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输入饱和的一类切换系统神经网络跟踪控制

司文杰 董训德 王聪

司文杰, 董训德, 王聪. 输入饱和的一类切换系统神经网络跟踪控制. 自动化学报, 2017, 43(8): 1383-1392. doi: 10.16383/j.aas.2017.c160372
引用本文: 司文杰, 董训德, 王聪. 输入饱和的一类切换系统神经网络跟踪控制. 自动化学报, 2017, 43(8): 1383-1392. doi: 10.16383/j.aas.2017.c160372
SI Wen-Jie, DONG Xun-De, WANG Cong. Adaptive Neural Tracking Control Design for a Class of Uncertain Switched Nonlinear Systems with Input Saturation. ACTA AUTOMATICA SINICA, 2017, 43(8): 1383-1392. doi: 10.16383/j.aas.2017.c160372
Citation: SI Wen-Jie, DONG Xun-De, WANG Cong. Adaptive Neural Tracking Control Design for a Class of Uncertain Switched Nonlinear Systems with Input Saturation. ACTA AUTOMATICA SINICA, 2017, 43(8): 1383-1392. doi: 10.16383/j.aas.2017.c160372

输入饱和的一类切换系统神经网络跟踪控制

doi: 10.16383/j.aas.2017.c160372
基金项目: 

国家重大科研仪器研制项目 61527811

详细信息
    作者简介:

    董训德  华南理工大学自动化科学与工程学院助理研究员.主要研究方向为系统识别, 动态模式辨识和确定学习理论研究.E-mail:audxd@scut.edu.cn

    王聪  华南理工大学自动化学院教授.主要研究方向为非线性系统自适应神经网络控制与辨识, 确定学习理论, 动态模式识别, 基于模式的智能控制, 振动故障诊断及在航空航天, 生物医学工程等领域的应用.E-mail:wangcong@scut.edu.cn

    通讯作者:

    司文杰 华南理工大学自动化科学与工程学院博士后.主要研究方向为自适应控制, 确定学习和故障诊断.本文通信作者.E-mail:mesiwenjie@scut.edu.cn

Adaptive Neural Tracking Control Design for a Class of Uncertain Switched Nonlinear Systems with Input Saturation

Funds: 

National Research and Development Program for Major Research Instruments 61527811

More Information
    Author Bio:

    Assistant professor at the School of Automation Science and Engineering, South China University of Technology. His research interest covers system identification, dynamical pattern recognition and deterministic learning theory

    Professor at the School of Automation, South China University of Technology. His research interest covers adaptive neural network control and identification of nonlinear systems, deterministic learning theory, dynamical pattern recognition, pattern-based intelligent control, oscillation fault diagnosis, and applications in aerospace and biomedical engineering

    Corresponding author: SI Wen-Jie Post-doctor at the School of Automation Science and Engineering, South China University of Technology. His research interest covers adaptive control, deterministic learning and fault diagnosis. Corresponding author of this paper
  • 摘要: 针对单输入单输出系统研究一种在任意切换下的跟踪控制问题,系统包含未知扰动和输入饱和特性.首先,利用高斯误差函数描述一个连续可导的非对称饱和模型.其次,利用径向基神经网络(Radial basis function neural network,RBF NN)逼近未知的系统动态.最后,基于公共的Lyapunov函数构造状态反馈控制器.设计的控制器避免过多参数调节从而减轻计算负荷.结果展示本文给出的状态反馈控制器可以保证闭环系统的所有信号是半全局一致有界的,并且跟踪误差可收敛到零值小的领域内.最后的仿真结果进一步验证提出方法的有效性.
    1)  本文责任编委 孙希明
  • 图  1  饱和函数

    Fig.  1  Saturation functions

    图  2  跟踪性能

    Fig.  2  Tracking performances

    图  3  跟踪误差 ${ y-y_d}$

    Fig.  3  The tracking error ${ y-y_d}$

    图  4  控制输入

    Fig.  4  Control inputs

    图  5  自适应更新率 $\hat{\theta}_1$

    Fig.  5  Response of the adaptive law $\hat{\theta}_1$

    图  6  自适应更新率 $\hat{\theta}_2$

    Fig.  6  Response of the adaptive law $\hat{\theta}_2$

    图  7  切换信号

    Fig.  7  Switching signal

    图  8  跟踪性能

    Fig.  8  Tracking performance

    图  9  跟踪误差

    Fig.  9  The tracking error

    图  10  自适应更新率 $\hat{\theta}_1$ , $\hat{\theta}_2$

    Fig.  10  Adaptive laws $\hat{\theta}_1$ , $\hat{\theta}_2$

    图  11  控制输入

    Fig.  11  Control inputs

    图  12  切换信号轨迹 $\sigma(t)$

    Fig.  12  Trajectory of the switching signal $\sigma(t)$

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出版历程
  • 收稿日期:  2016-05-04
  • 录用日期:  2016-10-09
  • 刊出日期:  2017-08-20

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