壓光機主要功能，為使織物經壓光處理後，在表面呈現均勻光澤，藉以提高織物的附加價值，壓光處理已廣泛應用於功能性服飾上。本研究中，主要針對壓光不均產生的品質瑕疵，分別在壓光結構與壓光製程參數設定進行探討。在壓光結構的分析上，壓光壓力藉由上下滾輪相互接觸，壓力由上滾輪傳至下滾輪，織物通過上下滾輪間而受壓。在壓光過程中，上下滾輪因壓力而產生變形，導致織物受到的壓力不均，產生壓光瑕疵。本研究首先針對壓光機滾輪進行數學模式的推導以分析滾輪的動態特性。對於滾輪的變形，於下滾輪處設計一致動器，並導入適應性控制器以消除因上滾輪壓力而產生的下滾輪變形。另外，也改良傳統工業使用的比例積分微分控制器，加入專家系統，並以實驗比較兩種控制器的特性與對滾輪變形的改善；在壓光製程參數設定上，本研究針對壓光織物的多種品質特性：分別為壓光織物的反射率、透氣性及色差，設計一系列的田口實驗，找出符合各別品質特性的製程參數。在壓光過程中，控制因子設定分別為壓光壓力、溫度與速度。由於田口實驗可以有效率的對單一品質特性進行分析，對不同的品質特性得到不同的最佳控制條件，但對於多重品質特性的要求，本研究結合灰色關聯分析，分析實驗數據與期望品質之間的灰色關聯度，得到符合所有品質特性的最佳壓光參數組合，經由確認實驗後證實，此參數組合可以得到最佳的壓光品質。最後，經實務驗證的結果，本研究所設計的兩種控制器皆能有效改善滾輪的變形，並可有效抵抗外界干擾，降低滾輪對壓光的影響；同時，最佳的壓光參數組合，也可以確保得到織物最佳壓光品質。

Calendering is mainly applied in fabric to improve the surface luster. The luster fabric has been used in wide range of functional clothes as sport wear and wind coat, and etc. The calendered fabric can improve the additional value than fabric without calendering. In this study, the main issue is to investigate the cause of uneven on fabric luster. We consider two main parts that cause the uneven of fabric during calendering. At first part, we deal with the dynamic structure of calender roller system. The deflection occurred due to pressure on calender roller. The uneven pressure cause uneven fabric luster. Therefore, the mathematic model is built for analysis the dynamic of calender roller. The actuator is designed to eliminate the deflection by using model reference adaptive controller (MRAC) and the expert system (ES) method combined with the proportional–integral–derivative (PID) controller. The experiment by using the designed controller in the calender roller system could be shown that it not only effectively eliminate the steady state error, but also robust to disturbances.
In the second part, this study proposes an advanced quality control strategy for calendering, employing the gray relational analysis based on Taguchi method to plan a series of calendering experiments, setting the robust process parameters for calender. Our study focused on the use of gray relational analysis to deal with a fundamental limitation of the standard Taguchi approach in respect of optimization problems having multiple quality characteristics. The quality characteristics include reflectance, water vapor permeability, and color difference of calendered fabrics. In order to estimate the optimization parameters with multiple quality characteristics, gray relational analysis was also incorporated to set quality characteristics as reference sequences and decide the optimal parameter combinations. The results show that the calendered qualities are greatly improved and effectively determined the process parameters through this study.

[1] Kuo, C. F. J., and Fang, C. C., “An entire strategy for control of a calender roller System. part I: dynamic system modeling and controller design”, Textile Research Journal, Vol. 77, pp. 343–352 (2007).
[2] Usoro, P. B., Mahil, S. S., and Nadir, R., “A finite element/lagrange approach to modeling lightweight flexible manipulators”, ASME Journal Dynamic Systems, Measurement and Control, Vol. 108, No. 3, pp. 198–205 (1986).
[3] Hickin, J., and Sinha, N. K., “Model reduction for linear multivariable systems”, IEEE Transactions on Automatic Control, Vol. 25, No. 6, pp. 1121–1133 (1980).
[4] Chang, R. Y., and Wang, M. L., “Model reduction and control system design by shifted legendre polynomial functions”, ASME Journal Dynamic Systems, Measurement and Control, Vol. 105, No. 1, pp. 52–55 (1983).
[5] Crawley, E. F., “Use of piezo-ceramics as distributed actuators in large space structures”, Proceedings of Structures, Structural Dynamics and Materials Conference, Orlando, FL, AIAA Paper, No. 85-0626, PP. 126–133 (1986) .
[6] Miller, D. W., Crawley, E. F., and Ward, B. A., “Inertial actuator design for maximum passive and active energy dissipation in flexible space structures”, Proceedings of Structures, Structural Dynamics and Materials Conference, Orlando, FL, AIAA Paper, No. 85-0777, PP. 536–544 (1986) .
[7] Yoon H. S., and Washington G., “Active vibration confinement of flexible structures using piezo-ceramic patch actuators”, Journal of Intelligent Material Systems and Structures, Vol. 19, pp. 145-155 (2008).
[8] Michael A. D. and Fariba F., “Model reference adaptive control of structurally perturbed second-order distributed parameter systems”, International Journal of Robust and Nonlinear Control, Vol. 16, pp. 773-799 (2006).
[9] Ge, S. S., Lee, T. H., Zhu, G., Hong, F., “Variable structure control of a distributed-parameter flexible beam”, Journal of Robotic Systems, Vol. 18, No. 1, pp. 17-27 (2001).
