本論文提出一種適用於具有內部未知連結大尺度資料取樣之多輸入多輸出非線性系統，且具有閉迴路解耦特性和輸入限制的分散式反覆學習控制追蹤器。首先，利用離線式的觀測器/卡爾曼濾波器鑑別方法求出此大尺度系統之適當階數(或低階)的分散式線性觀測器。為了克服每一個子系統鑑別線性模型時模型誤差所造成的影響，提出一種基於數位重新設計方法改進的高增益效應觀測器。為了達到追蹤的目的，將具有輸入限制的反覆學習演算控制器嵌入在分散式的模型中，利用連續投影的方式來制定限制型反覆學習控制的法則。基於此種投影方法，提出一種演算法來求得限制反覆學習控制。為了減少學習代數，運用具有高增益特性的數位再設計軌跡追蹤器設計方法決定反覆學習控制器的初始輸入。最後，提出兩個例子來說明此方法的可行性。

This thesis proposes the decentralized iterative learning control (ILC) for a class of unknown sampled-data interconnected large-scale nonlinear systems consisting of multi-input multi-output (MIMO) subsystems with a closed-loop decoupling property via the observer/Kalman filter identification (OKID) method and convex control input constraints. First, the off-line OKID method is utilized to determine decentralized appropriate (low-) order of discrete-time linear models for the class of unknown interconnected large-scale sampled-data systems by using known input-output sampled data. Then, to overcome the effect of modeling error on the identified linear model of each subsystem, an improved observer with the high-gain property based on the digital redesign approach is presented. For the tracking purpose, an norm-optimal ILC scheme is embedded to the decentralized models, and the constrained ILC problem is formulated in a successive projection framework. Based on the projection method, the algorithm is proposed to solve this constrained ILC. To reduce unwanted learning cycles, the digital-redesign linear quadratic tracker with the high-gain property is proposed to assign the initial control input of ILC. Finally, two illustrative examples are given to demonstrate the effectiveness of the proposed methodologies.

[2] H. S. Wu, “Decentralized robust-control for a class of large-scale interconnected systems with uncertainties,” International Journal of Systems Science, vol. 20, no. 12, pp. 2597-2608, 1989.
[3] M. Jamshidi, Large-Scale Systems: Modeling, Control, and Fuzzy Logic. New York, Prentice-Hall, 1996.
[4] E. J. Davison, “The robust decentralized control of servomechanism problem for composite system with input-output interconnection,” IEEE Transactions on Automatic Control, vol. 24, no. 2, pp. 325-327, 1979.
[5] P. A. Ioannou and J. Sun, Robust Adaptive Control. Englewood, Prentice-Hall, 1996.
[6] D. T. Gavel and D. D. Seljuk, “Decentralized adaptive control: Structural conditions for stability,” IEEE Transactions on Automatic Control, vol. 34, no. 4, pp. 413-426, 1989.
[7] J. N. Juang, Applied System Identification. Englewood Cliffs. NJ, Prentice-Hall, 1994.
[8] J. N. Juang, M. Q. Phan, L. G. Horta, and R. W. Longman, “Identification of observer/Kalman filter Markov parameters: Theory and experiments,” Journal of Guidance Control and Dynamics, vol. 16, no. 2, pp. 320-329, 1991.
[9] S. Arimoto, S. Kawamura, and F. Miyazaki, “Bettering operation of robots by learning,” Journal of Robotic Systems, vol. 1, no. 2, pp. 123-140, 1984.
[10] D. A. Bristow, M. Tharayil, and A. G. Alleyne, “A Survey of Iterative Learning Control,” IEEE Control Systems Magazine, vol. 26, no. 3, pp. 96-114, 2006.
[11] M. Norrlof, “An adaptive iterative learning control algorithm with experiments on an industrial robot,” IEEE Transactions on Robotics and Automation, vol. 18, no. 2, pp. 245-251, 2002.
[12] D. I. Kim and S. Kim, “An iterative learning control method with application for CNC machine tools,” IEEE Industry Applications, vol. 32, no. 1, pp. 66-72, 1996.
[13] C. Mi, H. Lin, and Y. Zhang, “Iterative learning control of antilock braking of electric and hybrid vehicles,” IEEE Transactions Vehicular Technology, vol. 54, no. 2, pp. 486-494, 2005.
[14] B. Chu and D. H. Owens, “Accelerated Norm-optimal Iterative Learning Control Algorithms using Successive Projection”, International Journal of Process Control, vol. 82, no. 8, pp. 1469-1484, 2008.
[15] B. Chu and D.H. Owens, “Iterative learning control for constrained linear systems”, International journal of Control, vol. 83, no. 7, pp. 1397-1413, 2010.
[16] D. H. Owens and R. P Jones, “Iterative Solution of Constrained Differential/Algebraic Systems”, International Journal of Control, vol. 27, no. 6, pp. 957-974, 1978.
[17] N. Amann, D. H. Owens, and E. Rogers, “Predictive Optimal Iterative Learning Control,” International Journal of Control, vol. 69, no. 2, pp. 203-226, 1998.
[18] J. S. H. Tsai, T. H. Chien, S. M. Guo, Y. P. Chang, and L. S. Shieh, “State-space self-tuning control for stochastic fractional-order chaotic systems,” IEEE Transactions on Circuits and Systems I-Regular Papers, vol. 54, no. 3, pp. 632-642, 2007.
[19] F. L. Lewis and V. L. Syrmos, Optimal Control. New York, John Wiley & Sons, 1995.
[20] S. M. Guo, L. S. Shieh, G. R. Chen, and C. F. Lin, “Effective chaotic orbit tracker: A prediction-based digital redesign approach,” IEEE Transactions on Circuits and Systems I-Fundamental Theory and Applications, vol. 47, no. 11, pp. 1557-1570, 2000.
[21] S. M. Guo and Z. H. Peng, “An observer-based decentralized tracker for sampled-data systems: an evolutionary programming approach,” International Journal of General Systems, vol. 34, no. 4, pp. 421-449, 2005.
[22] M. Phan, L. G. Horta, J. N. Juang, and R. W. Longman, “Linear system identification via an asymptotically stable observer,” Journal of Optimization Theory and Applications, vol. 79, no. 1, pp. 59-86, 1993.
[23] G. Chen and T. Ogorzalek, “Yet another chaotic attractor,” International Journal of Bifurcation Chaos, vol. 9, no. 7, pp.1465-1466, 1999.
[24] X. Yu and Y. Xia, “Detecting unstable periodic orbits in Chen’s chaotic attractor,” International Journal of Bifurcation and Chaos, vol. 10, no. 8, pp. 1987-1991, 2000.