What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Mechanical engineering
Jing Na spends much of his time researching Control theory, Adaptive control, Nonlinear system, Estimation theory and Control engineering.
Lyapunov function, Tracking error, Torque, System dynamics and Robustness are among the areas of Control theory where the researcher is concentrating his efforts.
His work on Backstepping as part of general Adaptive control study is frequently linked to Rate of convergence, therefore connecting diverse disciplines of science.
Jing Na interconnects Artificial neural network, Overshoot and Active suspension, Suspension in the investigation of issues within Nonlinear system.
He works mostly in the field of Artificial neural network, limiting it down to concerns involving Dead zone and, occasionally, Synchronous motor.
As a part of the same scientific family, he mostly works in the field of Estimation theory, focusing on Vehicle dynamics and, on occasion, Gradient descent.
His most cited work include:
- Adaptive Prescribed Performance Motion Control of Servo Mechanisms with Friction Compensation (290 citations)
- Robust adaptive finite-time parameter estimation and control for robotic systems (166 citations)
- Adaptive Parameter Estimation and Control Design for Robot Manipulators With Finite-Time Convergence (151 citations)
What are the main themes of his work throughout his whole career to date?
Jing Na mainly investigates Control theory, Nonlinear system, Adaptive control, Artificial neural network and Estimation theory.
His Control theory study frequently draws connections between related disciplines such as Control engineering.
His Nonlinear system study incorporates themes from Control system and Observer.
His Adaptive control study combines topics in areas such as Reference model, Transient response, Adaptive algorithm and System dynamics.
He works mostly in the field of Artificial neural network, limiting it down to topics relating to Servomechanism and, in certain cases, Servo and Servomotor, as a part of the same area of interest.
His biological study deals with issues like Vehicle dynamics, which deal with fields such as Torque.
He most often published in these fields:
- Control theory (83.72%)
- Nonlinear system (36.28%)
- Adaptive control (33.49%)
What were the highlights of his more recent work (between 2019-2021)?
- Control theory (83.72%)
- Nonlinear system (36.28%)
- Control system (11.16%)
In recent papers he was focusing on the following fields of study:
Jing Na mostly deals with Control theory, Nonlinear system, Control system, Adaptive control and Estimation theory.
His research integrates issues of Artificial neural network and Filter in his study of Control theory.
His Nonlinear system research focuses on Fuzzy logic and how it connects with Adaptive algorithm.
His Control system research incorporates themes from Stability, CarSim and Observer.
The Adaptive control study combines topics in areas such as Trajectory and Reference model.
His Estimation theory study which covers Servomotor that intersects with Backstepping.
Between 2019 and 2021, his most popular works were:
- USDE-Based Sliding Mode Control for Servo Mechanisms With Unknown System Dynamics (39 citations)
- Adaptive Finite-Time Fuzzy Control of Nonlinear Active Suspension Systems With Input Delay (39 citations)
- Neural-Network-Based Adaptive Funnel Control for Servo Mechanisms With Unknown Dead-Zone (39 citations)
In his most recent research, the most cited papers focused on:
- Control theory
- Artificial intelligence
- Mechanical engineering
His primary areas of investigation include Control theory, Nonlinear system, Servomotor, Estimation theory and Adaptive control.
His work carried out in the field of Control theory brings together such families of science as Artificial neural network and Filter.
CarSim, Suspension, Active suspension and Adaptive algorithm is closely connected to Fuzzy logic in his research, which is encompassed under the umbrella topic of Nonlinear system.
While the research belongs to areas of Servomotor, he spends his time largely on the problem of Servomechanism, intersecting his research to questions surrounding Backstepping.
His Adaptive control research is multidisciplinary, incorporating elements of Vehicle dynamics and Fuzzy control system.
The concepts of his Tracking error study are interwoven with issues in Control system and System dynamics.
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