What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Machine learning
His main research concerns Control theory, Nonlinear system, Backstepping, Adaptive control and Fuzzy logic.
Fuzzy control system, State observer, Adaptive neuro fuzzy inference system, Adaptive system and Control system are the subjects of his Control theory studies.
His Nonlinear system research includes themes of Artificial neural network and Bounded function.
His work carried out in the field of Artificial neural network brings together such families of science as Dimension and Stability.
His Adaptive control research is multidisciplinary, incorporating perspectives in Nonlinear control and Robust control.
His Fuzzy logic research integrates issues from Observer, Lyapunov function and Mathematical optimization.
His most cited work include:
- DSC Approach to Robust Adaptive Fuzzy Tracking Control for Strict-Feedback Nonlinear Systems (395 citations)
- Observer-Based Adaptive Fuzzy Tracking Control of MIMO Stochastic Nonlinear Systems With Unknown Control Directions and Unknown Dead Zones (357 citations)
- Observer-Based Adaptive Fuzzy Backstepping Dynamic Surface Control for a Class of MIMO Nonlinear Systems (331 citations)
What are the main themes of his work throughout his whole career to date?
Tieshan Li mainly focuses on Control theory, Nonlinear system, Control theory, Backstepping and Adaptive control.
His Control theory research includes elements of Artificial neural network, Bounded function and Fuzzy logic.
His research in Nonlinear system intersects with topics in Control system, Control, Function and Dimension.
His Control theory research focuses on Robustness and how it connects with Active disturbance rejection control.
His studies deal with areas such as Observer, State variable, Actuator and Robust control as well as Backstepping.
His Adaptive control research incorporates elements of Nonlinear control, Mathematical optimization and Adaptive system.
He most often published in these fields:
- Control theory (84.62%)
- Nonlinear system (50.48%)
- Control theory (45.19%)
What were the highlights of his more recent work (between 2017-2021)?
- Control theory (84.62%)
- Nonlinear system (50.48%)
- Artificial neural network (35.58%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Control theory, Nonlinear system, Artificial neural network, Control theory and Backstepping.
Tieshan Li interconnects Multi-agent system and Bounded function in the investigation of issues within Control theory.
The concepts of his Nonlinear system study are interwoven with issues in Convergence, Mathematical optimization, Collision avoidance and Dead zone.
Tieshan Li has researched Artificial neural network in several fields, including Support vector machine, Adaptive system, Optimal control and Reinforcement learning.
His Control theory study incorporates themes from Topology, Vehicle dynamics and Stability theory.
His study in Backstepping is interdisciplinary in nature, drawing from both Observer, State variable and Tracking error.
Between 2017 and 2021, his most popular works were:
- Event-Triggered Finite-Time Control for Networked Switched Linear Systems With Asynchronous Switching (150 citations)
- Finite-Time Formation Control of Under-Actuated Ships Using Nonlinear Sliding Mode Control (67 citations)
- Adaptive Reinforcement Learning Neural Network Control for Uncertain Nonlinear System With Input Saturation (57 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Machine learning
His primary areas of study are Control theory, Nonlinear system, Artificial neural network, Backstepping and Control theory.
His Control theory study frequently draws connections between related disciplines such as Bounded function.
His biological study focuses on Adaptive control.
His studies in Artificial neural network integrate themes in fields like Lyapunov stability and Reinforcement learning.
The Backstepping study combines topics in areas such as Observer, Tracking error, State variable and Fuzzy logic.
His work in the fields of Control theory, such as Underactuation and Sliding mode control, intersects with other areas such as Boost converter and Singularity.
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