-Jacobi-Bell man (HJB) equation can be approximated by a single NN method, and the Lyapunov stability principle proves that the weight errors are convergent, further guaranteeing the asymptotically stability of the fuzzy FOS. Finally, to display that the above-presented policy is effective, simulation results are presented.
引用
收藏
页码：5174 / 5184
页数：11
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