具有不確定控制增益嚴格反饋系統的自適應命令濾波控制

吳錦娃; 劉勇華; 蘇春翌; 魯仁全

doi:10.16383/j.aas.c210553

具有不確定控制增益嚴格反饋系統的自適應命令濾波控制

doi: 10.16383/j.aas.c210553

吳錦娃^{1, 2, 3,},
劉勇華^{1, 2, 3,},
蘇春翌^{1, 2, 3,},
魯仁全^{1, 2, 3,}

1.
廣東工業(yè)大學(xué)自動(dòng)化學(xué)院廣州 510006
2.
粵港智能決策與協(xié)同控制聯(lián)合實(shí)驗室廣州 510006
3.
廣東省智能決策與協(xié)同控制重點(diǎn)實(shí)驗室廣州 510006

基金項目: 國家自然科學(xué)基金(62173097, U2013601), 廣東省自然科學(xué)基金(2022A1515011239), 廣東省特支計劃本土創(chuàng )新創(chuàng )業(yè)團隊項目(2019BT02X353)資助

詳細信息

作者簡(jiǎn)介:
吳錦娃：廣東工業(yè)大學(xué)自動(dòng)化學(xué)院碩士研究生. 主要研究方向為自適應控制與智能控制. E-mail: jinwa.wu@outlook.com

劉勇華：廣東工業(yè)大學(xué)自動(dòng)化學(xué)院副教授. 主要研究方向為非線(xiàn)性控制與智能控制. 本文通信作者. E-mail: yonghua.liu@outlook.com

蘇春翌：廣東工業(yè)大學(xué)自動(dòng)化學(xué)院教授. 主要研究方向為控制理論及其在機電系統中的應用. E-mail: chunyi.su@concordia.ca

魯仁全：廣東工業(yè)大學(xué)自動(dòng)化學(xué)院教授. 主要研究方向為網(wǎng)絡(luò )化控制系統理論及應用, 醫療大數據分析, 智能制造. E-mail: rqlu@gdut.edu.cn

計量
- 文章訪(fǎng)問(wèn)數: 1408
- HTML全文瀏覽量: 467
- PDF下載量: 232
- 被引次數: 0
出版歷程
- 收稿日期: 2021-06-19
- 網(wǎng)絡(luò )出版日期: 2021-11-28
- 刊出日期: 2024-05-29

Adaptive Command Filtered Control of Strict Feedback Systems With Uncertain Control Gains

WU Jin-Wa^{1, 2, 3
,},
LIU Yong-Hua^{1, 2, 3
,},
SU Chun-Yi^{1, 2, 3
,},
LU Ren-Quan^{1, 2, 3
,}

1.
School of Automation, Guangdong University of Technology, Guangzhou 510006
2.
Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control, Guangzhou 510006
3.
Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, Guangzhou 510006

Funds: Supported by National Natural Science Foundation of China (62173097, U2013601), Natural Science Foundation of Guangdong Province (2022A1515011239), and the Local Innovative and Research Teams Project of Guangdong Special Support Program (2019BT02X353)

More Information

Author Bio:
WU Jin-Wa　Master student at the School of Automation, Guangdong University of Technology. Her research interest covers adaptive control and intelligent control

LIU Yong-Hua　Associate professor at the School of Automation, Guangdong University of Technology. His research interest covers nonlinear control and intelligent control. Corresponding author of this paper

SU Chun-Yi　Professor at the School of Automation, Guangdong University of Technology. His research interest covers control theory and its applications to mechanical systems

LU Ren-Quan　Professor at the School of Automation, Guangdong University of Technology. His research interest covers theory and application of networked control system, medical big data analysis, and intelligent manufacturing

摘要

摘要: 針對一類(lèi)具有不確定控制增益的嚴格反饋系統, 提出一種基于命令濾波反推技術(shù)的自適應神經(jīng)網(wǎng)絡(luò )控制方法. 該方法采用神經(jīng)網(wǎng)絡(luò )對系統中的未知非線(xiàn)性函數進(jìn)行逼近, 并引入命令濾波反推技術(shù)克服“計算膨脹”的問(wèn)題. 與現有的命令濾波反推控制文獻相比, 本文通過(guò)構造自適應誤差補償系統, 同時(shí)消除濾波器產(chǎn)生的邊界層誤差和不確定控制增益對系統性能造成的影響. 仿真結果驗證了所提控制方法的有效性.
- 非線(xiàn)性系統 /
- 命令濾波反推 /
- 神經(jīng)網(wǎng)絡(luò )控制 /
- 自適應控制
Abstract: In this paper, a command filtered-based adaptive neural control scheme is developed for strict feedback systems with uncertain control gains. In the developed scheme, neural networks are adopted to approximate the unknown nonlinear system functions and command filtered backstepping technique is utilized to solve the “explosion of complexity” problem. Compared with the literature on command filtered backstepping control, in this paper, an adaptive error compensating system is constructed to eliminate the impacts of the boundary layer errors generated by the filters and the uncertain control gains on system performance simultaneously. Simulation results are presented to verify the effectiveness of the proposed control scheme.
- Nonlinear systems /
- command filtered backstepping /
- neural network control /
- adaptive control

HTML全文

圖 1 系統輸出$y$, 期望軌跡$y_d$和跟蹤誤差$e_1$

Fig. 1 System output $y$, desired trajectory $y_d$ and tracking error $e_1$

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圖 4 自適應參數$||\hat{{\boldsymbol{\theta}}}_{g1}||$和$||\hat{{\boldsymbol{\theta}}}_{g2}||$

Fig. 4 Adaptive parameters $||\hat{{\boldsymbol{\theta}}}_{g1}||$ and $||\hat{{\boldsymbol{\theta}}}_{g2}||$

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圖 2 控制信號$u$

Fig. 2 Control signal $u$

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圖 3 自適應參數$||\hat{{\boldsymbol{\theta}}}_{f1}||$和$||\hat{{\boldsymbol{\theta}}}_{f2}||$

Fig. 3 Adaptive parameters $||\hat{{\boldsymbol{\theta}}}_{f1}||$ and $||\hat{{\boldsymbol{\theta}}}_{f2}||$

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圖 5 基于本文與文獻[36]控制方法的跟蹤誤差$e_1$

Fig. 5 Tracking errors $e_1$ under the control schemes in this paper and in [36]

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圖 6 基于本文與文獻[36] 控制方法的控制信號$u$

Fig. 6 Control signals $u$ under the control schemes in this paper and in [36]

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