Phi Tran

Research Associate

Dr Vu Phi Tran received the B.E. degree in Automation and Control Engineering from the HCMC University of Technology and Education, Saigon, Vietnam, in 2010, the M.S. degree in Mechatronics Engineering from Asian Institute of Technology, Bangkok, Thailand, in 2015, and a Ph.D. in the field of aerospace engineering from the University of New South Wales, Canberra, Australia, in 2019. From 2010 to 2012, he was a Lecturer in the Department of Automation and Control Engineering, HCMC University of Technology and Education, Saigon, Vietnam. Since 2020, he has been a Research Assistant, a Teaching Staff, and a Casual Professional staff at the University of New South Wales, Canberra, Australia.

Vu is a member of the Trusted Autonomy research group within the School of Engineering and Information Technology, UNSW Canberra. Vu publishes in high-impact IEEE Transactions journals (e.g. IEEE/ASME Trans on Mechatronics, Industrial Informatics, and Industrial Electronics) and Elsevier journals  (e.g. Practice Control Engineering, IFAC Journal of systems and control). 

Vu has been a reviewer for numerous high-impact control and robotics journals (e.g. IEEE Trans. on Vehicular Technology, IEEE Trans. on Robotics, and IEEE Trans. on Mans, Systems, and Cybernetics: Systems); in addition to many international conferences.

His current research interests include:

  •      Adaptive and robust control
  •      Non-linear control systems
  •      Consensus and formation control
  •      Neural networks
  •      Fuzzy systems
  •      UAV systems and robotics. 

Projects he is currently undertaking and looking to continue to include:

  • Machine Learning or Deep Learning to generate the desired formation as well as avoid the unexpected obstacles for a group of robots.
  • Auto flight control systems, robust and fast recovery against multiple disturbances.
  • Anti-disturbance controllers.

 

