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.
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.
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.
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.
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)
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)
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)
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)
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)
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)
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)
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)
Neural-Learning-Based Telerobot Control With Guaranteed Performance
Chenguang Yang;Xinyu Wang;Long Cheng;Hongbin Ma.
IEEE Transactions on Systems, Man, and Cybernetics (2017)
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)
Profile was last updated on December 6th, 2021.
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University of Science and Technology of China
Concordia University
University of Science and Technology Beijing
Nanjing University of Science and Technology
National University of Singapore
National University of Singapore
University of Manchester
Chinese Academy of Sciences
Kunming University of Science and Technology
Imperial College London
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