In this paper, a novel observer/Kalman filter identification (OKID) based iterative learning control (ILC) for a class sample-data nonlinear system is proposed and supplies a good tracking performance in both the transient and steady-state phase. The proposed observer-based digital redesign tracker can suppress the uncertainties and the nonlinear perturbations. First, even without resetting the identical initial condition the optimal linear model of the analog nonlinear system is constructed at the operating point. The operating point is generated due to the analog observer updated by the well-designed analog OKID-ILC nonlinear system. Thereafter, the linear quadratic regulator design technique with a high-gain property is applied to design an analog observer-based tracker of the optimal linear model. Finally, the proposed approach for the analog system is then extended to the case for a class of sampled-data nonlinear systems.
Ahn, H.S., Choi, C.H., and Kim, K.B., 1993, ‘‘Iterative learning control for a class of nonlinear systems,’’ Automatica29(6), 1575-1578.
2.
Arimoto, S., 1990, ‘‘Learning control theory for robotic motion ,’’ International Journal of Adaptive Control and Signal Processing4(6), 543-564.
3.
Arimoto, S., Kawamura, S., and Miyazaki, F., 1984, ‘‘Bettering operation of robots by learning ,’’ Journal of Robotic Systems1(2), 123-140.
4.
Chang, W., Park, Y.H., Joo, Y.H., and Chen, G., 2002, ‘‘Design of sampled-data fuzzy-model-based control systems by using intelligent digital redesign,’’ IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications49(4), 509-517.
5.
Chen, G. and Ueta, T., 1999, ‘‘Yet another chaotic attractor, ’’ International Journal of Bifurcation and Chaos9, 1465- 1466.
6.
Guo, S.M., Shieh, L.S., Chen, G., and Lin, C.F., 2000, ‘‘Effective chaotic orbit tracker: a prediction-based digital redesign approach,’’ IEEE Transactions on Circuits and Systems I, Fundamental Theory and Applications47(11), 1557-1570.
7.
Guo, S.M., Shieh, L.S., Lin, C.F., and Chandra, J., 2001, ‘‘Statespace self-tuning control for nonlinear stochastic and chaotic hybrid systems,’’ International Journal of Bifurcation Chaos11(4), 1079-1113.
8.
Juang, J.N., 1994, Applied System Identification, Prentice-HallEnglewood Cliffs. NJ.
9.
Joo, Y.H., Chen, G., and Shieh, L.S., 1999, ‘‘Hybrid state-space fuzzy model-based controller with dual-rate sampling for digital control of chaotic systems, ’’ IEEE Transactions Fuzzy Systems7(4), 394-408.
10.
Kuo, C., 1980, Digital Control System, Holt, Rinehart, and Winston, New York.
11.
Kuc, T.Y., Lee, J.S., and Nam, K., 1992, ‘‘An iterative learning control theory for a class of nonlinear dynamic systems,’’ Automatica28(6), 1215-1221.
12.
Lee, H.J., Park, J.B., and Joo, Y.H., 2003, ‘‘An efficient observer-based sampled-data control: digital redesign approach,’’IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications50(12), 1595-1601.
13.
Moore, K.L., 1999, ‘‘Iterative learning control - an expository overview,’’ Applied and Computational Control, Signal, and Circuits1, 151-214.
14.
Norrlof, M., 2002, ‘‘An adaptive iterative learning control algorithm with experiments on an industrial robot,’’IEEE Transactions on Robotics and Automation18(2), 245-251.
15.
Phan, M., Horta, L.G., Juang, J.N. and Longman, R.W., 1993, ‘‘Linear system identification via an asymptotically stable observer,’’ Journal of Optimization Theory and Applications79(1), 59-86.
16.
Rajamani, R., 1998, ‘‘Observers for Lipschitz nonlinear systems ,’’ IEEE Transactions Automatic Control43(3), 397-401.
17.
Shieh, L.S., Wang, W., and Tsai, J.S.H., 1999, ‘‘Optimal digital redesign of hybrid uncertain systems using genetic algorithms,’’ IEEE Processing - Control Theory Applications146(2), 119-130.
18.
Shieh, L.S., Wang, W.M., Bain, J., and Chandra, J., 2000, ‘‘Design of lifted dual-rate digital controllers for X-38 vehicle,’’Journal of Guidance, Control and Dynamics, 23(4), 629-639.
19.
Tayebi, A. and Xu, J.X., 2003, ‘‘Observer-based iterative learning control for a class of time-varying nonlinear systems,’’ IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications50(3), 452-455.
20.
Tsai, J.S.H., Chien, T.H., Guo, S.M., Chang, Y.P., and Shieh, L.S., 2007, ‘‘State-space self-tuning control for stochastic fractional-order chaotic systems,’’ IEEE Transactions on Circuits and Systems I: Regular Papers54(3), 632-642.
21.
Xu, J., Chen, G. and Shieh, L.S., 1996, ‘‘Digital redesign for controlling chaotic Chua’s circuit,’’ IEEE Transactions on Aerospace and Electronic Systems32, 1488-1499.
22.
Xu, J.X. and Qu, Z., 1996, ‘‘Robust learning control for a class of nonlinear systems,’’ in Proceedings of the IEEE 35th Conference on Decision and Control, Kobe, Japan, December 11- 13, 3, pp. 2484-2489.
23.
Xu, X.X. and Xu, J.X., 2004, ‘‘Observer based learning control for a class of nonlinear systems with time-varying parametric uncertainties, ’’ IEEE Transactions on Automatic Control49(2), 275-281.
24.
Yokoyama, T., Kosuge, K. and Furuta, K., 1989, ‘‘Digital redesign of robot control,’’ in Proceedings of IEEE Industrial Electronics Society of the 15th Annual Conference, Philadelphia, PA, November 6-10, 2, pp. 409-414.
