His scientific interests lie mostly in Control theory, Nonlinear system, Multi-agent system, Nonlinear control and Robust control. Jie Huang has included themes like Control engineering and Consensus in his Control theory study. His work on Backstepping as part of general Nonlinear system study is frequently linked to Class, therefore connecting diverse disciplines of science.
His Nonlinear control research is multidisciplinary, relying on both Control system and Output feedback. His Robust control study incorporates themes from Function, Polynomial and Automatic control. His Adaptive control research focuses on Bounded function and how it relates to Dynamical system.
Control theory, Nonlinear system, Multi-agent system, Internal model and Nonlinear control are his primary areas of study. Jie Huang interconnects Control and Mathematical optimization in the investigation of issues within Control theory. His Nonlinear system research includes elements of Control engineering, Servomechanism, Class and Robustness.
In general Multi-agent system, his work in Consensus is often linked to Telecommunications network linking many areas of study. His Internal model study combines topics in areas such as Event triggered, Special case and Van der Pol oscillator. The Robust control study combines topics in areas such as Automatic control, Exponential stability, Linear system and Polynomial.
Jie Huang mainly investigates Control theory, Multi-agent system, Observer, Consensus and Control. His work on Internal model, Nonlinear system and Output feedback as part of general Control theory study is frequently linked to Class, bridging the gap between disciplines. Many of his research projects under Nonlinear system are closely connected to Construct and Triangular matrix with Construct and Triangular matrix, tying the diverse disciplines of science together.
His studies in Multi-agent system integrate themes in fields like Mathematical optimization, Discrete time and continuous time and Adaptive control. The study incorporates disciplines such as Dimension, Class and Observer in addition to Observer. His Leader following study, which is part of a larger body of work in Consensus, is frequently linked to Rigid body, bridging the gap between disciplines.
Jie Huang spends much of his time researching Control theory, Multi-agent system, Consensus, Observer and Nonlinear system. Jie Huang combines subjects such as Control engineering and Control, Event triggered with his study of Control theory. In his work, Full state feedback is strongly intertwined with Robust control, which is a subfield of Event triggered.
His work carried out in the field of Multi-agent system brings together such families of science as Discrete time and continuous time, Adaptive control and State. The various areas that he examines in his Consensus study include Path and Lyapunov function. Jie Huang has researched Nonlinear system in several fields, including Linear system and Robustness.
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Nonlinear Output Regulation: Theory and Applications
Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form
D. Wang;Jie Huang.
IEEE Transactions on Neural Networks (2005)
On an output feedback finite-time stabilisation problem
Y. Hong;J. Huang;Y. Xu.
conference on decision and control (1999)
On an output feedback finite-time stabilization problem
Yigwruang Hong;Jie Huang;Yangsheng Xu.
IEEE Transactions on Automatic Control (2001)
Cooperative Output Regulation of Linear Multi-Agent Systems
Youfeng Su;Jie Huang.
IEEE Transactions on Automatic Control (2012)
Finite-time control for robot manipulators ☆
Yiguang Hong;Yangsheng Xu;Jie Huang.
Systems & Control Letters (2002)
A general framework for tackling the output regulation problem
Jie Huang;Zhiyong Chen.
IEEE Transactions on Automatic Control (2004)
A Distributed Control Approach to A Robust Output Regulation Problem for Multi-Agent Linear Systems
Xiaoli Wang;Yiguang Hong;Jie Huang;Zhong-Ping Jiang.
IEEE Transactions on Automatic Control (2010)
On a nonlinear multivariable servomechanism problem
J. Huang;W. J. Rugh.
Brief Adaptive neural network control for a class of uncertain nonlinear systems in pure-feedback form
Dan Wang;Jie Huang.
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