具有不确定控制增益严格反馈系统的自适应命令滤波控制

吴锦娃; 刘勇华; 苏春翌; 鲁仁全

doi:10.16383/j.aas.c210553

具有不确定控制增益严格反馈系统的自适应命令滤波控制

doi: 10.16383/j.aas.c210553

吴锦娃^{1, 2, 3,},
刘勇华^{1, 2, 3,},
苏春翌^{1, 2, 3,},
鲁仁全^{1, 2, 3,}

1.
广东工业大学自动化学院广州 510006
2.
粤港智能决策与协同控制联合实验室广州 510006
3.
广东省智能决策与协同控制重点实验室广州 510006

基金项目: 国家自然科学基金(62173097, U2013601), 广东省自然科学基金(2022A1515011239), 广东省特支计划本土创新创业团队项目(2019BT02X353)资助

详细信息

作者简介:
吴锦娃：广东工业大学自动化学院硕士研究生. 主要研究方向为自适应控制与智能控制. E-mail: jinwa.wu@outlook.com

刘勇华：广东工业大学自动化学院副教授. 主要研究方向为非线性控制与智能控制. 本文通信作者. E-mail: yonghua.liu@outlook.com

苏春翌：广东工业大学自动化学院教授. 主要研究方向为控制理论及其在机电系统中的应用. E-mail: chunyi.su@concordia.ca

鲁仁全：广东工业大学自动化学院教授. 主要研究方向为网络化控制系统理论及应用, 医疗大数据分析, 智能制造. E-mail: rqlu@gdut.edu.cn

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出版历程
- 收稿日期: 2021-06-19
- 网络出版日期: 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: 针对一类具有不确定控制增益的严格反馈系统, 提出一种基于命令滤波反推技术的自适应神经网络控制方法. 该方法采用神经网络对系统中的未知非线性函数进行逼近, 并引入命令滤波反推技术克服“计算膨胀”的问题. 与现有的命令滤波反推控制文献相比, 本文通过构造自适应误差补偿系统, 同时消除滤波器产生的边界层误差和不确定控制增益对系统性能造成的影响. 仿真结果验证了所提控制方法的有效性.
- 非线性系统 /
- 命令滤波反推 /
- 神经网络控制 /
- 自适应控制
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

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图 1 系统输出$y$, 期望轨迹$y_d$和跟踪误差$e_1$

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

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图 4 自适应参数$||\hat{{\boldsymbol{\theta}}}_{g1}||$和$||\hat{{\boldsymbol{\theta}}}_{g2}||$

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

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图 2 控制信号$u$

Fig. 2 Control signal $u$

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图 3 自适应参数$||\hat{{\boldsymbol{\theta}}}_{f1}||$和$||\hat{{\boldsymbol{\theta}}}_{f2}||$

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

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图 5 基于本文与文献[36]控制方法的跟踪误差$e_1$

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

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图 6 基于本文与文献[36] 控制方法的控制信号$u$

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

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