7 papers
SACK : Safe Active Continual Koopman Learning for Uncertain Systems with Contractive Guarantees
Chandan Kumar Sah, Rajpal Singh, Jishnu Keshavan
Koopman operator theory provides a powerful framework for representing nonlinear dynamics through a linear operator acting on lifted observables, enabling the use of linear control…
Generalized Momenta-Based Koopman Formalism for Robust Control of Euler-Lagrangian Systems
Rajpal Singh, Aditya Singh, Chidre Shravista Kashyap +1
This paper presents a novel Koopman operator formulation for Euler Lagrangian dynamics that employs an implicit generalized momentum-based state space representation, which decoupl…
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…
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…
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…