Iterative Learning Algorithm based on Observer and Linear Quadratic Performance Function
نویسندگان
چکیده
In this paper we propose an iterative learning algorithm based on observer and linear quadratic performance function. We calculate the initial control value for the iteractive learning algorithm based on the estimation of the states, which guarantees the efficient asymptotic tracking of any desired trajectories. Furthermore, with Linear quadratic optimal control theory, we obtain the optimized control value for the interactive progress by minimizing the performance function. Finally, we simulate the performance our ILC algorithm and it shows that this new method can provide the initial control value for the uncertain linear timeinvariant systems, as well as decrease the tracking errors asymptotically in the interactive progress.
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