Self-Triggered Adaptive NN Tracking Control for a Class of Continuous-Time Nonlinear Systems With Input Constraints

被引：5

作者：

Guo, Xinxin ^{[1
]}

Yan, Weisheng ^{[2
]}

Cui, Rongxin ^{[2
]}

Rout, Raja ^{[2
,3
]}

Zhang, Shouxu ^{[2
]}

机构：

[1] Southern Univ Sci & Technol, Dept Mech & Energy Engn, Shenzhen 518005, Peoples R China

[2] Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian 710072, Peoples R China

[3] Manipal Univ Jaipur, Dept Mechatron, Jaipur 303007, Rajasthan, India

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2022年 / 52卷 / 09期

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

Artificial neural networks; System dynamics; Adaptive systems; Heuristic algorithms; Control systems; Actuators; Vehicle dynamics; Adaptive tracking control; differentiator; input constraints; neural networks (NNs); self-triggered control; OUTPUT-FEEDBACK CONTROL; LIPSCHITZ CONSTANT; DIFFERENTIATION; DESIGN; MPC;

D O I：

10.1109/TSMC.2021.3130925

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 develops a self-triggered adaptive neural network (NN) tracking controller for a class of continuous-time nonlinear systems, that is, input constrained and with unknown drift and input dynamics. Since the drift and input dynamics are both unknown, an NN is built within a self-triggered update paradigm to approximate the unknown tracking control. The error derivative used in the weight update algorithm is derived using a robust exact differentiator technique. To address input constraints, an auxiliary compensator is designed for the unimplemented control effort. Through rigorous Lyapunov analyses, we can guarantee that all the tracking and weight errors are uniformly ultimately bounded. Finally, to show the effectiveness of the proposed control performance, simulation results of a two-link robot are provided and analyzed.

引用

页码：5805 / 5815

页数：11

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