簡易檢索 / 詳目顯示

回結果列表
研究生： 黃郁展
Huang, Yu-Chan
論文名稱： 熱軋連軋機之動態系統建模與非線性控制器設計
Dynamic System Modeling and Nonlinear Controller Design for Tandem Hot Strip Rolling Mills
指導教授： 彭兆仲
Peng, Chao-Chung
學位類別： 博士
Doctor
系所名稱： 工學院 - 航空太空工程學系
Department of Aeronautics & Astronautics
論文出版年： 2025
畢業學年度： 113
語文別： 英文
論文頁數： 139
中文關鍵詞： 熱軋連軋機 、神經網路 、厚度控制 、熱軋張力控制
外文關鍵詞： Hot strip tandem mill, neural network, thickness control, looper-tension control
相關次數： 點閱：49  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
    • 摘要 ii Abstract x Acknowledgements xi Content xii List of Tables xiv List of Figures xv 1. Introduction 1 1.1. Motivation and Purpose 1 1.2. Hot Strip Mill 2 1.3. Literature Review 4 1.4. Structure of the Dissertation 9 1.5. Contributions 10 2. Model of Finishing Mill 13 2.1. Rolling Mechanism 13 2.1.1. Rolling Force 13 2.1.2. Slip 13 2.2. Thickness Model 14 2.2.1. Work Roll Thermal Compensation 15 2.2.2. Oil film Compensation 15 2.2.3. Bending Force Compensation 16 2.3. Transport Delay 16 2.4. Flatness 17 2.5. Looper-tension System 18 2.6. Conventional Control Schemes 20 3. Looper-tension System Controller Design 24 3.1. Model Description 24 3.2. Feedback Linearization 26 3.3. Robust PI Controller Design 27 3.4. Controller Gains Optimization 33 3.5. Adaptive Control Law 35 3.6. Numerical Simulations 38 3.7. Discussion on Looper-tension Control 43 4. Tandem Thickness Controller Design 45 4.1. Introduction and Problem Statement 45 4.2. Tandem Rolling Simulation Environment 48 4.3. Shape–Thickness Coupling in Tandem AGC 48 4.4. Shape Balanced Controller Design 49 4.5. Asynchronous Shape Balanced Controller Design 51 4.6. Numerical Simulations 52 4.6.1. Simulation results of conventional M-AGC and SB-AGC 54 4.6.2. Simulation results of ASB-AGC 54 4.7. Application results 56 4.8. Discussion on Tandem Thickness Controller Design 59 5. Neural Network Based Thickness Modelling 61 5.1. Neural Network Architecture 61 5.1.1. Multi-Layer Perceptron 62 5.1.2. Recurrent Neural Network 64 5.1.3. Long Short Term Memory 65 5.2. Residual Learning 66 5.3. Thickness Models 67 5.3.1. Thickness Physical Model 68 5.3.2. MLP Thickness Prediction Model 69 5.3.3. MLP Thickness Compensation Prediction Model 70 5.4. Data Collection and Pre-processing 72 5.5. MLP Model Development 73 5.5.1. Input Selection 74 5.5.2. MLP Training Procedure 75 5.6. MLP Experimental Results 77 5.6.1. Loss function Comparison 79 5.6.2. MLP Training Convergence 79 5.6.3. MLP Performance Evaluation 81 5.7. Shortcut Learning Effect 89 5.7.1. Shortcut Effect in PENN-TC Residual Learning 90 5.7.2. Shortcut Effect Mitigation Strategies and Results 92 5.8. LSTM Thickness Model 93 5.8.1. LSTM Model Development 94 5.8.2. LSTM Performance Evaluation 95 5.9. Discussion on NN-based Thickness Modelling and Control 99 6. Conclusions and Future Research 103 Appendix A Tracking Error Derivation 106 Appendix B Looper Torque Uncertainties 108 Appendix C Loss Function Physical Constrain 111 Reference 113 Publication List 119

    [1] K. Omura et al., "Attenuation of roll eccentric disturbance by modified repetitive controllers for steel strip process with transport time delay," IFAC-PapersOnLine, vol. 48, no. 17, pp. 131-136, 2015/01/01/ 2015, doi: https://doi.org/10.1016/j.ifacol.2015.10.091.
