Multi-dimensional Taylor network-based control for a class of nonlinear stochastic systems with full state time-varying constraints and the finite-time output constraint

被引：9
作者：
Wang, Ming-Xin [1 ,2 ]
Zhu, Shan-Liang [1 ,2 ]
Han, Yu-Qun [1 ,2 ]
机构：
[1] Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao 266061, Peoples R China
[2] Qingdao Univ Sci & Technol, Res Inst Math & Interdisciplinary Sci, Qingdao, Peoples R China
来源：
ASIAN JOURNAL OF CONTROL | 2022年 / 24卷 / 06期
关键词：
adaptive control; finite-time performance function; multi-dimensional Taylor network; nonlinear stochastic systems; time-varying barrier Lyapunov function; BARRIER LYAPUNOV FUNCTIONS; STRICT-FEEDBACK SYSTEMS; ADAPTIVE NEURAL-CONTROL; TRACKING CONTROL; CONTROL DESIGN; STABILIZATION; INPUT;
D O I：
10.1002/asjc.2720
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 this paper, the adaptive multi-dimensional Taylor network (MTN) control problem is investigated for nonlinear stochastic systems with full state time-varying constraints and the finite-time output constraint. By combining the MTN-based approximation method and the adaptive backstepping control method, a novel adaptive MTN control scheme is provided by constructing the time-varying barrier Lyapunov function (TVBLF). To implement the finite-time output constraint, the finite-time performance function (FTPF) is introduced in the control scheme. The proposed scheme can ensure that the tracking error finally converges to a small neighborhood of the origin in the finite-time and all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) in probability. Finally, two simulation examples are presented to show the effectiveness of the provided control scheme.
引用
收藏
页码：3311 / 3325
页数：15
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