His main research concerns Control theory, Mathematical optimization, Multi-agent system, Lyapunov function and Network topology. The concepts of his Control theory study are interwoven with issues in Control engineering and Algebraic graph theory. His Mathematical optimization research incorporates elements of Energy consumption, Convergence, Lyapunov stability and Convex optimization.
His work carried out in the field of Multi-agent system brings together such families of science as Identifier, Control, Tracking and Protocol. His Lyapunov function research is under the purview of Nonlinear system. He has researched Network topology in several fields, including Stability and Consensus.
His primary scientific interests are in Control theory, Mathematical optimization, Multi-agent system, Lyapunov function and Nash equilibrium. All of his Control theory and Control theory, Nonlinear system, Adaptive control, Robust control and Robustness investigations are sub-components of the entire Control theory study. Guoqiang Hu focuses mostly in the field of Mathematical optimization, narrowing it down to matters related to Energy consumption and, in some cases, Smart grid.
His studies in Multi-agent system integrate themes in fields like Distributed computing, Tracking, Telecommunications network, Network topology and Bounded function. His research in Lyapunov function intersects with topics in Control engineering, Robot, Artificial intelligence, Computer vision and Servo control. His work on Best response and Epsilon-equilibrium as part of general Nash equilibrium study is frequently connected to Constant, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Mathematical optimization, Nash equilibrium, Optimization problem, Multi-agent system and Convergence are his primary areas of study. His Mathematical optimization research includes themes of Function, Graph and Range. His Nash equilibrium research is multidisciplinary, incorporating elements of Penalty method, Strongly monotone and Protocol.
His research on Multi-agent system also deals with topics like
Guoqiang Hu spends much of his time researching Mathematical optimization, Optimization problem, Function, Multi-agent system and HVAC. His Mathematical optimization research is multidisciplinary, incorporating perspectives in Convergence and Linearization, Nonlinear system. His Convergence study combines topics in areas such as Resource allocation and Nash equilibrium.
Guoqiang Hu has included themes like Entropy and Real-time computing in his Optimization problem study. His Function research integrates issues from Linear programming, Rate of convergence, Time domain and Directed graph. His Multi-agent system research also works with subjects such as
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Cloud robotics: architecture, challenges and applications
Guoqiang Hu;Wee Peng Tay;Yonggang Wen.
IEEE Network (2012)
Robust consensus tracking of a class of second-order multi-agent dynamic systems
conference on decision and control (2010)
Time-varying formation control for general linear multi-agent systems with switching directed topologies
Xiwang Dong;Guoqiang Hu.
Lyapunov-Based Tracking Control in the Presence of Uncertain Nonlinear Parameterizable Friction
C. Makkar;G. Hu;W.G. Sawyer;W.E. Dixon.
IEEE Transactions on Automatic Control (2007)
Consensus tracking for higher-order multi-agent systems with switching directed topologies and occasionally missing control inputs
Guanghui Wen;Guoqiang Hu;Wenwu Yu;Jinde Cao;Jinde Cao.
Systems & Control Letters (2013)
Time-Varying Formation Tracking for Linear Multiagent Systems With Multiple Leaders
Xiwang Dong;Guoqiang Hu.
IEEE Transactions on Automatic Control (2017)
A new continuously differentiable friction model for control systems design
C. Makkar;W.E. Dixon;W.G. Sawyer;G. Hu.
international conference on advanced intelligent mechatronics (2005)
Pinning Synchronization of Directed Networks With Switching Topologies: A Multiple Lyapunov Functions Approach
Guanghui Wen;Wenwu Yu;Guoqiang Hu;Jinde Cao.
IEEE Transactions on Neural Networks (2015)
The adaptive distributed observer approach to the cooperative output regulation of linear multi-agent systems
He Cai;Frank L. Lewis;Guoqiang Hu;Jie Huang.
Distributed Energy Consumption Control via Real-Time Pricing Feedback in Smart Grid
Kai Ma;Guoqiang Hu;Costas J. Spanos.
IEEE Transactions on Control Systems and Technology (2014)
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