基于<b>SCN</b>数据模型的<b>SISO</b>非线性自适应控制

代伟; 张政煊; 杨春雨; 马小平

doi:10.16383/j.aas.c210174

基于SCN数据模型的SISO非线性自适应控制

doi: 10.16383/j.aas.c210174

代伟^1,,
张政煊^{1, 2,},
杨春雨^1,,
马小平^1,

1.
中国矿业大学信息与控制工程学院徐州 221116
2.
北京科技大学自动化学院北京 100083

基金项目: 国家自然科学基金项目 (61973306), 江苏省自然科学基金项目 (BK20200086), 流程工业综合自动化国家重点实验室开放课题基金资助项目(2020-KF-21-10)

详细信息

作者简介:
代伟：中国矿业大学信息与控制工程学院教授.主要研究方向为复杂工业过程建模, 运行优化与控制. 本文通信作者. E-mail: weidai@cumt.edu.cn

张政煊：北京科技大学自动化学院博士研究生. 2021年获得中国矿业大学信息与控制工程学院硕士学位.主要研究方向为数据特征提取, 不规则采样数据的建模, 非线性自适应控制. E-mail: zzxqlkd@163.com

杨春雨：中国矿业大学信息与控制工程学院教授.于 2009 年获得东北大学博士学位.主要研究方向为广义系统, 鲁棒控制. E-mail: chunyuyang@cumt.edu.cn

马小平：中国矿业大学信息与控制工程学院教授.主要研究方向为过程控制, 网络控制, 故障诊断. E-mail: xpma@cumt.edu.cn

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出版历程
- 收稿日期: 2021-03-03
- 录用日期: 2022-03-01
- 网络出版日期: 2022-10-26

Adaptive Control of SISO Nonlinear System Using Data-driven SCN Model

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116
2.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083

Funds: Supported by National Natural Science Foundation of China (61973306), Natural Science Foundation of Jiangsu Provinces (BK20200086), State Key Laboratory of Synthetical Automation for Process Industries(2020-KF-21-10)

More Information

Author Bio:
DAI Wei　Professor at the School of Information and control Engineering, China University of Mining and Technology. His research interest covers modeling, operational optimization, and control for complex industrial process. Corresponding author of this paper

ZHANG Zheng-xuan　Ph. D. candidate at the School of Automation and Electrical Engineering, University of Science and Technology Beijing.He received his bachelor degree from School of Information and Control Engineering, China University of Mining and Technology in 2021. His research interest covers feature extraction of data, modeling of irregularly sampled data and nonlinear adaptive control

YANG Chun-yu　Professor at the School of Information and Control Engineering, China University of Mining and Technology. He received his Ph.D. degree from Northeastern University in 2009. His research interest covers descriptor systems and robust control

MA Xiao-ping　Professor at the School of Information and Control Engineering, China University of Mining and Technology. His research interest covers process control, networked control, and fault detection

摘要

摘要: 针对一类难以建立精确模型的单输入单输出(Single-input single-output, SISO) 非线性离散动态系统, 提出了一种数据驱动模型的自适应控制方法. 所提方法首先设计具有直链与增强结构的随机配置网络(Stochastic configuration network, SCN), 建立了一种可同时表征非线性系统低阶线性部分与高阶非线性项(未建模动态)的数据驱动模型, 并采用增量学习方法与监督机制, 对模型结构与模型参数进行同步更新优化, 保证了数据驱动模型的无限逼近能力, 解决了传统自适应控制采用交替辨识算法存在的建模精度低、模型收敛性无法保证的问题. 进而利用直链部分与增强部分, 分别设计了线性控制器及虚拟未建模动态补偿器, 建立了基于SCN 数据驱动模型的自适应控制新方法, 分析了其稳定性与收敛性, 通过数值仿真实验和采用交替辨识算法的传统自适应控制方法进行对比, 实验结果表明所提方法的有效性.
- 自适应控制 /
- 随机配置网络 /
- 监督机制 /
- 未建模动态 /
- 数据驱动模型
Abstract: For a class of SISO nonlinear systems which are difficult to establish accurate model, an novel of adaptive control method is proposed based on data driven models. A stochastic configuration network(SCN) is first employed to build the data-driven nonlinear system model, which adopts direct link and enhancement nodes to approximate the linear and nonlinear parts of system respectively. Besides, this paper employed incremental learning algorithm and supervision mechanism to obtain the optimal model structure and parameters, which guarantee the universal approximation property of model, solving the problems of low modeling accuracy and unguaranteed model convergence existing in traditional adaptive control with alternate identification algorithm. Then, direct link and enhancement nodes are used to design the linear controller and virtual unmodeled dynamics compensator respectively. The stability and convergence of the proposed control method are proved. Finally, simulation comparisons between our proposed method and a classic adaptive control method with an alternate identification algorithm are made, showing the effectiveness of the proposed method.
- Adaptive control /
- stochastic configuration network /
- supervision mechanism /
- unmodeled dynamic /
- data-driven model

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图 1 带直链的随机配置网络(SCN)

Fig. 1 Stochastic configuration network (SCN) with direct chain

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图 2 基于SCN数据模型的自适应控制方法结构图

Fig. 2 Structure diagram of adaptive control method with SCN-based data-driven model

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图 3 不同遗忘因子下的控制系统输出

Fig. 3 Output of control system under different forgetting factors

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图 4 控制系统输出对比

Fig. 4 Comparison of the output of the control system

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图 5 控制系统输入对比

Fig. 5 Comparison of the input of the control system

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图 6 控制系统输出误差对比

Fig. 6 Comparison of the output errors of the control systems

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图 7 非线性系统模型估计误差对比

Fig. 7 Comparison of model estimation errors of nonlinear systems

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图 8 基于SCN数据模型的灰分含量跟踪控制输出

Fig. 8 Output of Ash content tracking control based on SCN data-driven model

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图 9 基于SCN数据模型的重介质选煤灰分含量估计误差曲线

Fig. 9 Estimation error curve of ash content based on SCN data-driven model

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表 1 模型性能对比

Table 1 Performance comparison of models

模型性能指标	增强节点个数	离线建模时间(s)	模型在线平均绝对误差
传统RVFLN
模型^[21]	17	0.25719	0.0046
SCN模型	9	0.245820	0.0013

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表 2 控制系统模型估计性能对比

Table 2 Comparison of performance of model estimates for control systems

基于不同模型的自适应控制系统	${\rm MAE}\left\| {e'} \right\|$
基于线性模型的自适应控制	0.0092
基于BP交替辨识模型的自适应控制	0.0070
基于ANFIS交替辨识模型的自适应控制	0.0051
基于SCN数据模型的自适应控制	0.0013

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