H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Electronics and Electrical Engineering H-index 57 Citations 10,273 250 World Ranking 727 National Ranking 85

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Control theory
  • Computer vision

His scientific interests lie mostly in Control theory, Robot, Nonlinear system, Artificial neural network and Adaptive control. His Control theory research includes elements of Control engineering and Robot control. His Robot research includes themes of Simulation, Robotic arm and Trajectory.

His Nonlinear system study incorporates themes from Bounded function, Filter, Adaptive system and Robot manipulator. His research integrates issues of Manipulator and Robustness in his study of Artificial neural network. The Adaptive control study combines topics in areas such as Nonlinear control, Discrete time and continuous time and Reinforcement learning.

His most cited work include:

  • Composite Neural Dynamic Surface Control of a Class of Uncertain Nonlinear Systems in Strict-Feedback Form (245 citations)
  • Human-Like Adaptation of Force and Impedance in Stable and Unstable Interactions (226 citations)
  • Neural network-based motion control of an underactuated wheeled inverted pendulum model. (222 citations)

What are the main themes of his work throughout his whole career to date?

Chenguang Yang mostly deals with Control theory, Robot, Artificial intelligence, Artificial neural network and Computer vision. His study brings together the fields of Control engineering and Control theory. Chenguang Yang combines subjects such as Actuator and Motion control with his study of Control engineering.

His Robot study frequently draws connections to other fields, such as Simulation. His research in Artificial neural network intersects with topics in Control system, Stability, Lyapunov function, Robot manipulator and Robustness. His Control theory study integrates concerns from other disciplines, such as Admittance and Manipulator.

He most often published in these fields:

  • Control theory (54.81%)
  • Robot (53.69%)
  • Artificial intelligence (36.24%)

What were the highlights of his more recent work (between 2020-2021)?

  • Robot (53.69%)
  • Control theory (54.81%)
  • Control theory (20.58%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Robot, Control theory, Control theory, Artificial neural network and Artificial intelligence. The concepts of his Robot study are interwoven with issues in Motion, Actuator and Human–computer interaction. His research investigates the connection between Control theory and topics such as Admittance that intersect with issues in Linear system and Torque.

His multidisciplinary approach integrates Artificial neural network and Process in his work. In the subject of general Artificial intelligence, his work in Gesture, Transfer of learning and Deep learning is often linked to Generalization, thereby combining diverse domains of study. His research integrates issues of Adaptive control and Differentiator in his study of Control system.

Between 2020 and 2021, his most popular works were:

  • New Noise-Tolerant Neural Algorithms for Future Dynamic Nonlinear Optimization With Estimation on Hessian Matrix Inversion (28 citations)
  • Force Sensorless Admittance Control for Teleoperation of Uncertain Robot Manipulator Using Neural Networks (19 citations)
  • Asymmetric Bounded Neural Control for an Uncertain Robot by State Feedback and Output Feedback (19 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Control theory
  • Computer vision

His main research concerns Control theory, Robot, Robot manipulator, Control theory and Artificial neural network. His Control theory study frequently draws connections to adjacent fields such as Robot control. His study in Robot is interdisciplinary in nature, drawing from both Nonlinear programming, Approximation algorithm and Neural algorithms.

His Robot manipulator study combines topics in areas such as Robot kinematics, Bounded function, Lyapunov stability and Trajectory. His studies deal with areas such as Control system, Admittance, Lyapunov function, Observer and Teleoperation as well as Trajectory. His Artificial neural network research is multidisciplinary, incorporating perspectives in Control engineering, Motion, Impedance control and Human–robot interaction.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Neural Control of Bimanual Robots With Guaranteed Global Stability and Motion Precision

Chenguang Yang;Yiming Jiang;Zhijun Li;Wei He.
IEEE Transactions on Industrial Informatics (2017)

306 Citations

Neural network-based motion control of an underactuated wheeled inverted pendulum model.

Chenguang Yang;Zhijun Li;Rongxin Cui;Bugong Xu.
IEEE Transactions on Neural Networks (2014)

303 Citations

Human-Like Adaptation of Force and Impedance in Stable and Unstable Interactions

Chenguang Yang;G. Ganesh;S. Haddadin;S. Parusel.
IEEE Transactions on Robotics (2011)

298 Citations

Composite Neural Dynamic Surface Control of a Class of Uncertain Nonlinear Systems in Strict-Feedback Form

Bin Xu;Zhongke Shi;Chenguang Yang;Fuchun Sun.
IEEE Transactions on Systems, Man, and Cybernetics (2014)

284 Citations

Corrections to “Extended State Observer-Based Integral Sliding Mode Control for an Underwater Robot With Unknown Disturbances and Uncertain Nonlinearities”

Rongxin Cui;Lepeng Chen;Chenguang Yang;Mou Chen.
IEEE Transactions on Industrial Electronics (2017)

278 Citations

Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle

Bin Xu;Chenguang Yang;Yongping Pan.
IEEE Transactions on Neural Networks (2015)

263 Citations

Teleoperation Control Based on Combination of Wave Variable and Neural Networks

Chenguang Yang;Xingjian Wang;Zhijun Li;Yanan Li.
IEEE Transactions on Systems, Man, and Cybernetics (2017)

247 Citations

Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning

Rongxin Cui;Chenguang Yang;Yang Li;Sanjay Sharma.
IEEE Transactions on Systems, Man, and Cybernetics (2017)

239 Citations

Neural-Learning-Based Telerobot Control With Guaranteed Performance

Chenguang Yang;Xinyu Wang;Long Cheng;Hongbin Ma.
IEEE Transactions on Systems, Man, and Cybernetics (2017)

234 Citations

Output Feedback NN Control for Two Classes of Discrete-Time Systems With Unknown Control Directions in a Unified Approach

Chenguang Yang;Shuzhi Sam Ge;Cheng Xiang;Tianyou Chai.
IEEE Transactions on Neural Networks (2008)

234 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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