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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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Proceedings ArticleDOI
Robust nonlinear control design for an IPMC by using ABC-based operator approach
TL;DR: In this article, a robust nonlinear control design for ionic polymer metal composite (IPMC) with uncertainties is proposed by using artificial bee colony based-operator approach, where a robust control system based on operator based robust robust right coprime factorization (RRCF) approach is presented.
Proceedings ArticleDOI
Design of a robust observer and its application for third order systems with disturbances
TL;DR: In this paper, a robust observer for third order minimum-phase dynamical systems with matched disturbances is presented, by using implicit observer techniques and VSS theory, and the robust observer is constructed for systems with arbitrarily relative degrees.
Proceedings ArticleDOI
Adaptive control for continuous-time systems in the presence of actuator and sensor hysteresis
Xinkai Chen,Ying Feng,Chun-Yi Su +2 more
TL;DR: A new adaptive control scheme is developed to compensate the plant, the actuator and the sensor uncertainties and to generate an adaptive estimate of the plant output, which ensures the uniform boundedness of all signals in the closed-loop system.
Errata Erratum to "Adaptive Sliding-Mode Position Control for Piezo-Actuated Stage"
Xinkai Chen,Takeshi Hisayama +1 more
TL;DR: The print version of the above named paper did not reflect the correct Digital Object Identifier.
Proceedings ArticleDOI
Observation for the perspective system
Xinkai Chen,Hiroyuki Kano +1 more
TL;DR: A new method is proposed to identify the obtained dynamical system with nonlinearities that is very simple and easy to be implemented, and it is robust to measurement noises.