Vortrag von Dr. Venkatraman Renganathan

14. Februar 2023

--- Title: Distributionally Robust Covariance Steering with Optimal Risk Allocation

Zeit: 14. Februar 2023
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Dr. Venkatraman Renganathan
Department of Automatic Control
Lund University 
Lund, Sweden


Tuesday 2023-02-14 4 p.m.
IST Seminar Room 2.255 - Pfaffenwaldring 9 - Campus Stuttgart-Vaihingen



This research is about the optimal covariance steering (CS) problem for discrete time linear stochastic systems modelled using moment-based ambiguity sets. To hedge against the uncertainty in the state distributions while performing covariance steering, distributionally robust risk constraints are employed during the optimal allocation of the risk. Specifically, a distributionally robust iterative risk allocation (DR-IRA) formalism is used to solve the optimal risk allocation problem for the CS problem using a two-stage approach. The upper-stage of DR-IRA is a convex problem that optimizes the risk, while the lower-stage optimizes the controller with the new distributionally robust risk constraints. The proposed framework results in solutions that are robust against arbitrary distributions in the considered ambiguity set. Finally, I will demonstrate the proposed approach using numerical simulations. 


Biographical Information

Currently, I am a postdoctoral research fellow working with Dr. Anders Rantzer at the department of automatic control in Lund University, Sweden. I was born in India. I finished my undergraduate studies in Electrical & Electronics Engineering from the Anna University, India. I completed my Masters in Electrical Engineering from Arizona State University, USA where I worked with Dr. Armando Rodriguez on my thesis on Missile Target Engagement for bank-to-turn missiles. Subsequently, I moved to The University of Texas at Dallas for my PhD under Dr. Tyler Summers where I worked on security of cyberphysical systems and risk bounded motion planning using the distributionally robust optimization techniques. My current research interests include learning based and adaptive control, risk bounded motion planning, anomaly detection in cyber physical systems.


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