An Efficient Observer-based ILC for Time Varying Nonlinear Systems

J.S.-H. Tsai, C.-H. Chen, S.-M. Guo (Taiwan), and L.-S. Shieh (USA)


Observer-based, Iterative learning control and LQR.


In this paper, an observer-based iterative learning control (OBILC) with linear quadratic regulator (LQR) observer is proposed for the tracking problem of a class of time-varying nonlinear systems, where the state information is not available. The proposed observer-based tracker yields a good tracking performance both in transient and steady state response. By linearizing the nonlinear system, we construct the state observer, based on the linear quadratic analog tracker (LQAT) design technique, with a high gain property first. Thereafter, we propose the iterative learning law, based on the LQR observer, ensuring the boundedness of the tracking error. Moreover, it is shown that if the initial state variables are known, a perfect convergence to zero, over a finite tracking horizon, can be obtained, when the number of iterations tends to infinity.

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