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Technische Universität München
Technische Universität München

Postdoc


Foto von Junjie Jiao

Junjie Jiao, Dr. Ph.D.

Technische Universität München

Lehrstuhl für Informationstechnische Regelung (Prof. Hirche)

Postadresse

Postal:
Barerstr. 21
80333 München

Short Biography

  • Feb. 2022 - present: Marie Skłodowska-Curie fellow (within the EuroTechPostdoc2 programme) at the Chair of Information-Oriented Control (ITR), Technical University of Munich (TUM), Munich, Germany

  • Nov. 2020 - Jan. 2022: Postdoctoral researcher at the Chair of Information-Oriented Control (ITR), Technical University of Munich (TUM), Munich, Germany

  • Oct. 2016 - Oct. 2020: PhD student at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands

Research Interests

  • Learning-based control
  • Data-driven control
  • Distributed control and estimation
  • Robust and optimal control
  • Actuator and sensor scheduling

Projects

This is a Marie Skłodowska-Curie fellowship (for 24-month) funded within the EuroTechPostdoc2 programme. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 899987.

EuroTech Postdoc2 Fellowship

  • Co-host Institution: École Polytechnique Fédérale de Lausanne (EPFL)
  • Co-host Supervisor: Prof. Colin N. Jones
  • Summary project: In the near future, teams of underwater robots will collaboratively collect waste from seafloor (currently performed by human divers) and restore healthy oceans. Similar multi-agent concepts can be extended to unmanned aerial vehicles or smart home devices, which lead to applications in environmental monitoring or internet of things. Multi-agent systems can achieve complex tasks that are difficult or impossible for single agents to solve. However, current control methods are very conservative as they require either known accurate agent models or enormous amounts of high-quality data. This is highly impractical, as (i) agents are becoming increasingly complex, which makes accurate models extremely difficult to obtain; and (ii) enormous amounts of data lead to expensive computation, which significantly slows down agent operation. To fully exploit the potential of multi-agent systems, control methods with efficient data use have to be developed. The objective of this project is therefore to develop optimal online distributed data-driven control algorithms for networked multi-agent systems with theoretical guarantees that use agent data much more efficiently than the existing solutions.

Student Theses

  • I am continuously looking for motivated students who are interested in my research. Please feel free to contact me via email, if you are interested in working on your Bachelor's or Master's thesis under my supervision. Please include your transcript of records, CV (if available) and your preferred starting date in your email.

Publications

2022

  • A. Lederer; Z. Yang; J. Jiao; S. Hirche: Cooperative Control of Uncertain Multi-Agent Systems via Distributed Gaussian Processes. IEEE Transactions on Automatic Control, 2022 mehr… BibTeX
  • Jiao, Junjie; Capone, Alexandre; Hirche, Sandra: Backstepping tracking control using Gaussian processes with event-triggered online learning. IEEE Control Systems Letters, 2022, 3176 - 3181 mehr… BibTeX
  • Jiao, Junjie; Maity, Dipankar; Baras, John S.; Hirche, Sandra: Actuator scheduling for linear systems: a convex relaxation approach. IEEE Control Systems Letters, 2022, 1-1 mehr… BibTeX
  • Jiao, Junjie; Trentelman, Harry L.; Camlibel, M. Kanat: H2 and H-infinity Suboptimal Distributed Filter Design for Linear Systems. IEEE Transactions on Automatic Control, 2022 mehr… BibTeX

2021

  • J. Jiao; H. L. Trentelman; M. K. Camlibel: H2 suboptimal output synchronization of heterogeneous multi-agent systems. Systems & Control Letters 149, 2021, 104872 mehr… BibTeX
  • J. Jiao; H.J. van Waarde; H.L. Trentelman M.K. Camlibel; S. Hirche: Data-driven output synchronization of heterogeneous leader-follower multi-agent systems. 60th IEEE Conference on Decision and Control, 2021 mehr… BibTeX
  • Z. Yang; S. Sosnowski; Q. Liu; J. Jiao; A. Lederer; S. Hirche: Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes. 60th Conference on Decision and Control (CDC), IEEE, 2021 mehr… BibTeX

2020

  • J. Jiao; H. L. Trentelman; M. K. Camlibel: A suboptimality approach to distributed H2 control by dynamic output feedback. Automatica 121, 2020, 109164 mehr… BibTeX
  • J. Jiao; H. L. Trentelman; M. K. Camlibel: Distributed Linear Quadratic Optimal Control: Compute Locally and Act Globally. IEEE Control Systems Letters 4 (1), 2020, 67-72 mehr… BibTeX
  • J. Jiao; H. L. Trentelman; M. K. Camlibel: A Suboptimality Approach to Distributed Linear Quadratic Optimal Control. IEEE Transactions on Automatic Control 65 (3), 2020, 1218-1225 mehr… BibTeX
  • J. Jiao; H.L. Trentelman; M.K. Camlibel: Distributed Linear Quadratic Tracking Control for Leader-Follower Multi-Agent Systems: A Suboptimality Approach. IFAC World Congress 2020, 2020 mehr… BibTeX

2018

  • J. Jiao; H. L. Trentelman; M. K. Camlibel: A Suboptimality Approach to Distributed H2 Optimal Control. IFAC-PapersOnLine 51 (23), 2018, 154-159 mehr… BibTeX
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