东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2009, Vol. 30 ›› Issue (2): 153-157.DOI: -

• 论著 •    下一篇

三角形式非线性离散系统自适应神经网络控制

翟廉飞;柴天佑;   

  1. 东北大学流程工业综合自动化教育部重点实验室;东北大学自动化研究中心;
  • 收稿日期:2013-06-22 修回日期:2013-06-22 出版日期:2009-02-15 发布日期:2013-06-22
  • 通讯作者: Zhai, L.-F.
  • 作者简介:-
  • 基金资助:
    国家高技术研究发展计划项目(2006AA04Z179);;

Adaptive neural network control of discrete-time nonlinear systems with triangular-form

Zhai, Lian-Fei (1); Chai, Tian-You (1)   

  1. (1) Key Laboratory of Integrated Automation of Process Industry, Northeastern University, Shenyang 110004, China; (2) Research Center of Automation, Northeastern University, Shenyang 110004, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2009-02-15 Published:2013-06-22
  • Contact: Zhai, L.-F.
  • About author:-
  • Supported by:
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摘要： 针对一类控制输入为三角形式的多输入多输出离散非线性系统,提出了基于反步法的自适应神经网络控制方法.由于该系统的控制输入为非仿射形式,不能采用反馈线性化的方法设计控制系统;因此,首先采用隐函数定理证实了能够使系统输出跟踪期望轨迹的理想控制输入的存在性,并构造了理想的控制输入.利用高阶神经网络估计这些控制输入,提出了基于反步法的自适应神经网络控制方法.证明了所提出的控制方法能够保证闭环系统的所有信号半全局一致最终有界,并通过仿真验证了该方法的有效性.

关键词: 非线性离散系统, 神经网络, 自适应控制, 反步法, 多输入多输出, 三角形式

Abstract: For a class of multi-input-multi-output (MIMO) discrete-time nonlinear systems with triangular form control inputs, an adaptive neural network control is proposed via backstepping. Due to the non-affine form of the control inputs, feedback linearization method can not be used to design control system. Therefore, implicit function theorem is firstly exploited to assert the existence of the ideal control inputs, which can compel the system outputs to track their desired trajectories, and then ideal control inputs are constructed. By using high-order neural networks to approximate the ideal control inputs, an adaptive neural network control is developed via backstepping design. All signals of the closed-loop system are proved to be semi-globally uniformly ultimately bounded under the proposed control, while the effectiveness of the proposed control is illustrated by simulations.

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