[10] Herold, S., Mayer, D. and Hanselka, H., “Transient simulation of adaptive structures”, Journal of Intelligent Material Systems and Structures, Vol. 15 pp. 215-224 (2004).
[11] Mahmoodi, S. N. and Ahmadian, M., “Active vibration control with modified positive position feedback”, Journal of Dynamic Systems, Measurement, and Control, 131 041002-1-8 (2009).
[12] Tervo, J. and Nihtilä, M., “Exponential stability of a nonlinear distributed parameter system”, Journal of Analysis an Its Applications, Vol. 19, No. 1, pp. 77-93 (2000).
[13] Årzén, K. E., “An architecture for expert control systems based feedback control”, Automatica, Vol. 25, No. 6, pp. 813–827 (1989).
[14] Devanathan, R., Keong, C. C., Kin, L. T., and Soon, C. Y., “An expert PID controller”, IEEE International Workshop on Artificial Intelligence for Industrial Applications, pp. 525–530 (1988).
[15] Devanathan, R., Keong, C. C., Pei, T. L., and Ting, Y. T., “Expert PID controller for an industrial process”, IEEE International Workshop on Artificial Intelligence for Industrial Applications, pp. 404–407 (1989).
[16] Wenli, D., Mandan, L., and Feng, Q., “The application of real-time expert control system to large scale PTA plant”, IEEE Proceedings of the 4th World Congress on Intelligent Control and Automation, Vol. 1, pp. 450–453 (2001).
[17] Jinbang, X., Jin, Z., Ling, L., and Shuyun, W., “Expert PID controller for AC/DC converter”, IEEE Proceedings of The 5th World Congress on Intelligent Control and Automation, Vol. 1, pp. 5586–5590 (2004).
[18] Chee, F., Fernando, T. L., Savkin, A. V., and Heeden, P. V., “Expert PID control system for blood glucose control in critically patients”, IEEE Transactions on Information Technology in Biomedicine, Vol. 7, No. 4, pp. 419–425 (2003).
[19] Zobel, S. Maze, B., Tafreshi Vahedi, H., Wang, Q., and Pourdeyhimi, B., “Simulating permeability of 3-D calendered fibrous structures”, Chemical Engineering Science, Vol. 62, pp. 6285-6296 (2007).
[20] Mahapatra, S. S., Patnaik, A., and Satapathy, A., “Taguchi method applied to parametric appraisal of erosion behavior of GF-reinforced polyester composites”, Wear, Vol. 265, No. 1-2, pp. 214-222 (2008).
[21] Rout, B. K., and Mittal, R. K., “Tolerance design of robot parameters using Taguchi method”, Mechanical Systems and Signal Processing, Vol. 20, No. 8, pp. 1832-1852 (2006).
[22] Fowlkes, W. Y., and Creveling, C. M., Engineering Methods for Robust Product Design, Using Taguchi Methods in Technology and Product Development, Prentice Hall, New Jersey.
[23] Ross, P. J., Taguchi Techniques for Quality Engineering, Second Edition, McGraw-Hill, New York (1996).
[24] Lu, D., Antony, J., “Optimization of multiple responses using a fuzzy-rule based inference system”, International Journal of Production Research, Vol. 40, No. 7, pp. 1613-1625 (2002).
[25] Debabrata, M., Surjya, K. P., and Partha, S., “Back propagation neural network based modeling of multi-response of an electrical discharge machining process”, International Journal of Knowledge-Based and Intelligent Engineering Systems, Vol. 11, No. 2, pp. 105-113 (2007).
[26] Antony, J., Anand, R.B., Kumar, M., Tiwari, M.K., “Multiple response optimization using Taguchi methodology and neuro-fuzzy based model”, Journal of Manufacturing Technology Management, Vol. 17, No. 7, pp. 908-925 (2006).
[27] Deng, J., Gray Control System, Huazhong Engineering College Press, Wuhang, China (1985).
[28] Chan, J. W. K., and Tong, T. K. L., “Multi-criteria material selections and end-of-life product strategy: gray relational analysis approach”, Materials & Design, Vol. 28, No. 5, pp. 1539-1546 (2007).
[29] Morán, J., Granada, E., Míguez, J.L., Porteiro, J., “Use of gray relational analysis to assess and optimize small biomass boilers,” Fuel Processing Technology, Vol. 87, No. 2, PP. 123-127 (2006).
[30] Xin, G., You, H., and Xiao, Y., “Gray track-to-track correlation algorithm for distributed multi-target tracking system”, Signal Processing, Vol. 86, No. 11, pp. 3448-3455 (2006).
[31] Michael N., and Denis K., Fundamentals of Noise and Vibration Analysis for Engineers, Cambridge, UK.
[32] Haym B., Mechanical Vibration: Analysis, Uncertainties, and Control, Prentice Hall, New Jersey.
[33] Benson H. T., Principles of Vibration, Oxford, New York.
[34] Singiresu S. Rao, Mechanical Vibrations, Pearson, Singapore.
[35] Slotine, J. J. E. and Li, W., Applied Nonlinear Control, Pearson, Taiwan.
[36] Gang, T., Adaptive Control Design and Analysis, Wiley, New Jersey.
[37] Åström, K. J., Anton, J. J., and Årzén, K. E., “Expert control”, Automatica, Vol. 22, No. 3, pp. 277–286 (1986).
[38] Deng J., “Introduction to gray system theory”, The Journal of Gray System, Vol. 1, pp. 1-24 (1989).
[39] Guo H., “Identification coefficient of relational grade”, Fuzzy Mathematics, Vol. 15, No. 2, pp. 55-58 (1985).