New UROP opportunities will be listed here for one month, and thereafter will appear on the relevant faculty page (UROP website) until notified otherwise by the relevant member of academic staff.


Title of UROP Opportunity (Research Experience) & DetailsExperienced required (if any)Contact Details and any further Information


NEW: April 27, 2021

Real-time predictive control of a minidrone: This project aims to develop a predictive control-based trajectory generator to navigate a small quadcopter around obstacles in an environment.  Predictive controllers solve optimization problems in real-time to compute optimal trajectories that ensure the given constraints are satisfied. Predictive control is the most widely implemented advanced control techniques in industry, with applications as diverse as robotics, vehicles, aircraft, spacecraft, chemical processes, medicine and finance.

The goals of this project are to:

1. Develop a new hardware lab for the Predictive Control module in the Department of Electrical and Electronic Engineering. The plan is that this module will also be made available to students from the Department of Aeronautics.

2. Explore and extend the limits of performance, robustness and computational requirements when implementing a predictive controller on a minidrone.

Skills and experience required: It is essential that the applicant should have be in their 3rd year and have done Control module on state space methods and have experience with Matlab and Simulink. Experience with designing and implementing your own controller on a drone with an embedded processor is highly desirable.

When: Summer vacation only

Bursary: the objective is to ensure that the successful candidate has access to a bursary.

Contact details: Dr Eric Kerrigan, Room 1108, Department of Electrical and Electronic Engineering, South Kensington Campus. Email:; Tel: 02075946343

Further information: The simulation and hardware platform will be based on the MathWorks Minidrone Competition ( The student will explore the capabilities of the Matlab Model Predictive Control toolbox, including the newly released integration with the state-of-the-art FORCES Pro solver ( used in industry. The project is funded by and in collaboration with MathWorks.

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