2023 - Research.com Electronics and Electrical Engineering in China Leader Award
His primary scientific interests are in Control theory, Nonlinear system, Adaptive control, Lyapunov function and Bounded function. Yan-Jun Liu has researched Control theory in several fields, including Artificial neural network, Mathematical optimization and Fuzzy logic. His study looks at the intersection of Nonlinear system and topics like Adaptive system with Stability, Fault and Actuator.
In his study, Lyapunov stability is inextricably linked to Nonlinear control, which falls within the broad field of Adaptive control. His Lyapunov function research is multidisciplinary, incorporating perspectives in Discrete time and continuous time, Tracking error and Optimal control. His Backstepping study combines topics from a wide range of disciplines, such as Observer and Adaptive neuro fuzzy inference system.
His main research concerns Control theory, Nonlinear system, Adaptive control, Artificial neural network and Lyapunov function. His Control theory research includes themes of Bounded function and Fuzzy logic. His studies deal with areas such as State observer and Dead zone as well as Fuzzy logic.
The Nonlinear system study combines topics in areas such as Control system, Stability, Adaptive system and Observer. Yan-Jun Liu combines subjects such as Nonlinear control, Discrete time and continuous time, Tracking error, Mathematical optimization and Robustness with his study of Adaptive control. His Artificial neural network research is multidisciplinary, relying on both Mobile robot, Multi-agent system, Uniform boundedness, Actuator and Reinforcement learning.
His primary areas of investigation include Control theory, Nonlinear system, Artificial neural network, Control theory and Adaptive control. Yan-Jun Liu interconnects Bounded function and Fuzzy logic in the investigation of issues within Control theory. His Nonlinear system research incorporates elements of Stability, State variable and Dead zone.
His Artificial neural network research includes elements of Adaptive system and Robustness. His work carried out in the field of Adaptive control brings together such families of science as Control system, Servomechanism, Computer simulation and Stability theory. His research investigates the connection between Lyapunov function and topics such as Lyapunov stability that intersect with issues in Sliding mode control.
Yan-Jun Liu mostly deals with Control theory, Nonlinear system, Adaptive control, Artificial neural network and Control theory. His study in Control theory is interdisciplinary in nature, drawing from both Gradient descent and Fuzzy logic. His Nonlinear system study combines topics in areas such as State variable, Bounded function and Computer simulation.
His Bounded function research is multidisciplinary, incorporating elements of Instability, Interval and State observer. The concepts of his Adaptive control study are interwoven with issues in Servomechanism and Active suspension. His research integrates issues of Stability and Dynamical system in his study of Lyapunov function.
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Barrier Lyapunov functions for Nussbaum gain adaptive control of full state constrained nonlinear systems
Yan Jun Liu;Shaocheng Tong.
Barrier Lyapunov Functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints
Yan-Jun Liu;Shaocheng Tong.
Observer-Based Adaptive Fuzzy Backstepping Control for a Class of Stochastic Nonlinear Strict-Feedback Systems
Shaocheng Tong;Yue Li;Yongming Li;Yanjun Liu.
systems man and cybernetics (2011)
Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-Delay Systems Using Neural Networks
C. L. Philip Chen;Guo-Xing Wen;Yan-Jun Liu;Fei-Yue Wang.
IEEE Transactions on Neural Networks (2014)
Observer-Based Adaptive Backstepping Consensus Tracking Control for High-Order Nonlinear Semi-Strict-Feedback Multiagent Systems
C. L. Philip Chen;Guo-Xing Wen;Yan-Jun Liu;Zhi Liu.
IEEE Transactions on Systems, Man, and Cybernetics (2016)
Fuzzy Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Stochastic Systems
C. L. Philip Chen;Yan-Jun Liu;Guo-Xing Wen.
IEEE Transactions on Systems, Man, and Cybernetics (2014)
Adaptive control-based Barrier Lyapunov Functions for a class of stochastic nonlinear systems with full state constraints
Yan Jun Liu;Shumin Lu;Shaocheng Tong;Xinkai Chen.
Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints
Yan-Jun Liu;Jing Li;Shaocheng Tong;C. L. Philip Chen.
IEEE Transactions on Neural Networks (2016)
Adaptive Neural Output Feedback Tracking Control for a Class of Uncertain Discrete-Time Nonlinear Systems
Yan-Jun Liu;C. L. P. Chen;Guo-Xing Wen;Shaocheng Tong.
IEEE Transactions on Neural Networks (2011)
Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone
Yan-Jun Liu;Ying Gao;Shaocheng Tong;Yongming Li.
IEEE Transactions on Fuzzy Systems (2016)
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