具有全状态约束和未建模动态的严格反馈系统有限时间自适应动态面控制
作者:
作者单位:

江苏扬州大学

作者简介:

通讯作者:

中图分类号:

TP13

基金项目:

国家自然科学基金项目(62073283); 江苏省自然科学基金项目(BK20181218)


Finite-time adaptive dynamic surface control for strict-feedback systems with full state constraints and unmodeled dynamics
Author:
Affiliation:

Yangzhou University

Fund Project:

The National Natural Science Foundation of China under Grant 62073283; the Natural Science Foundation of Jiangsu Province under Grant BK20181218.

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    本文针对一类具有全状态约束、未建模动态和动态扰动的严格反馈非线性系统, 通过构造一种非线性滤波器,并利用Young"s不等式, 提出了一种新的有限时间自适应动态面控制方法. 引入非线性映射处理全状态约束, 将有约束系统变成无约束系统. 利用径向基函数来逼近未知光滑函数. 利用辅助系统产生的动态信号处理未建模动态. 对变换后的系统, 利用改进的动态面控制和有限时间方法所设计的控制器结构简单, 移去了现有有限时间控制中出现的``奇异性"问题, 加快了系统的收敛速度. 理论分析表明, 闭环系统中的所有信号在有限时间内有界, 全状态不违背约束条件. 数值算例的仿真结果表明, 本文所提出的自适应动态面控制方案是有效的.

    Abstract:

    By constructing nonlinear filters and using Young"s inequality, a new adaptive finite-time control method is proposed for a class of strict-feedback nonlinear systems with full state constraints, unmodeled dynamics and dynamic disturbances in this paper. The constrained system is transformed into an unconstrained system by introducing the nonlinear mapping. The radial basis function neural networks are utilized to approximate unknown nonlinear smooth functions. A dynamic signal produced by a auxiliary system is used to deal with unmodeled dynamics. Using modified dynamic surface control technology and finite-time control method, a simple controller is developed. The singularity problem in the existing finite -time control is removed, and the converging speed of the system is accelerated. Theoretical analysis shows that all signals in the closed-loop system are bounded in finite time. Full state constraints are not triggered. Simulation results of numerical example show that the proposed approach is effective

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2020-07-23
  • 最后修改日期:2020-10-15
  • 录用日期:2020-11-03
  • 在线发布日期: 2020-12-01
  • 出版日期: