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http://dx.doi.org/10.14775/ksmpe.2022.21.08.032

Asymptotic Output Tracking of Non-minimum Phase Nonlinear Systems through Learning Based Inversion  

Kim, Nam Guk (R&D Center, Cybernetics Imaging Systems)
Publication Information
Journal of the Korean Society of Manufacturing Process Engineers / v.21, no.8, 2022 , pp. 32-42 More about this Journal
Abstract
Asymptotic tracking of a non-minimum phase nonlinear system has been a popular topic in control theory and application. In this paper, we propose a new control scheme to achieve asymptotic output tracking in anon-minimum phase nonlinear system for periodic trajectories through an iterative learning control with the stable inversion. The proposed design method is robust to parameter uncertainties and periodic external disturbances since it is based on iterative learning. The performance of the proposed algorithm was demonstrated through the simulation results using a typical non-minimum nonlinear system of an inverted pendulum on a cart.
Keywords
Learning Control; Non-minimum Phase; Asymptotic Output Tracking; Inverted Pendulum;
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