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

 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, 2021, 45(x): 1−14
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, 2021, 45(x): 1−14 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, 2021, 45(x): 1−14

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

Funds: Supported by National Key R&D Program of China (2018YFB1307401), National Natural Science Foundation of China under Grant (61906023), Scientific and Technical Programs of Sichuan Province of China (2021YJ0092), Natural Science Foundation of Chongqing Municipality of China (cstc2019jcyj-msxmX0722; cstc2019jcyj-msxmX0710)
###### Author Bio: YOU Xing-Xing　Ph. D. candidate at the College of Electrical Engineering from Sichuan University. He received his MS degrees from Chongqing Jiaotong University in 2020. His research interests include the stability theory of neural network, adaptive control of nonlinear systems and its application YANG Dao-Wen　His research interests include 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 interests include 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 B.S. and M.S. degrees in computer science from Sichuan University, China, in 1996 and 2001, and his Ph.D. in electrical engineering from the University of Kentucky, USA in 2010. His research interests include 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 MS degrees of Control Engineering from Sichuan University, China in 1996 and 2002, respectively. He received his Ph.D degree in Nanomechanics Engineering from Tohoku University, Japan in 2009. His current research interests include advanced control methods and intelligent signal processing, power-electronics system and its control, motion control and robotic control ZHU Yu-Qi　Master student at the Electrical Engineering, Sichuan University. His research interests include modeling and motion control for soft robots, disturbance-rejection control
• 摘要: 针对一类具有外部扰动的非线性系统, 本文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  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  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  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$z_{2}$ under different 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}\|$$\|{{\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

##### 计量
• 文章访问数:  335
• HTML全文浏览量:  273
• 被引次数: 0
##### 出版历程
• 收稿日期:  2021-05-07
• 录用日期:  2021-11-02
• 网络出版日期:  2021-11-28

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