Y
Yu Hui
Researcher at Qingdao University of Science and Technology
Publications - 16
Citations - 266
Yu Hui is an academic researcher from Qingdao University of Science and Technology. The author has contributed to research in topics: Nonlinear system & Iterative learning control. The author has an hindex of 4, co-authored 13 publications receiving 90 citations. Previous affiliations of Yu Hui include Beihang University.
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Extended State Observer-Based Data-Driven Iterative Learning Control for Permanent Magnet Linear Motor With Initial Shifts and Disturbances
TL;DR: An extended state observer-based data-driven iterative learning control for a permanent magnet linear motor (PMLM) that shows the robustness of the proposed method in the presence of iteration-varying initial shifts and disturbances is shown.
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Discrete-Time Extended State Observer-Based Model-Free Adaptive Control Via Local Dynamic Linearization
TL;DR: A local compact form dynamic linearization (local-CFDL) is developed at first to transform the original nonlinear nonaffine system into an affine structure consisting of both an unknown residual nonlinear time-varying term and a linearly parametric term affine to the control input.
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Adjacent-Agent Dynamic Linearization-Based Iterative Learning Formation Control
TL;DR: An adjacent-agent dynamic linearization-based iterative learning formation control (ADL-ILFC) is proposed for the child agent using 3-D control knowledge from iterations, time instants, and the parent agent.
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3-D Learning-Enhanced Adaptive ILC for Iteration-Varying Formation Tasks
TL;DR: A 3-D learning-enhanced adaptive iterative learning control (3D-AILC) is proposed by utilizing the additional control information from previous time instants, iterative operations, and parent agents to strengthen its learnability.
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Active Disturbance Rejection Control for Nonaffined Globally Lipschitz Nonlinear Discrete-time Systems
TL;DR: The proposed adaptive feedback controller considers desired reference trajectory, parameter adaption, error feedback, and the compensation of total uncertainties caused by parameter estimation error and unknown nonlinearity simultaneously to achieve better control performance.