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基于观测器和指定性能的非线性系统事件触发跟踪控制

游星星 杨道文 郭斌 刘凯 佃松宜 朱雨琪

游星星, 杨道文, 郭斌, 刘凯, 佃松宜, 朱雨琪. 基于观测器和指定性能的非线性系统事件触发跟踪控制. 自动化学报, 2024, 50(9): 1747−1760 doi: 10.16383/j.aas.c210387
引用本文: 游星星, 杨道文, 郭斌, 刘凯, 佃松宜, 朱雨琪. 基于观测器和指定性能的非线性系统事件触发跟踪控制. 自动化学报, 2024, 50(9): 1747−1760 doi: 10.16383/j.aas.c210387
You Xing-Xing, Yang Dao-Wen, Guo Bin, Liu Kai, Dian Song-Yi, Zhu Yu-Qi. Event-triggered tracking control for a class of nonlinear systems with observer and prescribed performance. Acta Automatica Sinica, 2024, 50(9): 1747−1760 doi: 10.16383/j.aas.c210387
Citation: You Xing-Xing, Yang Dao-Wen, Guo Bin, Liu Kai, Dian Song-Yi, Zhu Yu-Qi. Event-triggered tracking control for a class of nonlinear systems with observer and prescribed performance. Acta Automatica Sinica, 2024, 50(9): 1747−1760 doi: 10.16383/j.aas.c210387

基于观测器和指定性能的非线性系统事件触发跟踪控制

doi: 10.16383/j.aas.c210387 cstr: 32138.14.j.aas.c210387
基金项目: 国家资助博士后研究人员计划(GZC20231783), 国家重点研发计划(2018YFB1307401), 国家自然科学基金(62403340, 62303339, 61906023), 四川省自然科学基金(2023NSFSC0475, 2021YJ0092), 重庆市自然科学基金(cstc2019jcyj-msxmX0722, cstc2019jcyj-msxmX0710)资助
详细信息
    作者简介:

    游星星:四川大学电气工程学院助理研究员. 主要研究方向为神经网络的稳定性理论, 非线性系统的自适应控制及其应用. E-mail: youxingxing@stu.scu.edu.cn

    杨道文:四川大学电气工程学院博士研究生. 主要研究方向为机器视觉、感知, 人工智能和大数据. 本文通信作者. E-mail: yangdaowen@dubhedi.com

    郭斌:四川大学电气工程学院副研究员. 2020年获得电子科技大学博士学位. 主要研究方向为故障诊断–容错控制, 信息物理融合系统, 预测控制和鲁棒控制. E-mail: bguodxl@163.com

    刘凯:四川大学电气工程学院教授. 分别于1996年和2001年获得四川大学计算机科学专业学士和硕士学位. 2010年获得美国肯塔基大学电气工程博士学位. 主要研究方向为计算机/机器视觉, 主动/被动立体视觉和图像处理. E-mail: kailiu@scu.edu.cn

    佃松宜:四川大学电气工程学院教授. 分别于1996年和2002年获得四川大学控制工程专业学士和硕士学位. 2009年获得日本东本大学纳米力学工程专业博士学位. 主要研究方向为先进控制理论和智能信号处理, 电力电子系统及其控制, 运动控制和机器人控制. E-mail: scudiansy@scu.edu.cn

    朱雨琪:四川大学电气工程学院博士研究生. 主要研究方向为软体机器人建模及运动控制, 抗扰控制. E-mail: zhuyuqi@stu.scu.edu.cn

Event-triggered Tracking Control for a Class of Nonlinear Systems With Observer and Prescribed Performance

Funds: Supported by the Postdoctoral Fellowship Program of CPSF (GZC20231783), National Key Research and Development Program of China (2018YFB1307401), National Natural Science Foundation of China (62403340, 62303339, 61906023), Natural Science Foundation of Sichuan Province (2023NSFSC0475, 2021YJ0092), and Natural Science Foundation of Chongqing Municipality of China (cstc2019jcyj-msxmX0722, cstc2019jcyj-msxmX0710)
More Information
    Author Bio:

