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Concordia Institute of Aerospace Design and Innovation

Concordia Institute of Aerospace Design and Innovation

Research Internship Summer 2025: Drone Control Validation through Virtual Prototyping

Lab’s application deadline: Feb 7, 2025

Eligibility

This internship is open to students registered in a Bachelor's degree program. The plan is to fund this internship with the Undergraduate Student Research Award (USRA) or the Concordia Undergraduate Student Research Award (CUSRA).

The project will begin the week of April 28th, 2024. The duration is 16 consecutive weeks on a full-time basis (35 hours per week) at the High Reliability Aerospace Design Laboratory (HiRAD). The work period does not include provisions for holidays, except statutory holidays. We are looking for students not enrolled in a course during the duration of the internship.

Internship Description

The HiRAD lab specializes in advancing the design of safety-critical unmanned aerial vehicles (UAVs) by creating new methodologies and tools. Recent research has demonstrated the possibility of improving UAV reliability control through a physics-based approach [1], [2].

Figure 1 and 2 illustrate the virtual and physical prototypes employed as case studies in this research.

Figure 1. HARV - Virtual Prototype Figure 1. HARV - Virtual Prototype
Figure 2. Physical Prototype Figure 2. Physical Prototype

Figure 1. HARV - Virtual (left) and Physical (right) Prototypes

Internship objectives

  1. Update the existing virtual prototype in MATLAB-Simulink to reflect the new design developed by the Physical Prototyping team.
  2. Optimize performance and ensure stability by updating control laws.
  3. Integrate and evaluate newly provided control allocation methods with reliability optimization.
  4. Calibrate and verify the virtual prototype using ground and flight test data provided by the Physical Prototyping team.

Project steps

  1. Gain familiarity with prior research and the virtual prototype developed in MATLAB-Simulink using Simscape.
  2. Update the control laws to enhance system performance.
  3. Integrate and test new control allocation models for functionality and reliability.
  4. Calibrate and validate the virtual prototype by comparing simulation results with flight test data from the physical prototype.
  5. Document and report project progress and findings.

Qualifications & skills

  • Experience with Matlab-Simulink.
  • Strong understanding of aerospace and control engineering principles.
  • Interest in and enthusiasm for drone technology.
  • Strong writing and communication skills.
  • Demonstrated professionalism.

How to apply

Qualified and highly motivated candidates are invited to send their application per email to jonathan.liscouet@concordia.ca using the subject "Application: Virtual Prototyping and Flight Testing for Control Method Validation" with the following elements:

  1. Brief email with your motivation and relevant experience
  2. Clarify if you are a candidate for either USRA or CUSRA
  3. Up-to-date transcript
  4. Up-to-date CV

Undergraduate Student Research Award (USRA)

Values of awards: $6,000 (NSERC) + (Concordia supplement) + $1,500 (FRQNT supplement) + $2,500 (CIADI supplement, competition)

Prerequisites:

  • Be a Canadian Citizen or Permanent Resident of Canada as of February 1st 2024. 
  • Be registered in a Bachelor’s degree. 
  • Have a Cumulative GPA of at least 3.00 (“B” average).

Additional information: https://www.concordia.ca/students/financial/scholarships-funding/awards/nserc-usra.html

Concordia Undergraduate Student Research Award (CUSRA)

Values of awards: $8,270 (Concordia) + $2,500 (CIADI, competition)

Prerequisites

  • Canadian, Permanent resident or International Concordia undergraduate students; 
  • Completed at least 30 credits in the program and a minimum 3.3 CGPA is required at the time of application.

Additonal information: https://www.concordia.ca/research/funding/cusra.html

References

[1] J. Liscouët, J. Desrosiers, Z. Heit, I. Uwantare, A. Remoundos, and A. Senouci, “Physics-Based Reliability Modeling for Control Applications: Adaptative Control Allocation,” IEEE Access, vol. 12, pp. 161054–161074, Nov. 2024, doi: 10.1109/ACCESS.2024.3487916.

[2] J. Liscouët, Y. Grytsyk, A. Toma, H. Tan, and S. Ramesh, “Introducing Control Reallocation-Ability for UAV Reliability Optimization,” in AIAA SCITECH 2025 Forum, in AIAA SciTech Forum. Orlando, FL: American Institute of Aeronautics and Astronautics, Jan. 2025, p. AIAA 2025-1927. doi: https://arc.aiaa.org/doi/10.2514/6.2025-1927.

 
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