Efficient Data-Driven Optimization in Cooperative Control Systems with Constraints

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This is one of two research projects studying unmanned aerial and surface vehicles (UAVs and USVs). The University of Melbourne is the home institution for this project. To view the SJTU-based partner project, click here.

This project will fully exploit available sensor data to develop novel data-driven optimization-based techniques to achieve control and management goals optimally with the ability to handle constraints and uncertainties. With the consideration of practical implementation issues such as the computational cost, physical constraints from sensors and actuators, and limitation of communication ability, efficient online data-driven optimal collaborative control algorithms in the presence of constraints will be explored.

Project goals

The objectives of this project are:

  • Study the data-based rapid identification method of landing landmarks to realize the intelligent environment perception of UAV's autonomous landing.
  • A data-based optimization design framework is proposed to solve the problem of UAV-USV cooperative control.
  • Develop data-driven optimization technology to solve the control problem of UAV and USV under uncertain conditions.

Supervision team

The University of Melbourne: Professor Ying Tan, Dr Ye Pu

Shanghai Jiao Tong University: Professor Weidong Zhang

*Click on the researcher's name above to learn more about their publication and grant successes.

Who we are looking for

We are seeking a PhD candidate with the following skills:

  • Demonstrated research experience in the field of automation and engineering.
  • Demonstrated ability to work independently and as part of the team.
  • Demonstrated time and project management skills.
  • Demonstrated ability to write research reports or other publications to a publishable standard (even if not published to date).

Further details

To apply for this joint PhD opportunity, and to view the entry requirements, visit How to apply. Please read the application guidelines and eligibility information for both UoM and SJTU before contacting the lead supervisors.

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