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Design and Implementation of Adaptive Predictive Controllers for Industrial Processes
|關鍵字:||predictive control;預估控制;industrial processes;工業程序||出版社:||電機工程學系所||引用:||Bibliography  M. A. Henson and D. E. Seborg, Nonlinear Process Control, Prentice-Hall, New Jersey, 1997.  D. W. Clarke, “Application of generalized predictive control to industrial processes,” IEEE Control Systems Magazine, vol. 8, pp. 49-55, 1988.  J. B. Rawlings, “Tutorial overview of model predictive control,” IEEE Control Systems Magazine, vol. 4, pp. 38-52, 2000.  F. Allgower and A. Zheng, Nonlinear Model Predictive Control, Birkhauser Verlag, Germany, 2000.  B. Kouvaritakis and M. Cannon, Nonlinear Predictive Control - Theory and Practice, IEE, London, 2001.  S. Huang, K. K. Tan, and T.H. Lee, Applied Predictive Control, Springer-Verlag, London, 2002.  G. D. Nicolao, L. Magi, and R. Scattolini, “Stabilizing predictive control of nonlinear ARX Models,” Automatica, vol. 33, no.9, pp. 1691-1697, 1997.  W. H. Chen, D. J. Balance, and P. J. Gawthrop, J. J. Gribble, J. 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文中設計四種控制法則，為遞迴式類神經網路的廣義預估控制（Generalized Predictive Control with Recurrent Neural Networks）、遞迴式模糊類神經網路的廣義預估控制（Generalized Predictive Control Using Recurrent Fuzzy Neural Networks）、類神經網路的解耦預估控制（Decoupling Predictive Control with Neural Networks）和多變數類神經網路預估控制（Multivariable Neural-Network Predictive Control），這些控制策略已被證明具有系統穩定特性與步級輸入時系統零穩態誤差響應。
針對工業程序所提設計之控制器，其系統參數估測與控制法則均被整合成即時適應預估控制演算法，並以PC-based技術實現而應用在可變頻率油冷卻機（Variable-Frequency Oil-Cooling Machine），或以德州儀器（Texas Instruments）生產之TMS320C31數位信號處理器（DSP）以實現應用在射出成型機（Plastic Injection Molding Machine）。由實驗數據可證實本論文所提方法，在工業程序設備其設定點與負載變動下具有良好控制性能。
This dissertation presents design and implementation of adaptive predictive controllers for industrial processes. The nonlinear control strategies using neural networks and fuzzy systems are derived based on the minimization of predictive performance criterion. The control performance of the proposed control laws is studied as well.
Four control laws proposed in the dissertation are generalized predictive control with recurrent neural networks, generalized predictive control using recurrent fuzzy neural networks, decoupling predictive control with neural networks, and multivariable neural-network predictive control. The system stability and steady-state performance of the resulting control systems using these control algorithms have been well analyzed and proved.
The real-time adaptive predictive control algorithms for the industrial processes are proposed which have been implemented using a PC-based controller for the variable-frequency oil-cooling machine and utilizing a digital signal processor (DSP) TMS320C31 from Texas Instruments for the plastic injection-molding machine. Through experimental results, these proposed methods have been shown powerful in achieving the performance specifications under setpoint and load changes.
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