基于SCN數據模型的SISO非線(xiàn)性自適應控制

代偉; 張政煊; 楊春雨; 馬小平

doi:10.16383/j.aas.c210174

基于SCN數據模型的SISO非線(xiàn)性自適應控制

doi: 10.16383/j.aas.c210174

代偉^1,,
張政煊^{1, 2,},
楊春雨^1,,
馬小平^1,

1.
中國礦業(yè)大學(xué)信息與控制工程學(xué)院徐州 221116
2.
北京科技大學(xué)自動(dòng)化學(xué)院北京 100083

基金項目: 國家自然科學(xué)基金(61973306), 江蘇省自然科學(xué)基金 (BK20200086), 流程工業(yè)綜合自動(dòng)化國家重點(diǎn)實(shí)驗室開(kāi)放課題基金(2020-KF-21-10)資助

詳細信息

作者簡(jiǎn)介:
代偉：中國礦業(yè)大學(xué)信息與控制工程學(xué)院教授. 主要研究方向為復雜工業(yè)過(guò)程建模, 運行優(yōu)化與控制. 本文通信作者. E-mail: weidai@cumt.edu.cn

張政煊：北京科技大學(xué)自動(dòng)化學(xué)院博士研究生. 2021年獲得中國礦業(yè)大學(xué)信息與控制工程學(xué)院碩士學(xué)位. 主要研究方向為數據特征提取, 不規則采樣數據的建模, 非線(xiàn)性自適應控制. E-mail: zzxqlkd@163.com

楊春雨：中國礦業(yè)大學(xué)信息與控制工程學(xué)院教授. 2009 年獲得東北大學(xué)博士學(xué)位. 主要研究方向為廣義系統, 魯棒控制. E-mail: chunyuyang@cumt.edu.cn

馬小平：中國礦業(yè)大學(xué)信息與控制工程學(xué)院教授. 主要研究方向為過(guò)程控制, 網(wǎng)絡(luò )控制, 故障診斷. E-mail: xpma@cumt.edu.cn

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- 文章訪(fǎng)問(wèn)數: 738
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- 被引次數: 0
出版歷程
- 收稿日期: 2021-03-03
- 錄用日期: 2022-03-01
- 網(wǎng)絡(luò )出版日期: 2022-10-26
- 刊出日期: 2024-10-21

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), and 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 master degree from the 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: 針對一類(lèi)難以建立精確模型的單輸入單輸出(Single-input single-output, SISO) 非線(xiàn)性離散動(dòng)態(tài)系統, 提出了一種數據驅動(dòng)模型的自適應控制方法. 所提方法首先設計具有直鏈與增強結構的隨機配置網(wǎng)絡(luò )(Stochastic configuration network, SCN), 建立了一種可同時(shí)表征非線(xiàn)性系統低階線(xiàn)性部分與高階非線(xiàn)性項(未建模動(dòng)態(tài))的數據驅動(dòng)模型, 并采用增量學(xué)習方法與監督機制, 對模型結構與模型參數進(jìn)行同步更新優(yōu)化, 保證了數據驅動(dòng)模型的無(wú)限逼近能力, 解決了傳統自適應控制采用交替辨識算法存在的建模精度低、模型收斂性無(wú)法保證的問(wèn)題. 進(jìn)而利用直鏈部分與增強部分, 分別設計了線(xiàn)性控制器及虛擬未建模動(dòng)態(tài)補償器, 建立了基于SCN 數據驅動(dòng)模型的自適應控制新方法, 分析了其穩定性與收斂性, 通過(guò)數值仿真實(shí)驗和采用交替辨識算法的傳統自適應控制方法進(jìn)行對比, 實(shí)驗結果表明了所提方法的有效性.
- 自適應控制 /
- 隨機配置網(wǎng)絡(luò ) /
- 監督機制 /
- 未建模動(dòng)態(tài) /
- 數據驅動(dòng)模型
Abstract: For a class of single-input single-output (SISO) nonlinear discrete dynamical systems which are difficult to establish an accurate model, a novel adaptive control method is proposed based on data-driven model. In the proposed approach, 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 low-order linear and the high-order nonlinear parts (unmodeled dynamics) of system, respectively. Besides, this paper employed an incremental learning algorithm and supervision mechanism to optimize the model structure and model parameters synchronously, which guarantee the universal approximation property of the data-driven model, solving the problems of low modeling accuracy and unguaranteed model convergence existing in traditional adaptive control with alternate identification algorithm. Then, the direct link and enhancement nodes are used to design the linear controller and virtual unmodeled dynamics compensator respectively. A new adaptive control approach based on SCN data-driven model is established, and the stability and convergence of the proposed control method are proved. Finally, simulation comparisons between our proposed method and the classic adaptive control method with alternate identification algorithm are made, showing the effectiveness of the proposed method.
- Adaptive control /
- stochastic configuration network (SCN) /
- supervision mechanism /
- unmodeled dynamic /
- data-driven model

HTML全文

圖 1 帶直鏈的隨機配置網(wǎng)絡(luò )

Fig. 1 Stochastic configuration network with direct link

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圖 2 基于SCN數據驅動(dòng)模型的自適應控制方法結構圖

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

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圖 3 不同遺忘因子下的控制系統輸出

Fig. 3 Output of control system under different forgetting factors

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圖 4 控制系統輸出對比

Fig. 4 Comparison of the output of the control system

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圖 5 控制系統輸入對比

Fig. 5 Comparison of the input of the control system

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圖 6 控制系統輸出誤差對比

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

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圖 7 非線(xiàn)性系統模型估計誤差對比

Fig. 7 Comparison of model estimation errors of nonlinear systems

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圖 8 基于SCN數據模型的灰分含量跟蹤控制輸出

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

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圖 9 基于SCN數據模型的重介質(zhì)選煤灰分含量估計誤差曲線(xiàn)

Fig. 9 Estimation error curve of ash content in dense medium separation process based on SCN data model

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表 1 模型性能對比

Table 1 Performance comparison of models

模型性能指標	增強節點(diǎn)個(gè)數	離線(xiàn)建模時(shí)間 (s)	模型在線(xiàn)平均絕對誤差
傳統RVFLNN模型	17	0.257 19	0.004 6
SCN模型	9	0.245 82	0.001 3

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表 2 控制系統模型估計性能對比

Table 2 Comparison of performance of model estimates for control systems

基于不同模型的自適應控制系統	${\rm MAE}$
基于線(xiàn)性模型的自適應控制	0.009 2
基于BP交替辨識模型的自適應控制	0.007 0
基于A(yíng)NFIS交替辨識模型的自適應控制	0.005 1
基于SCN數據模型的自適應控制	0.001 3

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