    YOU Xing-Xing Assistant researcher at the College of Electrical Engineering, Sichuan University. His research interest covers the stability theory of neural network, and adaptive control of nonlinear systems and its application

    YANG Dao-Wen Ph.D. candidate at the College of Electrical Engineering, Sichuan University. His research interest covers machine vision, perception, artificial intelligence, and big data. Corresponding author of this paper

    GUO Bin Associate researcher at the College of Electrical Engineering, Sichuan University. He received his Ph.D. degree from University of Electronic Science and Technology in 2020. His research interest covers fault diagnosis-fault-tolerant control, cyber-physical fusion system, predictive control, and robust control

    LIU Kai Professor at the College of Electrical Engineering, Sichuan University. He received his bachelor and master degrees in computer science from Sichuan University, in 1996 and 2001, respectively, and his Ph.D. degree in electrical engineering from the University of Kentucky, USA in 2010. His research interest covers computer/machine vision, active/passive stereo vision, and image processing

    DIAN Song-Yi Professor at the College of Electrical Engineering, Sichuan University. He received his bachelor and master degrees in control engineering from Sichuan University in 1996 and 2002, respectively. He received his Ph.D. degree in nanomechanics engineering from Tohoku University, Japan in 2009. His research interest covers advanced control methods and intelligent signal processing, power-electronics system and its control, motion control, and robotic control

    ZHU Yu-Qi Ph.D. candidate at the College of Electrical Engineering, Sichuan University. His research interest covers modeling and motion control for soft robots, and disturbance-rejection control

  • 摘要: 针对一类具有外部扰动的非线性系统, 提出了一种自适应模糊跟踪控制方法. 首先, 利用模糊逻辑系统逼近系统未知的非线性函数, 并设计了一个模糊状态观测器来估计系统的不可测状态. 其次, 通过指定性能函数, 使系统的跟踪误差能够约束在指定范围内. 然后, 利用Backsteping方法结合包含对数函数的Lyapunov泛函, 设计了一个基于事件触发条件的自适应模糊控制器. 基于Lyapunov稳定性理论和$\tanh$函数的性质证明了所提出的控制策略能够保证闭环系统中所有信号是半全局一致最终有界的. 最后, 通过一个数值仿真例子验证了所提出方法的有效性.
  • 图  1  带齿轮连接的单连杆机械手

    Fig.  1  Single-link robot arm with a gearing connection

    图  2  不同方法下的系统跟踪误差$\bar{s}_{1}$

    Fig.  2  System tracking errors$\bar{s}_{1}$under different methods

    图  3  参考信号${y}_{d}$和不同方法下的系统状态$z_{1}$

    Fig.  3  Reference signal${y}_{d}$and system states$z_{1}$ under different methods

    图  4  参考信号${\dot{y}}_{d}$和不同方法下的系统状态$z_{2}$

    Fig.  4  Reference signal${\dot{y}}_{d}$and system states$z_{2}$underdifferent methods

    图  5  系统输出$y = z_{1}$和观测状态$\hat{z}_{1}$

    Fig.  5  System output$y = z_{1}$and observed state$\hat{z}_{1}$

    图  6  系统状态$z_{2}$和观测状态$\hat{z}_{2}$

    Fig.  6  System state$z_{2}$and observed state$\hat{z}_{2}$

    图  7  自适应律$\|{{\boldsymbol{ \vartheta}}}_{1}\|$和$\|{{\boldsymbol{ \vartheta}}}_{2}\|$

    Fig.  7  Adaptive laws$\|{{\boldsymbol{ \vartheta}}}_{1}\|$and$\|{{\boldsymbol{ \vartheta}}}_{2}\|$

    图  8  不同采样策略下的控制信号

    Fig.  8  Control signals under different sampling strategies

    图  9  事件触发间隔和触发次数

    Fig.  9  Event trigger interval and number of triggers

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出版历程
  • 收稿日期:  2021-05-06
  • 录用日期:  2021-11-02
  • 网络出版日期:  2021-11-28
  • 刊出日期:  2024-09-19

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