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              基于SCN數據模型的SISO非線性自適應控制

              代偉 張政煊 楊春雨 馬小平

              代偉, 張政煊, 楊春雨, 馬小平. 基于SCN數據模型的SISO非線性自適應控制. 自動化學報, 2022, 45(x): 1?11 doi: 10.16383/j.aas.c210174
              引用本文: 代偉, 張政煊, 楊春雨, 馬小平. 基于SCN數據模型的SISO非線性自適應控制. 自動化學報, 2022, 45(x): 1?11 doi: 10.16383/j.aas.c210174
              Dai Wei, Zhang Zheng-Xuan, Yang Chun-Yu, Ma Xiao-Ping. Adaptive control of siso nonlinear system using data-driven scn model. Acta Automatica Sinica, 2022, 45(x): 1?11 doi: 10.16383/j.aas.c210174
              Citation: Dai Wei, Zhang Zheng-Xuan, Yang Chun-Yu, Ma Xiao-Ping. Adaptive control of siso nonlinear system using data-driven scn model. Acta Automatica Sinica, 2022, 45(x): 1?11 doi: 10.16383/j.aas.c210174

              基于SCN數據模型的SISO非線性自適應控制

              doi: 10.16383/j.aas.c210174
              基金項目: 國家自然科學基金項目 (61973306), 江蘇省自然科學基金項目 (BK20200086), 流程工業綜合自動化國家重點實驗室開放課題基金資助項目(2020-KF-21-10)
              詳細信息
                作者簡介:

                代偉:中國礦業大學信息與控制工程學院教授.主要研究方向為復雜工業過程建模, 運行優化與控制. 本文通信作者. E-mail: weidai@cumt.edu.cn

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

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

                馬小平:中國礦業大學信息與控制工程學院教授.主要研究方向為過程控制, 網絡控制, 故障診斷. E-mail: xpma@cumt.edu.cn

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

              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: 針對一類難以建立精確模型的單輸入單輸出(Single-input single-output, SISO) 非線性離散動態系統, 提出了一種數據驅動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絡(Stochastic configuration network, SCN), 建立了一種可同時表征非線性系統低階線性部分與高階非線性項(未建模動態)的數據驅動模型, 并采用增量學習方法與監督機制, 對模型結構與模型參數進行同步更新優化, 保證了數據驅動模型的無限逼近能力, 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、模型收斂性無法保證的問題. 進而利用直鏈部分與增強部分, 分別設計了線性控制器及虛擬未建模動態補償器, 建立了基于SCN 數據驅動模型的自適應控制新方法, 分析了其穩定性與收斂性, 通過數值仿真實驗和采用交替辨識算法的傳統自適應控制方法進行對比, 實驗結果表明所提方法的有效性.
              • 圖  1  帶直鏈的隨機配置網絡(SCN)

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

                圖  2  基于SCN數據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

                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

                圖  4  控制系統輸出對比

                Fig.  4  Comparison of the output of the control system

                圖  5  控制系統輸入對比

                Fig.  5  Comparison of the input of the control system

                圖  6  控制系統輸出誤差對比

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

                圖  7  非線性系統模型估計誤差對比

                Fig.  7  Comparison of model estimation errors of nonlinear systems

                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

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

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

                表  1  模型性能對比

                Table  1  Performance comparison of models

                模型性能指標 增強節點個數 離線建模
                時間(s)
                模型在線平均絕
                對誤差
                傳統RVFLN
                模型[21] 17 0.25719 0.0046
                SCN模型 9 0.245820 0.0013
                下載: 導出CSV

                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 ${\rm MAE}\left| {e'} \right|$
                基于線性模型的自適應控制 0.0092
                基于BP交替辨識模型的自適應控制 0.0070
                基于ANFIS交替辨識模型的自適應控制 0.0051
                基于SCN數據模型的自適應控制 0.0013
                下載: 導出CSV
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                        • 收稿日期:  2021-03-03
                        • 錄用日期:  2022-03-01
                        • 網絡出版日期:  2022-10-26

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