    [2] H. Shi, J. Liu, Y. Wu, K. Zhang, L. Zhang, and P. Xue, "Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach," International Journal of Systems Science, vol. 47, no. 5, pp. 1095-1109, 2016.
    [3] F. Yin, "Discrete model predictive control scheme for an integrated gauge-looper control system in a tandem hot strip mill," IEEE Access, vol. 8, pp. 73972-73985, 2020.
    [4] R. Sims, "The calculation of roll force and torque in hot rolling mills," Proceedings of the Institution of Mechanical Engineers, vol. 168, no. 1, pp. 191-200, 1954.
    [5] I. Choi, J. Rossiter, and P. Fleming, "Looper and tension control in hot rolling mills: A survey," Journal of Process Control, vol. 17, no. 6, pp. 509-521, 2007.
    [6] A. Gaber, M. Elnaggar, and H. A. Fattah, "Looper and tension control in hot strip finishing mills based on different control approaches," Journal of Engineering and Applied Science, vol. 69, no. 1, p. 100, 2022.
    [7] Y. Kotera and F. Watanabe, "Multivariable control of hot strip mill looper," IFAC Proceedings Volumes, vol. 14, no. 2, pp. 2471-2476, 1981.
    [8] Y. Ji, H. Yuan, L. Song, H. Li, W. Peng, and J. Sun, "Coordinate control of strip thickness-crown-tension based on inverse linear quadratic in tandem hot rolling mill," The International Journal of Advanced Manufacturing Technology, vol. 118, no. 3, pp. 1213-1226, 2022.
    [9] H. Yuan, X. Li, X. Wang, and Y. Ji, "A looper-thickness coordinated control strategy based on ILQ theory and GA-BP neural network," The International Journal of Advanced Manufacturing Technology, vol. 127, no. 9, pp. 4845-4860, 2023.
    [10] H. Imanari et al., "Looper H-infinity control for hot-strip mills," IEEE Trans. Ind. Appl., vol. 33, no. 3, pp. 790-796, 1997.
    [11] G. Hearns and M. J. Grimble, "Robust multivariable control for hot strip mills," ISIJ international, vol. 40, no. 10, pp. 995-1002, 2000.
    [12] T. Hesketh, Y. A. Jiang, D. J. Clements, D. Butler, and R. Van der Laan, "Controller design for hot strip finishing mills," IEEE Transactions on Control Systems Technology, vol. 6, no. 2, pp. 208-219, 1998.
    [13] R. Furlan, F. A. Cuzzola, and T. Parisini, "Friction compensation in the interstand looper of hot strip mills: A sliding-mode control approach," Control Engineering Practice, vol. 16, no. 2, pp. 214-224, 2008.
    [14] F. A. Cuzzola, "A Multivariable and Multi-Objective Approach for the Control of Hot-Strip Mills," Journal of Dynamic Systems, Measurement, and Control, vol. 128, no. 4, pp. 856-868, 2006, doi: 10.1115/1.2361323.
    [15] C. J. Park and I. C. Hwang, "Tension control in hot strip process using adaptive receding horizon control," Journal of Materials Processing Technology, vol. 209, no. 1, pp. 426-434, 2009.
    [16] J. Xiao-hong, L.-p. Shao, and Y. Peng, "Adaptive coordinated control for hot strip finishing mills," Journal of Iron and Steel Research International, vol. 18, no. 4, pp. 36-43, 2011.
    [17] U. Knechtelsdorfer, A. Steinboeck, F. Schausberger, and A. Kugi, "A novel mass flow controller for tandem hot rolling mills," Journal of Process Control, vol. 104, pp. 168-177, 2021.
    [18] Z. Zhong, J. Wang, and L. Lu, "Looper and tension control in hot strip finishing mills based on sliding mode and adaptive control," in 2010 8th World Congress on Intelligent Control and Automation, 2010: IEEE, pp. 1156-1161.
