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Chenguang Yang
Researcher at University of the West of England
Publications - 85
Citations - 2532
Chenguang Yang is an academic researcher from University of the West of England. The author has contributed to research in topics: Robot & Artificial neural network. The author has an hindex of 18, co-authored 85 publications receiving 1053 citations. Previous affiliations of Chenguang Yang include South China University of Technology & Stanford University.
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Admittance-Based Controller Design for Physical Human–Robot Interaction in the Constrained Task Space
TL;DR: It is proved that all states of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB) by utilizing the Lyapunov stability principles.
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Improved Human–Robot Collaborative Control of Redundant Robot for Teleoperated Minimally Invasive Surgery
TL;DR: An improved human–robot collaborative control scheme is proposed in a teleoperated minimally invasive surgery scenario, based on a hierarchical operational space formulation of a seven-degree-of-freedom redundant robot and results show that the accuracy of both the RCM constraint and the surgical tip is improved.
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Reinforcement Learning Control of a Flexible Two-Link Manipulator: An Experimental Investigation
TL;DR: The control design and experiment validation of a flexible two-link manipulator (FTLM) system represented by ordinary differential equations (ODEs) are discussed and a reinforcement learning (RL) control strategy is developed that is based on actor–critic structure to enable vibration suppression while retaining trajectory tracking.
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Force Sensorless Admittance Control With Neural Learning for Robots With Actuator Saturation
TL;DR: A sensorless admittance control scheme for robotic manipulators to interact with unknown environments in the presence of actuator saturation by employing Lyapunov stability theory and the stability of the closed-loop system is achieved.
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Deep Neural Network Approach in Robot Tool Dynamics Identification for Bilateral Teleoperation
TL;DR: A model-free based deep convolutional neural network (DCNN) structure is proposed for the tool dynamics identification, which features fast computation and noise robustness and provides superior accuracy for predicting the noised dynamics force and enable its feasibility for bilateral teleoperation.