Fixed-time tracking control of pure-feedback system with input saturation and output constraints

被引：0
|
作者：
Huang Y.-H. [1 ]
Dai J.-Y. [1 ]
Ying J. [1 ]
Jiang Y. [1 ]
Li Y.-D. [1 ]
Lu L.-L. [1 ]
机构：
[1] School of Information Engineering, Nanchang Hangkong University, Nanchang
来源：
Kongzhi yu Juece/Control and Decision | 2023年 / 38卷 / 02期
关键词：
Barrier Lyapunov function; fixed-time control; output constraints; quantized input saturation;
D O I：
10.13195/j.kzyjc.2021.0856
中图分类号：
学科分类号：
摘要：
A fixed-time adaptive neural tracking control method is firstly proposed for nonlinear pure-feedback systems with quantized input saturation and output constraints. To resolve the problem of the system with non-simulation structure, the mean value theorem is introduced firstly. The Barrier Lyapunov function is used to constrain system output, and the RBF neural network is used to approximate the unknown function based on the back stepping method. According to the fixed time control theory, we design an input signal to guarantee this system to trace desired signal at fixed time under the situation of saturation input and limited output. Besides, the input signal is quantified by a lagging quantizier, which contributes to decrease the communications rate of the control signal. and the convergence speed is unrelated to the initial state in this system. Finally, Matlab simulation software is conducted to verify the effectiveness the proposed controller. © 2023 Northeast University. All rights reserved.
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页码：429 / 434
页数：5
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