    [19] J. Chiasson, "A new approach to dynamic feedback linearization control of an induction motor," IEEE Transactions on Automatic Control, vol. 43, no. 3, pp. 391-397, 1998.
    [20] Z. Zhong and J. Wang, "Looper-tension almost disturbance decoupling control for hot strip finishing mill based on feedback linearization," IEEE Transactions on Industrial Electronics, vol. 58, no. 8, pp. 3668-3679, 2010.
    [21] L.-H. Chen and C.-C. Peng, "Extended backstepping sliding controller design for chattering attenuation and its application for servo motor control," applied sciences, vol. 7, no. 3, p. 220, 2017.
    [22] C.-C. Peng and M.-C. Tsai, "Analysis of an Electronic Heating Device: System Modeling, Identification and Control," IEEE Transactions on Industrial Electronics, 2024.
    [23] C.-L. Lee and C.-C. Peng, "Analytic time domain specifications PID controller design for a class of 2 nd order linear systems: A genetic algorithm method," IEEE Access, vol. 9, pp. 99266-99275, 2021.
    [24] S. K. Yildiz et al., "Dynamic modelling and simulation of a hot strip finishing mill," Applied Mathematical Modelling, vol. 33, no. 7, pp. 3208-3225, 2009.
    [25] K. Prinz, A. Steinboeck, and A. Kugi, "Optimization-based feedforward control of the strip thickness profile in hot strip rolling," Journal of Process Control, vol. 64, pp. 100-111, 2018.
    [26] L. Li et al., "Influence mechanism of rolling force on strip shape during tandem hot rolling using a novel 3D multi-stand coupled thermo-mechanical FE model," Journal of Manufacturing Processes, vol. 81, pp. 505-521, 2022.
    [27] Y. Liu, X. Wang, J. Sun, G. Liu, H. Li, and Y. Ji, "Strip Thickness and Profile–Flatness Prediction in Tandem Hot Rolling Process Using Mechanism Model‐Guided Machine Learning," steel research international, vol. 94, no. 1, p. 2200447, 2023.
    [28] L. Meng, J. Ding, X. Li, G. Cao, Y. Li, and D. Zhang, "Novel shape control system of hot-rolled strip based on machine learning fused mechanism model," Expert Systems with Applications, vol. 255, p. 124789, 2024/12/01/ 2024, doi: https://doi.org/10.1016/j.eswa.2024.124789.
    [29] Y. Ji, L. Song, H. Yuan, H. Li, W. Peng, and J. Sun, "Prediction of strip section shape for hot-rolled based on mechanism fusion data model," Applied Soft Computing, vol. 146, p. 110670, 2023/10/01/ 2023, doi: https://doi.org/10.1016/j.asoc.2023.110670.
    [30] Z. Hu, Z. Wei, H. Sun, J. Yang, and L. Wei, "Optimization of metal rolling control using soft computing approaches: a review," Archives of Computational Methods in Engineering, vol. 28, pp. 405-421, 2021.
    [31] P. J. Schmid, "Dynamic mode decomposition of numerical and experimental data," Journal of fluid mechanics, vol. 656, pp. 5-28, 2010.
    [32] S. L. Brunton, J. L. Proctor, and J. N. Kutz, "Discovering governing equations from data by sparse identification of nonlinear dynamical systems," Proceedings of the national academy of sciences, vol. 113, no. 15, pp. 3932-3937, 2016.
    [33] J.-N. Juang and R. S. Pappa, "An eigensystem realization algorithm for modal parameter identification and model reduction," Journal of guidance, control, and dynamics, vol. 8, no. 5, pp. 620-627, 1985.
    [34] H. V. H. Ayala, D. Habineza, M. Rakotondrabe, and L. dos Santos Coelho, "Nonlinear black-box system identification through coevolutionary algorithms and radial basis function artificial neural networks," Applied Soft Computing, vol. 87, p. 105990, 2020.
    [35] K. Tang, S. Huang, K. Tan, and T. Lee, "Combined PID and adaptive nonlinear control for servo mechanical systems," Mechatronics, vol. 14, no. 6, pp. 701-714, 2004.
