Asymptotic Fuzzy Neural Network Control for Pure-Feedback Stochastic Systems Based on a Semi-Nussbaum Function Technique

被引：31
作者：
Chen, Ci [1 ,2 ]
Liu, Zhi [1 ]
Xie, Kan [1 ,2 ]
Zhang, Yun [1 ]
Chen, C. L. Philip [3 ]
机构：
[1] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China
[2] Guangdong Key Lab IoT Informat Technol, Guangzhou 510006, Guangdong, Peoples R China
[3] Univ Macau, Fac Sci & Technol, Macau 999078, Peoples R China
来源：
IEEE TRANSACTIONS ON CYBERNETICS | 2017年 / 47卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Adaptive control; asymptotic control; purefeedback stochastic systems; semi-Nussbaum function; MIMO NONLINEAR-SYSTEMS; TRACKING CONTROL; ADAPTIVE-CONTROL; STABILIZATION; STABILITY; CONSENSUS; DELAY;
D O I：
10.1109/TCYB.2016.2628182
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Most existing control results for pure-feedback stochastic systems are limited to a condition that tracking errors are bounded in probability. Departing from such bounded results, this paper proposes an asymptotic fuzzy neural network control for pure-feedback stochastic systems. The control goal is realized by proposing a novel semi-Nussbaum function-based technique and employing it in adaptive backstepping controller design. The proposed Nussbaum function is integrated with adaptive control technique to guarantee that the tracking error is asymptotically stable in probability.
引用
收藏
页码：2448 / 2459
页数：12
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