PUBLICATIONS
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Preprints
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2022, Robust Fuzzy Q-Learning-Based Strictly Negative Imaginary Tracking Controllers for the Uncertain Quadrotor Systems, , http://arxiv.org/abs/2203.13959v1
2022
2021, Frontier-led Swarming: Robust Multi-Robot Coverage of Unknown Environments, , http://arxiv.org/abs/2111.14295v2
2021
2020, Continuous Deep Hierarchical Reinforcement Learning for Ground-Air Swarm Shepherding, , http://dx.doi.org/10.48550/arxiv.2004.11543
2020
2018, Time-Varying Formation Control of a Collaborative Multi-Agent System Using Negative-Imaginary Systems Theory, , http://dx.doi.org/10.48550/arxiv.1811.06206
2018
2018, Distributed Obstacle and Multi-Robot Collision Avoidance in Uncertain Environments, , http://dx.doi.org/10.48550/arxiv.1811.06196
2018
2018, Apprenticeship Bootstrapping Via Deep Learning with a Safety Net for UAV-UGV Interaction, , http://dx.doi.org/10.48550/arxiv.1810.04344
2018
Book Chapters
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2021, 'Tuning Swarm Behaviour for Environmental Sensing Tasks Represented as Coverage Problems', in Artificial Intelligence and Data Science in Environmental Sensing, pp. 155 - 178, http://dx.doi.org/10.1016/B978-0-323-90508-4.00001-0
2021
2020, 'A Fuzzy Logic-Based Adaptive Strictly Negative-Imaginary Formation Controller for Multi-Quadrotor Systems', in Qian D (ed.), A Closer Look at Formation Control, Nova Science Publisher, New York, USA, https://novapublishers.com/shop/a-closer-look-at-formation-control/
2020
Conference Papers
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2022, 'A Multi-Agent Approach to Landing Speed Control with Angular Rate Stabilization for Multirotors', in 2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings, IEEE, presented at 2022 IEEE Vehicle Power and Propulsion Conference (VPPC), 01 November 2022 - 04 November 2022, http://dx.doi.org/10.1109/VPPC55846.2022.10003396
2022
2022, 'Robust adaptive learning control for different classes of dissipative vehicle systems', in 2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings, IEEE, presented at 2022 IEEE Vehicle Power and Propulsion Conference (VPPC), 01 November 2022 - 04 November 2022, http://dx.doi.org/10.1109/VPPC55846.2022.10003416
2022
Nguyen T; Nguyen H; Garratt M; Tran P; Kasmarik K; Barlow M; Anavatti S; Abbass H, 2018, 'Apprenticeship Bootstrapping via Deep Learning with a Safety Net for UAV-UGV Interaction', in AAAI Fall Symposia Papers, AAAI, Stanford USA, presented at Fifth AAAI Fall Symposium Series on Artificial Intelligence for Human-Robot Interaction, Stanford USA, - , https://aaai.org/Library/Symposia/symposia-library.php
2018
Tran VP; Garratt M; Petersen IR, 2018, 'Formation control of multi-UAVs using negative-imaginary systems theory', in 2017 Asian Control Conference, ASCC 2017, pp. 2031 - 2036, http://dx.doi.org/10.1109/ASCC.2017.8287487
2018
Conference Posters
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2022, 'Transfer Learning for Autonomous Recognition of Swarm Behaviour in UGVs', Sydney, presented at Australian Joint Conference on Artificial Intelligence, Sydney, 02 February 2022 - 04 February 2022, http://dx.doi.org/10.1007/978-3-030-97546-3_43
2022
Journal articles
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2023, 'Multi-gas source localization and mapping by flocking robots', Information Fusion, 91, pp. 665 - 680, http://dx.doi.org/10.1016/j.inffus.2022.11.001
2023
2023, 'Dynamic Frontier-Led Swarming: Multi-Robot Repeated Coverage in Dynamic Environments', IEEE/CAA Journal of Automatica Sinica, 10, pp. 646 - 661, http://dx.doi.org/10.1109/JAS.2023.123087
2023
2022, 'Frontier Led Swarming: Robust Multi-robot coverage of Unknown Environments', Swarm and Evolutionary Computation
2022
2022, 'Robust Fuzzy Q-Learning-Based Strictly Negative Imaginary Tracking Controllers for the Uncertain Quadrotor Systems', IEEE Transactions on Cybernetics, http://dx.doi.org/10.1109/TCYB.2022.3175366
2022
2021, 'Multi-vehicle formation control and obstacle avoidance using negative-imaginary systems theory', IFAC Journal of Systems and Control, 15, http://dx.doi.org/10.1016/j.ifacsc.2020.100117
2021
2021, 'Hybrid adaptive negative imaginary- neural-fuzzy control with model identification for a quadrotor', IFAC Journal of Systems and Control, 16, http://dx.doi.org/10.1016/j.ifacsc.2021.100156
2021
2021, 'Distributed Formation Control Using Fuzzy Self-Tuning of Strictly Negative Imaginary Consensus Controllers in Aerial Robotics', IEEE/ASME Transactions on Mechatronics, 26, pp. 2306 - 2315, http://dx.doi.org/10.1109/TMECH.2020.3036829
2021
2021, 'Adaptive Trajectory Tracking for Quadrotor Systems in Unknown Wind Environments Using Particle Swarm Optimization-Based Strictly Negative Imaginary Controllers', IEEE Transactions on Aerospace and Electronic Systems, 57, pp. 1742 - 1752, http://dx.doi.org/10.1109/TAES.2020.3048778
2021
2020, 'Fuzzy Self-Tuning of Strictly Negative-Imaginary Controllers for Trajectory Tracking of a Quadcopter Unmanned Aerial Vehicle', IEEE Transactions on Industrial Electronics, pp. 1 - 1, http://dx.doi.org/10.1109/tie.2020.2988219
2020
2020, 'Switching formation strategy with the directed dynamic topology for collision avoidance of a multi-robot system in uncertain environments', IET Control Theory and Applications, 14, pp. 2948 - 2959, http://dx.doi.org/10.1049/iet-cta.2020.0502
2020
2020, 'Distributed Artificial Neural Networks-Based Adaptive Strictly Negative Imaginary Formation Controller for Unmanned Aerial Vehicles in Time-Varying Environments', IEEE Transactions on Industrial Informatics, pp. 1 - 1, http://dx.doi.org/10.1109/tii.2020.3004600
2020
2020, 'Neural Network-Based Self-Learning of an Adaptive Strictly Negative Imaginary Tracking Controller for a Quadrotor Transporting a Cable-Suspended Payload with Minimum Swing', IEEE Transactions on Industrial Electronics, pp. 1 - 1, http://dx.doi.org/10.1109/TIE.2020.3026302
2020
2020, 'Switching time-invariant formation control of a collaborative multi-agent system using negative imaginary systems theory', Control Engineering Practice, 95, http://dx.doi.org/10.1016/j.conengprac.2019.104245
2020
2019, 'Adaptive Second Order Strictly Negative Imaginary Controllers Based on the Interval Type-2 Fuzzy Self-Tuning Systems For a Hovering Quadrotor with Uncertainties', IEEE/ASME Transactions on Mechatronics, http://dx.doi.org/10.1109/TMECH.2019.2941525
2019
2018, 'Time-Varying Formation Control of a Collaborative Multi-Agent System Using Negative-Imaginary Systems Theory', Control Engineering Practice, 95, pp. 2020, http://dx.doi.org/10.1016/j.conengprac.2019.104245
2018
2018, 'Distributed Obstacle and Multi-Robot Collision Avoidance in Uncertain Environments', , http://arxiv.org/abs/1811.06196v1
2018

AWARDS
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- Rockwell Automation Scholarship for “Keeping with Rockwell’s Philosophy of Maintaining the Highest Standards of Quality, Service and Engineering Excellence”.

- “THE HISAMATSU PRIZE” in recognition of the most outstanding academic performance of Mechatronics, AIT, Thailand.

- “UNSW Tuition Fee Scholarship” from the University of New South Wales, Canberra, Australia for my study toward a Doctorate at UNSW Canberra campus.

- "Dean’s Award for Outstanding PhD Theses" from the University of New South Wales, Canberra, Australia for the top 10% of Ph.D. theses examined.

- "T.F.C Lawrence Prize" for the best performance by a graduating research student specializing in Aeronautical Science.

GRANTS
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- DST project at the University of New South Wales, Canberra.

- AFOSR project at the University of New South Wales, Canberra.

Organisational units
lensSchool of Engineering & Information Technology
Sub Theme
lensSystems & Control
lensTrusted Autonomy