    [36] C.-H. Lee and C.-C. Teng, "Identification and control of dynamic systems using recurrent fuzzy neural networks," IEEE Transactions on fuzzy systems, vol. 8, no. 4, pp. 349-366, 2000.
    [37] Y. Wang, "A new concept using LSTM Neural Networks for dynamic system identification," in 2017 American control conference (ACC), 2017: IEEE, pp. 5324-5329.
    [38] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.
    [39] S. Wen, Y. Wang, Y. Tang, Y. Xu, P. Li, and T. Zhao, "Real-time identification of power fluctuations based on LSTM recurrent neural network: A case study on Singapore power system," IEEE Transactions on Industrial Informatics, vol. 15, no. 9, pp. 5266-5275, 2019.
    [40] C.-C. Peng and Y.-H. Chen, "Digital twins-based online monitoring of TFE-731 turbofan engine using fast orthogonal search," IEEE Systems Journal, vol. 16, no. 2, pp. 3060-3071, 2021.
    [41] R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, "Neural ordinary differential equations," Advances in neural information processing systems, vol. 31, 2018.
    [42] M. Raissi, P. Perdikaris, and G. E. Karniadakis, "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations," Journal of Computational physics, vol. 378, pp. 686-707, 2019.
    [43] S. Cuomo, V. S. Di Cola, F. Giampaolo, G. Rozza, M. Raissi, and F. Piccialli, "Scientific machine learning through physics–informed neural networks: Where we are and what’s next," Journal of Scientific Computing, vol. 92, no. 3, p. 88, 2022.
    [44] M. Raissi, "Deep hidden physics models: Deep learning of nonlinear partial differential equations," Journal of Machine Learning Research, vol. 19, no. 25, pp. 1-24, 2018.
    [45] G. Pang, L. Lu, and G. E. Karniadakis, "fPINNs: Fractional physics-informed neural networks," SIAM Journal on Scientific Computing, vol. 41, no. 4, pp. A2603-A2626, 2019.
    [46] Z. Lai, C. Mylonas, S. Nagarajaiah, and E. Chatzi, "Structural identification with physics-informed neural ordinary differential equations," Journal of Sound and Vibration, vol. 508, p. 116196, 2021/09/15/ 2021, doi: https://doi.org/10.1016/j.jsv.2021.116196.
    [47] C.-C. Peng and Y.-H. Chen, "A hybrid neural ordinary differential equation based digital twin modeling and online diagnosis for an industrial cooling fan," Future Internet, vol. 15, no. 9, p. 302, 2023.
    [48] Y. Huang, X. Zhou, and Z. Gao, "Thickness prediction of thin strip cold rolling based on VBGM-RBF," The International Journal of Advanced Manufacturing Technology, vol. 120, no. 9, pp. 5865-5884, 2022.
    [49] W.-g. Li, L. Xie, Y.-t. Zhao, Z.-x. Li, and W.-b. Wang, "Prediction model for mechanical properties of hot-rolled strips by deep learning," Journal of Iron and Steel Research International, vol. 27, pp. 1045-1053, 2020.
    [50] Z.-H. Wang, D.-Y. Gong, X. Li, G.-T. Li, and D.-H. Zhang, "Prediction of bending force in the hot strip rolling process using artificial neural network and genetic algorithm (ANN-GA)," The International Journal of Advanced Manufacturing Technology, vol. 93, pp. 3325-3338, 2017.
    [51] M. Bagheripoor and H. Bisadi, "Application of artificial neural networks for the prediction of roll force and roll torque in hot strip rolling process," Applied Mathematical Modelling, vol. 37, no. 7, pp. 4593-4607, 2013/04/01/ 2013, doi: https://doi.org/10.1016/j.apm.2012.09.070.
    [52] J. Pittner and M. A. Simaan, "A useful control model for tandem hot metal strip rolling," IEEE Trans. Ind. Appl., vol. 46, no. 6, pp. 2251-2258, 2010.
