What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Mathematical analysis
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 most cited work include:
- Observer-Based Adaptive Fuzzy Backstepping Control for a Class of Stochastic Nonlinear Strict-Feedback Systems (450 citations)
- Barrier Lyapunov Functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints (441 citations)
- Barrier Lyapunov functions for Nussbaum gain adaptive control of full state constrained nonlinear systems (436 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Control theory (93.79%)
- Nonlinear system (68.36%)
- Adaptive control (62.71%)
What were the highlights of his more recent work (between 2019-2021)?
- Control theory (93.79%)
- Nonlinear system (68.36%)
- Artificial neural network (39.55%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- Multiple Lyapunov Functions for Adaptive Neural Tracking Control of Switched Nonlinear Nonlower-Triangular Systems (61 citations)
- Integral Barrier Lyapunov function-based adaptive control for switched nonlinear systems (59 citations)
- Barrier Lyapunov Function-Based Adaptive Fuzzy FTC for Switched Systems and Its Applications to Resistance–Inductance–Capacitance Circuit System (54 citations)
In his most recent research, the most cited papers focused on:
- Control theory
- Artificial intelligence
- Mathematical analysis
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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