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Xinkai Chen
Researcher at Shibaura Institute of Technology
Publications - 218
Citations - 3757
Xinkai Chen is an academic researcher from Shibaura Institute of Technology. The author has contributed to research in topics: Adaptive control & Control theory. The author has an hindex of 26, co-authored 201 publications receiving 3057 citations. Previous affiliations of Xinkai Chen include Electric Power University & Wakayama University.
Papers
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Journal ArticleDOI
Adaptive Pseudoinverse Control for Constrained Hysteretic Nonlinear Systems and its Application on Dielectric Elastomer Actuator
TL;DR: In this article , a fuzzy logic system (FLS) and barrier Lyapunov function (BLF) based adaptive pseudoinverse control scheme is proposed for a class of state-constrained hysteretic nonlinear systems, where all the states are always strictly limited in each constrained set.
Motion recovery by using stereo vision
Xinkai Chen,Hiroyuki Kano +1 more
TL;DR: The motion recovery for a class of movements in the space by using stereo vision is considered by observing at least three points and the estimations of the motion parameters which are all time-varying are developed in the proposed algorithm based on the second method of Lyapunov.
Journal ArticleDOI
Identification of a class of dynamics under perspective observation
Xinkai Chen,Hiroyuki Kano +1 more
TL;DR: In this article, a new discontinuous state observer, motivated by the sliding mode control method and adaptive techniques, is proposed for the obtained dynamical system, which is robust to measurement noises.
Proceedings Article
Adaptive control for uncertain systems in the presence of actuator and sensor hysteresis represented by Prandtl-Ishlinskii model
Ryo Oshima,Xinkai Chen +1 more
TL;DR: In this article, the output tracking control for a continuous-time linear plant containing uncertain hysteresis nonlinearities in actuator and sensor devices simultaneously, where the hystresis is described by the Prandtl-Ishlinskii model, is discussed.
Journal ArticleDOI
Linear quadratic tracking control of unknown systems: A two-phase reinforcement learning method
TL;DR: In this paper , the authors decompose the full-order discounted Riccati equation into a reduced-order Riccaci equation and a Sylvester equation, which facilitate designing the feedback and feedforward control gains.