    [53] X. Zhang, Z. Jiang, A. Tieu, X. Liu, and G. Wang, "Numerical modelling of the thermal deformation of CVC roll in hot strip rolling," Journal of Materials Processing Technology, vol. 130, pp. 219-223, 2002.
    [54] A. Tieu, Z. Jiang, and C. Lu, "A 3D finite element analysis of the hot rolling of strip with lubrication," Journal of Materials Processing Technology, vol. 125, pp. 638-644, 2002.
    [55] P. Montmitonnet, "Hot and cold strip rolling processes," Computer Methods in Applied Mechanics and Engineering, vol. 195, no. 48, pp. 6604-6625, 2006/10/01/ 2006, doi: https://doi.org/10.1016/j.cma.2005.10.014.
    [56] J.-J. E. Slotine and W. Li, Applied nonlinear control (no. 1). Prentice hall Englewood Cliffs, NJ, 1991.
    [57] M. Chilali and P. Gahinet, "H/sub /spl infin// design with pole placement constraints: an LMI approach," IEEE Transactions on Automatic Control, vol. 41, no. 3, pp. 358-367, 1996, doi: 10.1109/9.486637.
    [58] M. Chilali, P. Gahinet, and P. Apkarian, "Robust pole placement in LMI regions," IEEE Transactions on Automatic Control, vol. 44, no. 12, pp. 2257-2270, 1999, doi: 10.1109/9.811208.
    [59] R. Nandan, R. Rai, R. Jayakanth, S. Moitra, N. Chakraborti, and A. Mukhopadhyay, "Regulating crown and flatness during hot rolling: A multiobjective optimization study using genetic algorithms," Materials and Manufacturing Processes, vol. 20, no. 3, pp. 459-478, 2005.
    [60] M. Salimi and M. Forouzan, "Determination of bending actuators set points to control crown and flatness in hot rolling of strip," Journal of Materials Processing Technology, vol. 125, pp. 670-677, 2002.
    [61] K. Shohet and N. Townsend, "Flatness control in plate rolling," J. Iron Steel Inst., vol. 209, pp. 769-775, 1971.
    [62] D. Li, J.-c. Liu, S.-b. Tan, X. Yu, and C.-j. Zhang, "A new monitor-AGC system in hot continues rolling," in Proceedings of the 33rd Chinese Control Conference, 2014: IEEE, pp. 6319-6323.
    [63] W.-Y. Chien, H.-H. Cheng, C.-S. Yi, and M.-C. Chang, "A strategy to monitor AGC in consideration of rolling force distribution," China Steel Technical Report, vol. 26, 2013.
    [64] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations by back-propagating errors," nature, vol. 323, no. 6088, pp. 533-536, 1986.
    [65] P. J. Werbos, "Backpropagation through time: what it does and how to do it," Proceedings of the IEEE, vol. 78, no. 10, pp. 1550-1560, 1990.
    [66] A. Sherstinsky, "Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network," Physica D: Nonlinear Phenomena, vol. 404, p. 132306, 2020.
    [67] M. Schuster and K. K. Paliwal, "Bidirectional recurrent neural networks," IEEE transactions on Signal Processing, vol. 45, no. 11, pp. 2673-2681, 1997.
    [68] K. He, X. Zhang, S. Ren, and J. Sun, "Identity mappings in deep residual networks," in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, 2016: Springer, pp. 630-645.
    [69] Z. Chen and D. Xiu, "On generalized residual network for deep learning of unknown dynamical systems," Journal of Computational Physics, vol. 438, p. 110362, 2021/08/01/ 2021, doi: https://doi.org/10.1016/j.jcp.2021.110362.
    [70] R. Geirhos et al., "Shortcut learning in deep neural networks," Nature Machine Intelligence, vol. 2, no. 11, pp. 665-673, 2020.
    [71] K. L. Hermann, H. Mobahi, T. Fel, and M. C. Mozer, "On the foundations of shortcut learning," arXiv preprint arXiv:2310.16228, 2023.

    無法下載圖示 校內：2030-08-18公開
    校外：2030-08-18公開
    電子論文尚未授權公開，紙本請查館藏目錄
    QR CODE