I am a PhD student at the Institute for Systems Theory and Automatic Control (IST), University of Stuttgart affiliated with the International Max Planck Research School for Intelligent Systems (IMPRS-IS). I am currently working in the area of Learning and Adaptive Control.

Stochastic Model Predictive Control (SMPC) strategies require the initial state of a system to be within a particular set, referred to as feasibility set, probabilistically, to ensure recursive feasibility. Such strategies are restrictive in nature when the uncertainty in the system is unbounded. In such a case, the feasibility set is very small and leads to a strategy that is very conservative. To reduce this conservatism, some constraint violations are permitted. However, such violations affect the closed-loop behaviour of the system leading to performance degradation. This performance degradation can be quantified as a penalty on the system for violating constraints, and intuitively, it can be thought of as a risk taken by the system in that undesirable state. An approach following the exact penalty method is proposed using the Conditional Value at Risk (CVaR) function to determine the penalty cost.Since accurate computation of the expected value of risk using the CV aR function is not possible, a scenario-based approximation of the CVaR is used to obtain an overall tractable and computationally efficient SMPC strategy.

Course: Networked and Distributed Control SystemsConducted office hours to provide assistance with course assignments.

Developed a soft lunar landing strategy using the GMPSP guidance algorithm and validated the strategy through PIL simulation studies.

Courses:-Digital Signal Processing: Conducted assisted lectures for assignment discussions and evaluated assignments. Assisted during lab sessions for signal processing experiments using MATLAB, CCStudio and TMS320C6742/8 development kits. Co-authored a lab manual for the Digital Signal Processing Lab.-Digital Communication: Conducted assisted lectures for assignment discussions and evaluated assignments.