UROP Research Mentor Project Submission Portal: Submission #1259
Submission information
Submission Number: 1259
Submission ID: 20891
Submission UUID: 5238214c-b449-4fe4-bbc3-173495b456d2
Submission URI: /urop-research-mentor-project-submission-portal
Submission Update: /urop-research-mentor-project-submission-portal?token=Iyvvy6lXhXBB0ypO0pCvJc0IsODZkcGhBgTH68h3hgQ
Created: Mon, 08/18/2025 - 04:29 PM
Completed: Mon, 08/18/2025 - 04:48 PM
Changed: Mon, 08/25/2025 - 11:59 AM
Remote IP address: 68.63.41.233
Submitted by: Anonymous
Language: English
Is draft: No
Webform: UROP Project Proposal Portal
Submitted to: UROP Research Mentor Project Submission Portal
Research Mentor Information
Fengjiao Liu
She/her
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Faculty
FAMU-FSU College of Engineering
Electrical and Computer Engineering
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Additional Research Mentor(s)
Overall Project Details
Covariance Control for Stochastic Systems
control theory, uncertainty quantification
Yes
2
Computer Science, Electrical and Computer Engineering, Mathematics, Statistics, Data Science, Mechanical Engineering, Industrial & Manufacturing Engineering
The location is flexible, we can discuss on Zoom or on Engineering campus, or occasionally on FSU main campus
No, the project is remote
Fully Remote
6 hours per week
Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Covariance control is one way to quantify the uncertainty in dynamical systems and has applications in trajectory planning. This project aims to control the covariance matrix of the state vector in stochastic dynamical systems from a given initial state covariance to a desired target state covariance over a finite time horizon, while minimizing a quadratic cost function such as control energy. There are two separate objectives of this project:
1. For a linear stochastic system, design an optimal control law for covariance steering with partial constraint on the target state covariance.
2. For a hybrid stochastic system whose dynamics involve a combination of continuous evolution and discrete transitions, design an optimal control law for covariance steering.
1. For a linear stochastic system, design an optimal control law for covariance steering with partial constraint on the target state covariance.
2. For a hybrid stochastic system whose dynamics involve a combination of continuous evolution and discrete transitions, design an optimal control law for covariance steering.
literature review, problem formulation, mathematical analysis and proof, coding and simulation.
Required: a good mathematical background, coding and programming skills.
Recommended: knowledge on ordinary differential equations, linear algebra, real analysis, probability, stochastic process, and control theory.
Recommended: knowledge on ordinary differential equations, linear algebra, real analysis, probability, stochastic process, and control theory.
The students should have a good math background and are expected to work independently and plan their timelines. The advisor and students can have a short Zoom meeting biweekly to discuss progress and challenges.
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UROP Program Elements
Yes
Yes
Yes
Yes
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2025
https://cre.fsu.edu/urop-research-mentor-project-submission-portal?token=Iyvvy6lXhXBB0ypO0pCvJc0IsODZkcGhBgTH68h3hgQ