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Xiaoping Liu
Researcher at Lakehead University
Publications - 232
Citations - 10328
Xiaoping Liu is an academic researcher from Lakehead University. The author has contributed to research in topics: Nonlinear system & Backstepping. The author has an hindex of 46, co-authored 213 publications receiving 8049 citations. Previous affiliations of Xiaoping Liu include Liaoning University & University of Science and Technology, Liaoning.
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Brief paper: Direct adaptive fuzzy control of nonlinear strict-feedback systems
TL;DR: This paper focuses on adaptive fuzzy tracking control for a class of uncertain single-input /single-output nonlinear strict-feedback systems and a novel direct adaptive fuzzy Tracking controller is constructed via backstepping.
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Robust Adaptive Fuzzy Tracking Control for Pure-Feedback Stochastic Nonlinear Systems With Input Constraints
TL;DR: The proposed adaptive fuzzy tracking controller guarantees that all signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighborhood of the desired reference signal in the sense of mean quartic value.
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Adaptive Fuzzy Control of a Class of Nonlinear Systems by Fuzzy Approximation Approach
TL;DR: A variable separation approach is developed to overcome the difficulty from the nonstrict-feedback structure and a state feedback adaptive fuzzy tracking controller is proposed, which guarantees that all of the signals in the closed-loop system are bounded, while the tracking error converges to a small neighborhood of the origin.
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Finite-Time Adaptive Fuzzy Tracking Control Design for Nonlinear Systems
TL;DR: A novel adaptive fuzzy control scheme is proposed by a backstepping technique that can guarantee that the tracking error converges to a small neighborhood of the origin in a finite time, and the other closed-loop signals remain bounded.
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Adaptive Neural Control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique
Min Wang,Xiaoping Liu,Peng Shi +2 more
TL;DR: Under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced significantly, and semiglobal uniform ultimate boundedness of all of the signals in the closed-loop system is guaranteed.