Adaptive fuzzy ILC of nonlinear discrete-time systems with unknown dead zones and control directions |
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Authors: | Qing-Yuan Xu |
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Affiliation: | 1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, People’s Republic of China;2. Key Lab of Machine Intelligence and Advanced Computing (Sun Yat-sen University), Ministry of Education, Guangzhou, People’s Republic of China;3. School of Electrical and Computer Engineering, Nanfang College of Sun Sat-sen University, Guangzhou, People’s Republic of China |
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Abstract: | This paper presents an adaptive fuzzy iterative learning control (ILC) design for non-parametrized nonlinear discrete-time systems with unknown input dead zones and control directions. In the proposed adaptive fuzzy ILC algorithm, a fuzzy logic system (FLS) is used to approximate the desired control signal, and an additional adaptive mechanism is designed to compensate for the unknown input dead zone. In dealing with the unknown control direction of the nonlinear discrete-time system, a discrete Nussbaum gain technique is exploited along the iteration axis and applied to the adaptive fuzzy ILC algorithm. As a result, it is proved that the proposed adaptive fuzzy ILC scheme can drive the ILC tracking errors beyond the initial time instants into a tunable residual set as iteration number goes to infinity, and keep all the system signals bounded in the adaptive ILC process. Finally, a simulation example is used to demonstrate the feasibility and effectiveness of the adaptive fuzzy ILC scheme. |
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Keywords: | Adaptive fuzzy iterative learning control (ILC) unknown input dead zone unknown control direction discrete Nussbaum gain fuzzy logic system (FLS) |
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