Lecturer at School of Computing | University of Derby
About Dr. Quang Dan Le
Dr. Quang Dan Le is a Lecturer in Embedded Sytems at the University of Derby, with a distinguished academic and professional background. He received his B.S. degree in Mechatronic Engineering from Danang University of Technology, Danang City, Vietnam, in 2011. He earned his M.S. and Ph.D. degrees from the School of Electrical Engineering at the University of Ulsan, South Korea, in 2022.
Dr. Le's professional experience includes serving as a Research Scientist at the Agency for Science, Technology and Research (A*STAR), Singapore, in 2022-2023. From 2023 to 2025, he conducted postdoctoral research in teleoperation and wearable haptics at the University of Strathclyde, United Kingdom. His expertise and passion for robotics inspire both his teaching and research, fostering innovation in intelligent systems.
Research Interests
Dr. Le's research primarily focuses on the following areas:
End-to-End Multi-task Reinforcement Learning: Developing algorithms to enable robots to efficiently perform multiple tasks by leveraging diverse datasets.
Imitation Learning via Inverse Reinforcement: Designing systems that allow robots to replicate human behavior by inferring objectives from observed actions.
Model-based Fault Detection for Robot Manipulators: Utilizing dynamic models of robot manipulators to estimate actuator faults.
Fault-tolerant Control for Robot Manipulators: Enabling robots to maintain acceptable performance during system faults until maintenance can be performed.
Force Control: Developing precise force control techniques for robot manipulators and haptic devices.
Teleoperation Control: Creating wearable haptic devices for industrial applications in teleoperation.
Human-Robot Interaction: Designing controllers to enhance safe and effective collaboration between robots and humans in shared workspaces.
His work has been recognized in academic communities, contributing to advancements in intelligent automation and robotic systems.
Le, Q.D.; Yang, E. Adaptive Fault-Tolerant Tracking Control for Multi-Joint Robot Manipulators via Neural Network-Based Synchronization. Sensors 2024, 24, 6837. https://doi.org/10.3390/s24216837.
Vo, A.T.; Truong, T.N.; Le, Q.D.; Kang, H.J. Fixed-Time Sliding Mode-Based Active Disturbance Rejection Tracking Control Method for Robot Manipulators. Machines 2023, 11(2), 140.
Le, Q.D.; Kang, H.-J. Implementation of Sensor-less Contact Force Estimation in Collaborative Robot Based on Adaptive Third Order Sliding Mode Observer. Systems Science and Control Engineering: An Open Access Journal 2022, DOI: 10.1080/21642583.2022.2063201.
Le, Q.D.; Kang, H.-J. Finite-Time Fault-Tolerant Control for a Robot Manipulator Based on Synchronous Terminal Sliding Mode Control. Appl. Sci. 2020, 10, 2998.
Le, Q.D.; Kang, H.-J. Implementation of Fault-Tolerant Control for a Robot Manipulator Based on Synchronous Sliding Mode Control. Appl. Sci. 2020, 10, 2534.
Doan, Q.V.; Le, T.D.; Le, Q.D.; Kang, H.-J. A neural network–based synchronized computed torque controller for three degree-of-freedom planar parallel manipulators with uncertainties compensation. International Journal of Advanced Robotic Systems 2018.
Le, Q.D.; Kang, H.J. An Adaptive Controller with An Orthogonal Neural Network and A Third Order Sliding Mode Observer for Robot Manipulators. International Journal of Mechanical Engineering and Robotics Research 2018, 7(2).
Conference Papers
Chen, C.; Yan, S.; Yuan, M.; Tay, C.; Choi, D.; Le, Q.D. A Minimal Collision Strategy of Synergy Between Pushing and Grasping for Large Clusters of Objects. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA, 2023, pp. 6817-6822. DOI: 10.1109/IROS55552.2023.10341452.
Le, Q.D.; Kang, H.J. Sensor-Less Contact Force Estimation in Physical Human-Robot Interaction. International Conference on Intelligent Computing, 2021, pp. 233-244. Springer, Cham.
Le, Q.D.; Kang, H.J. Real Implementation of an Active Fault Tolerant Control Based on Super Twisting Technique for a Robot Manipulator. International Conference on Intelligent Computing, 2019, pp. 294-305. Springer, Cham.
Le, Q.D.; Kang, H.J. Real Implementation of Fault-Tolerant Sliding Mode Control for a Robot Manipulator. 2018 3rd International Conference on Control, Robotics and Cybernetics (CRC), Penang, Malaysia, 2018, pp. 48-52. DOI: 10.1109/CRC.2018.00018.
Le, Q.D.; Kang, H.J.; Le, T.D. An Adaptive Position Synchronization Controller Using Orthogonal Neural Network for 3-DOF Planar Parallel Manipulators. Intelligent Computing Methodologies, ICIC 2017, Lecture Notes in Computer Science, vol. 10363. Springer, Cham.
Le, Q.D.; Kang, H.J.; Le, T.D. Adaptive Extended Computed Torque Control of 3 DOF Planar Parallel Manipulators Using Neural Network and Error Compensator. Intelligent Computing Methodologies, ICIC 2016, Lecture Notes in Computer Science, vol. 9773. Springer, Cham.
Project
Wearable Haptic
As the technical lead for this project, Dr. Le is responsible for designing and prototyping a wearable haptic device for underwater teleoperation, starting from scratch. This includes both hardware and software development, with low-level control based on an ARM microcontroller integrated with ROS (Robot Operating System). The haptic device enables the operator to control an underwater robot's gripper with force feedback, allowing them to feel objects being touched underwater. The system employs admittance control to achieve this functionality.
Lectures
Real-Time Embedded Systems
This module explores the design and implementation of real-time embedded systems on Texas Instrument microcontroller, critical for applications in robotics and automation. Students learn about embedded system architectures, real-time operating systems, and programming techniques to develop responsive and reliable systems for industrial and research purposes.
Share Code
3-Dof Parallel robot manipulator
Contact
For inquiries, collaborations, or academic engagements, please reach out to Dr. Quang Dan Le via the University of Derby's official channels.