Robotics have been extensively applied for many practical applications such as manufacturing, agriculture and ocean exploration. To increase the performance of robots for these applications, concepts of human-robot interaction/collaboration and multiple collaborative robots have been extensively studied.
However, robotics working in uncertain and unstructured environments will inevitably encounter unanticipated situations during their operation because of the effects of model uncertainties and environmental disturbances, which reduce the resilience and safety of the system. In addition, the constraints on control efforts, states and safety boundaries limit the flexibilities of the design of control inputs.
This presentation introduces new findings about fixed-time control techniques for robotics systems subject to physical constraints, for instance input saturation, working space constraints and obstacle avoidance to increase the response and convergence of the system.
Learning methods based on neural networks are also introduced to approximate the model uncertainties and disturbances and incorporated into the fixed-time controllers to increase the performance of the system.
Finally, applications of the learning-based fixed-time controllers for physical human-robot collaboration (pHRI) and multiple collaborative autonomous underwater vehicles (AUVs) are discussed.
About the Speaker
Dr Mien Van is a lecturer in Robotics and Intelligent Control at the School of Electronics, Electrical Engineering and Computer Science (EEECS) and the i-AMS Research Centre at Queen’s University Belfast (QUB).
from 2015 to 2019 he was a Senior Research Fellow with the University of Exeter and a Research Fellow with the University of Warwick and National University of Singapore. He received the PhD from the University of Ulsan, South Korea in 2015.
He has published over 60 peer reviewed papers with citations over 2000 and h-index of 26. His research focuses on developing advanced control methodologies and life cycle modelling-based resilient control techniques for enhancing the robustness, safety and resilience of robotics autonomous systems.