基于觀(guān)測器和指定性能的非線(xiàn)性系統事件觸發(fā)跟蹤控制

游星星; 楊道文; 郭斌; 劉凱; 佃松宜; 朱雨琪

doi:10.16383/j.aas.c210387

基于觀(guān)測器和指定性能的非線(xiàn)性系統事件觸發(fā)跟蹤控制

doi: 10.16383/j.aas.c210387 cstr: 32138.14.j.aas.c210387

游星星^1,,
楊道文^{1, 2,},
郭斌^1,,
劉凱^1,,
佃松宜^1,,
朱雨琪^1,

1.
四川大學(xué)電氣工程學(xué)院成都 610065
2.
四川大學(xué) & 瀚維智能機器人感知與控制聯(lián)合實(shí)驗室成都 610065

基金項目: 國家資助博士后研究人員計劃(GZC20231783), 國家重點(diǎn)研發(fā)計劃(2018YFB1307401), 國家自然科學(xué)基金(62403340, 62303339, 61906023), 四川省自然科學(xué)基金(2023NSFSC0475, 2021YJ0092), 重慶市自然科學(xué)基金(cstc2019jcyj-msxmX0722, cstc2019jcyj-msxmX0710)資助

詳細信息

作者簡(jiǎn)介:
游星星：四川大學(xué)電氣工程學(xué)院助理研究員. 主要研究方向為神經(jīng)網(wǎng)絡(luò )的穩定性理論, 非線(xiàn)性系統的自適應控制及其應用. E-mail: youxingxing@stu.scu.edu.cn

楊道文：四川大學(xué)電氣工程學(xué)院博士研究生. 主要研究方向為機器視覺(jué)、感知, 人工智能和大數據. 本文通信作者. E-mail: yangdaowen@dubhedi.com

郭斌：四川大學(xué)電氣工程學(xué)院副研究員. 2020年獲得電子科技大學(xué)博士學(xué)位. 主要研究方向為故障診斷–容錯控制, 信息物理融合系統, 預測控制和魯棒控制. E-mail: bguodxl@163.com

劉凱：四川大學(xué)電氣工程學(xué)院教授. 分別于1996年和2001年獲得四川大學(xué)計算機科學(xué)專(zhuān)業(yè)學(xué)士和碩士學(xué)位. 2010年獲得美國肯塔基大學(xué)電氣工程博士學(xué)位. 主要研究方向為計算機/機器視覺(jué), 主動(dòng)/被動(dòng)立體視覺(jué)和圖像處理. E-mail: kailiu@scu.edu.cn

佃松宜：四川大學(xué)電氣工程學(xué)院教授. 分別于1996年和2002年獲得四川大學(xué)控制工程專(zhuān)業(yè)學(xué)士和碩士學(xué)位. 2009年獲得日本東本大學(xué)納米力學(xué)工程專(zhuān)業(yè)博士學(xué)位. 主要研究方向為先進(jìn)控制理論和智能信號處理, 電力電子系統及其控制, 運動(dòng)控制和機器人控制. E-mail: scudiansy@scu.edu.cn

朱雨琪：四川大學(xué)電氣工程學(xué)院博士研究生. 主要研究方向為軟體機器人建模及運動(dòng)控制, 抗擾控制. E-mail: zhuyuqi@stu.scu.edu.cn

計量
- 文章訪(fǎng)問(wèn)數: 1345
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- 被引次數: 0
出版歷程
- 收稿日期: 2021-05-06
- 錄用日期: 2021-11-02
- 網(wǎng)絡(luò )出版日期: 2021-11-28
- 刊出日期: 2024-09-19

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

YOU Xing-Xing^1
,,
YANG Dao-Wen^{1, 2
,},
GUO Bin^1
,,
LIU Kai^1
,,
DIAN Song-Yi^1
,,
ZHU Yu-Qi^1
,

1.
College of Electrical Engineering, Sichuan University, Chengdu 610065
2.
Robot Perception and Control Joint Laboratory, Sichuan University & Aisono, Chengdu 610065

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

摘要: 針對一類(lèi)具有外部擾動(dòng)的非線(xiàn)性系統, 提出了一種自適應模糊跟蹤控制方法. 首先, 利用模糊邏輯系統逼近系統未知的非線(xiàn)性函數, 并設計了一個(gè)模糊狀態(tài)觀(guān)測器來(lái)估計系統的不可測狀態(tài). 其次, 通過(guò)指定性能函數, 使系統的跟蹤誤差能夠約束在指定范圍內. 然后, 利用Backsteping方法結合包含對數函數的Lyapunov泛函, 設計了一個(gè)基于事件觸發(fā)條件的自適應模糊控制器. 基于Lyapunov穩定性理論和$\tanh$函數的性質(zhì)證明了所提出的控制策略能夠保證閉環(huán)系統中所有信號是半全局一致最終有界的. 最后, 通過(guò)一個(gè)數值仿真例子驗證了所提出方法的有效性.
- 自適應控制 /
- 指定性能 /
- 事件觸發(fā) /
- 模糊邏輯系統 /
- 模糊觀(guān)測器
Abstract: This paper investigates an adaptive fuzzy tracking control method for a class of nonlinear systems with external disturbances. Firstly, fuzzy logic systems and the fuzzy state observer are implemented to approximate unknown nonlinear functions and estimate the unmeasured states of systems, respectively. Then, the tracking error can be constrained within the specified range by means of the performance function. Furthermore, an event-triggered adaptive fuzzy controller is designed by employing the Backstepping method and Lyapunov functional with logarithm function. The proposed control strategy can ensure that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded based on the Lyapunov stability theory and the properties of $\tanh$function. Finally, a numerical simulation example is provided to verify the effectiveness of proposed method.
- Adaptive control /
- prescribed performance /
- event-triggered /
- fuzzy logic system /
- fuzzy observer

HTML全文

圖 1 帶齒輪連接的單連桿機械手

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

下載: 全尺寸圖片幻燈片

圖 2 不同方法下的系統跟蹤誤差$\bar{s}_{1}$

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

下載: 全尺寸圖片幻燈片

圖 3 參考信號${y}_rf50c1hsl6$和不同方法下的系統狀態(tài)$z_{1}$

Fig. 3 Reference signal${y}_rf50c1hsl6$and system states$z_{1}$ under different methods

下載: 全尺寸圖片幻燈片

圖 4 參考信號${\dot{y}}_rf50c1hsl6$和不同方法下的系統狀態(tài)$z_{2}$

Fig. 4 Reference signal${\dot{y}}_rf50c1hsl6$and system states$z_{2}$underdifferent methods

下載: 全尺寸圖片幻燈片

圖 5 系統輸出$y = z_{1}$和觀(guān)測狀態(tài)$\hat{z}_{1}$

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

下載: 全尺寸圖片幻燈片

圖 6 系統狀態(tài)$z_{2}$和觀(guān)測狀態(tài)$\hat{z}_{2}$

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

下載: 全尺寸圖片幻燈片

圖 7 自適應律$\|{{\boldsymbol{ \vartheta}}}_{1}\|$和$\|{{\boldsymbol{ \vartheta}}}_{2}\|$

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

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圖 8 不同采樣策略下的控制信號

Fig. 8 Control signals under different sampling strategies

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圖 9 事件觸發(fā)間隔和觸發(fā)次數

Fig. 9 Event trigger interval and number of triggers

下載: 全尺寸圖片幻燈片

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