5 papers
Deep Robust Koopman Learning from Noisy Data
Aditya Singh, Rajpal Singh, Jishnu Keshavan
Koopman operator theory has emerged as a leading data-driven approach that relies on a judicious choice of observable functions to realize global linear representations of nonlinea…
Periodic Event-Triggered Prescribed Time Control of Euler-Lagrange Systems under State and Input Constraints
Chidre Shravista Kashyap, Karnan A, Pushpak Jagtap +1
This article proposes a periodic event-triggered adaptive barrier control policy for the trajectory tracking problem of perturbed Euler-Lagrangian systems with state, input, and te…
Dynamics-Invariant Quadrotor Control using Scale-Aware Deep Reinforcement Learning
Varad Vaidya, Jishnu Keshavan
Due to dynamic variations such as changing payload, aerodynamic disturbances, and varying platforms, a robust solution for quadrotor trajectory tracking remains challenging. To add…
Tracking Control of Euler-Lagrangian Systems with Prescribed State, Input, and Temporal Constraints
Chidre Shravista Kashyap, Pushpak Jagtap, Jishnu Keshavan
The synthesis of a smooth tracking control for Euler-Lagrangian (EL) systems under stringent state, input, and temporal (SIT) constraints is challenging. In contrast to existing me…
Event-Based Adaptive Koopman Framework for Optic Flow-Guided Landing on Moving Platforms
Bazeela Banday, Chandan Kumar Sah, Jishnu Keshavan
This paper presents an optic flow-guided approach for achieving soft landings by resource-constrained unmanned aerial vehicles (UAVs) on dynamic platforms. An offline data-driven l…